Verbesserung der Aufmerksamkeitsleistungen von
Kindern in klinischen Kontexten
Der Philosophischen Fakultät
der Friedrich-Alexander-Universität
Erlangen-Nürnberg
zur
Erlangen des Doktorgrades Dr. phil.
vorgelegt von
Jessica Ashley Van Doren
Als Dissertation genehmigt von der Philosophischen Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg
Tag der mündlichen Prüfung: 20.01.2020
Vorsitzender des Promotionsorgans: Prof. Dr. Thomas Demmelhuber
Gutachter: Prof. Dr. Matthias Berking
Prof. Dr. Nicolas Rohleder
PD Dr. Phil Dr. med. habil. Anna Eichler
Table of Contents 0. Abstract ...... 1 0.1 Zusammenfassung ...... 2 1. Theoretical background and state of the art ...... 8 1.1 Attention ...... 8 1.1.1 Definitions and Theories ...... 8 1.1.2 Psychology ...... 11 1.1.3 Neuroscience ...... 14 1.1.4 Development ...... 19 1.2 Psychopathology ...... 22 1.3 Enhancing Attention ...... 27 1.3.1 Neurofeedback ...... 29 1.3.2 Light ...... 36 2. Optimizing therapeutic techniques for improving attentional abilities as the goal of this dissertation ...... 43 3. Peer reviewed papers ...... 46 3.1 Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects ...... 47 3.2 Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis ...... 47 3.3 Effects of blue- and red-enriched light on attention and sleep in typically developing adolescents ...... 47 4.1 Overview of the results ...... 48 4.2 Theoretical and Practical Implications ...... 52 4.3 Limitations and future research ...... 62 4.4 Conclusion ...... 65 5. References ...... 66 6. Acknowledgements ...... 84
0. Abstract
This dissertation is concerned with the assessment of two current available methods of enhancing attentional abilities in children and adolescents: neurofeedback and ambient light exposition. Study 1 investigated the short term effects of neurofeedback in children with attention deficit hyperactivity disorder (ADHD) in a two session format. It was found that attentional resources, reflected by suppression of the theta wave, were able to be influenced after only two neurofeedback sessions. This finding was also found to be variant among the participants, leading to distinctions in this early phase of training as ‘good’ or ‘poor’ regulators, reflective of potentially different endophenotypes of ADHD. Study 2 was a meta- analysis assessing the long-term symptomatic effects of neurofeedback training in children and adolescents with ADHD, in which assessments were conducted pre-, post- and follow-up
(two months to two years) to training. Here it was found that neurofeedback training was superior to non-active controls for both inattention and hyperactivity/impulsivity at post- and follow-up training. Neurofeedback was found to be inferior to active controls (medication, self management training) at post training, but then comparable at follow-up time points.
These results support the use of neurofeedback for the long term therapeutic improvement of
ADHD symptoms. Study 3 investigated the effect of red vs. blue light exposition in healthy adolescents in a laboratory setting. Blue light exposition was found to improve attention (as measured by reduced reaction time variability and improved arithmetic performance) in this demographic, while red evening light exposition was found to slightly improve sleep quality, which can in turn affect attentional abilities. Together these three studies provide evidence that attentional abilities can be influenced in children and adolescents in clinical settings.
Additionally, they support that different endophenotypes of ADHD should be considered when choosing a neurofeedback protocol and that light exposition could be additionally used as either an additive treatment or an independent therapy.
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0.1 Zusammenfassung
Das Konstrukt ‚Aufmerksamkeit‘ ist vielschichtig und wird seit langem erforscht. Aus neurologischer Sicht sind etliche Marker bekannt, die Aufmerksamkeitsprozesse abbilden, wie zum Beispiel die kortikale Aktivierung oder die Synchronität neuronaler Signale sowohl auf zellulärer als auch höherer Ebene. Die Synchronität kann über elektroenzephalographische
(EEG-) Bänder gemessen werden. Man geht davon aus, dass alle Bandbreiten der hirnelektrischen Aktivierung mit Aufmerksamkeitsprozessen in Zusammenhang stehen, wobei das Theta-Band (4–8 Hz) eines der am häufigsten untersuchten ist.
Das Ausformen/ Die Ausprägung neuronaler Aufmerksamkeitsprozesse ist zentraler
Bestandteil einer gesunden Entwicklung im Kindes- und Jugendalter. Bei Untersuchungen zu den Aufmerksamkeitsleistungen von Kindern und Jugendlichen werden häufig drei Bereiche unterschieden: selektive Aufmerksamkeit, Daueraufmerksamkeit und
Aufmerksamkeitskontrolle (exekutive Kontrolle). Diese verschiedenen Arten von
Aufmerksamkeit beginnen sich im ersten Lebensjahr zu entwickeln und verbessern sich fortschreitend im Laufe von Kindheit, Jugend und teilweise auch noch im Erwachsenenalter.
Besonders die Altersbereiche 8-10 und 12-14 Jahre sind sensible Phasen der
Aufmerksamkeitsentwicklung. Kommt es zu diesen Entwicklungszeitpunkten zu
Beeinträchtigungen, kann dies zu klinisch auffälligen Entwicklungsverläufen führen. Eines der häufigsten Störungsbilder bei Kindern, das durch veränderte Aufmerksamkeitsprozesse gekennzeichnet ist, ist die Aufmerksamkeitsdefizit-Hyperaktivitätsstörung (ADHS).
Die Weltweite Prävalenz von ADHS liegt bei 7,9%. Kinder oder Jugendliche mit ADHS zeigen Symptome von Unaufmerksamkeit, Hyperaktivität und Impulsivität in verschiedenen
Lebensbereichen und sind dadurch beeinträchtigt. Diagnostisch werden je nach Stärke und
Vorhandensein der einzelnen Symptome drei Subtypen unterschieden: Der kombinierte Typ
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(sowohl Unaufmerksamkeits- als auch Hyperaktivitätskriterien aufweisend), der unaufmerksame Typ oder der hyperaktiv-impulsive Typ. Aktuelle Forschungsbefunde deuten jedoch darauf hin, dass diese Unterteilung möglicherweise nicht differenziert genug ist und dass eine Klassifikation in Anlehnung an verschiedene Endo- und Biotypen sinnvoller sein könnte.
Unabhängig von der Art der Klassifikation wird angenommen, dass eine veränderte
Entwicklung von Aufmerksamkeitsprozessen bei ADHS bereits im Säuglings- und
Kindesalter einsetzt und die Symptome, insbesondere die Unaufmerksamkeit, häufig bis ins
Erwachsenenalter anhalten. Orientiert man sich an oben genannter Einteilung von
Aufmerksamkeitsleistungen, konnte gezeigt werden, dass Kinder mit ADHS vor allem hinsichtlich exekutiver Funktionen und Daueraufmerksamkeit beeinträchtigt sind, wohingegen selektive Aufmerksamkeitsprozesse meist nicht betroffen sind. Diese spezifischen
Beeinträchtigungen spiegeln sich auch in bestimmten neurologischen Veränderungen wider, wie einer erhöhten Theta-Aktivität und volumenreduzierten kortikalen Arealen. Die
Symptome von Unaufmerksamkeit führen bei Menschen mit ADHS häufig zu Leidensdruck und einer geringeren Lebensqualität, was therapeutische Ansätze zur Verbesserung der
Aufmerksamkeitsleistung erforderlich macht. Vor diesem Hintergrund befasst sich die vorliegende Dissertation mit der Anwendung von zwei derzeit verfügbaren Methoden zur
Verbesserung der Aufmerksamkeitsleistungen von Kindern und Jugendlichen: Neurofeedback und Licht-Exposition.
Neurofeedback ist ein kognitives Training, das auf Grundlage lerntheoretischer Annahmen mithilfe eines Brain-Computer-Interface den Teilnehmer anleitet, seine neuronale Aktivität zu steuern (Neuroregulation). Das Therapieverfahren ist in der Lerntheorie verwurzelt und wird seit über 30 Jahren zur Behandlung von Kindern mit ADHS eingesetzt. Die neuronale
Aktivität, auf die diese Methode abzielt, ist facettenreich, wobei ein wirksames Paradigma die
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Erhöhung der Beta- und die Unterdrückung der Theta-Aktivität - widergespiegelt im Theta-
Beta-Ratio - ist. Es liegen viele Studien vor, die die unmittelbare Wirksamkeit von
Neurofeedback (nach dem Training) sowohl bei Unaufmerksamkeits- als auch bei
Hyperaktivitätssymptomen belegen. Allerdings wird in einigen Veröffentlichungen auch die
Frage gestellt, ob diese Wirksamkeit möglicherweise auf Placeboeffekte zurückzuführen oder mit der von anderen, kostengünstigeren Methoden zur Behandlung von ADHS vergleichbar ist. Darüber hinaus gab es bisher nur wenige Studien, in denen die Langzeitwirksamkeit von
Neurofeedback untersucht wurde.
Eine weitere Methode zur Steigerung der Aufmerksamkeitsleistung ist die Licht-Exposition.
Bisherige Studien beurteilten die Wirksamkeit von Licht zur Beeinflussung der
Aufmerksamkeit sowohl tagsüber als auch nachts, in der Schule wie am Arbeitsplatz. Es wurde festgestellt, dass helles blaues Licht eine aktivierende Wirkung hat (hauptsächlich über das nicht-bildliche visuelle System), wobei jedoch je nach Zeitpunkt der Exposition die
Aufmerksamkeitssteigerung auch auf Kosten veränderter Schlafmuster gehen kann. Die bisher veröffentlichten Forschungsarbeiten zum Thema Licht und Aufmerksamkeit wurden im
Kindesalter an Schülerkohorten im Klassenzimmer durchgeführt. In klinischen Kontexten lagen bislang überwiegend erwachsene Stichproben zugrunde. Die aktivierende und aufmerksamkeitssteigernde Wirkung der Licht-Exposition könnte eine vielversprechende therapeutische Methode für Kinder mit ADHS sein. Um dies jedoch beurteilen zu können, müssen zunächst Befunde für gesunde Kinder und Jugendliche vorliegen.
Ergebnisse
Theta/Beta Neurofeedback bei Kindern mit ADHS: Durchführbarkeit eines Kurzzeit-Trainings und Plastizitätseffekte
Die Kurzzeiteffekte von Neurofeedback bei Kindern mit ADHS wurden im Rahmen von zwei
Sitzungen untersucht. Es zeigte sich, dass Aufmerksamkeitsressourcen, die sich in der 4
Unterdrückung der Theta-Wellen widerspiegeln, bereits nach zwei Neurofeedback-Sitzungen beeinflusst werden konnten. Diese Verbesserung fiel bei verschiedenen Teilnehmern unterschiedlich aus, sodass bereits in dieser frühen Phase eines Theta/Beta Trainings zwischen „guten“ und „schlechten“ Regulierern unterschieden werden konnte, was unterschiedliche Endophänotypen von ADHS widerspiegeln könnte. Es zeigte sich ein
Aktivierungsmuster, bei dem der Theta-Effekt zu Beginn des Trainingsdurchlaufs am deutlichsten war. Dieser Effekt war für gute Regulierer größer und bildete den Abfall der
Aufmerksamkeitsleistung im Trainingsverlauf ab. Interessanterweise wurden keine Baseline-
Unterschiede zwischen den Gruppen im Grundzustand festgestellt.
Anhaltende Effekte von Neurofeedback bei ADHS: Systematisches Review und Metaanalyse
In dieser Metaanalyse wurden die symptombezogenen Langzeiteffekte von Neurofeedback
Training bei Kindern und Jugendlichen mit ADHS untersucht. Die Messungen fanden in den
Studien jeweils zu drei Zeitpunkten des Trainings statt: Prä-Messung, Post-Messung und
Follow-Up Messung (zwei Monate bis zwei Jahre nach Training). Es stellte sich heraus, dass das Neurofeedback-Training den nicht-aktiven Kontrollbedingungen sowohl in Bezug auf
Unaufmerksamkeit als auch auf Hyperaktivität / Impulsivität bei der Post- und Follow-Up
Messung überlegen war. Im Vergleich zu den aktiven Kontrollbedingungen (Medikation, kognitives Verhaltenstraining) fielen die Effekte des Neurofeedbacks bei der Post-Messung geringer aus, waren aber zum Zeitpunkt der Follow-Up Messungen vergleichbar hoch. Die
Ergebnisse sprechen folglich für den Einsatz der Neurofeedback-Methode zur langfristigen
Verbesserung von ADHS-Symptomen.
Die Effekte von blau und rot angereichertem Licht auf Aufmerksamkeit und Schlaf bei gesunden Jugendlichen
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Bei dieser Studie wurden die Auswirkungen einer Exposition gegenüber blauem und roten
Licht unter Laborbedingungen auf die Aufmerksamkeit und den Schlaf von gesunden
Jugendlichen untersucht. Blaues Licht steigerte im Vergleich zu rotem Licht die
Aufmerksamkeitsleistungen der Probanden, operationalisiert durch Leistungen in einem
Mathematik-Test und durch reduzierte Reaktionszeitvariabilitäten in einem
Aufmerksamkeitstest. Es wird angenommen, dass die Variabilität der Reaktionszeit insbesondere die Daueraufmerksamkeit widerspiegelt. Die Exposition gegenüber rotem Licht am Abend konnte die Schlafqualität der Jugendlichen leicht verbessern, was wiederum die
Aufmerksamkeitsleistungen positiv beeinträchtigen kann.
Diskussion
Die Ergebnisse dieser drei Arbeiten ergänzen den aktuellen Stand der Forschung um die
Erkenntnis, dass die Aufmerksamkeitsleistungen von Kindern und Jugendlichen im klinischen
Kontext durch äußere Faktoren – hier durch Neurofeedback und Licht-Exposition - beeinflussbar sind.
Die Arbeit zum Kurzzeit-Neurofeedback-Training konnte zum ersten Mal spezifische neuronale Mechanismen identifizieren, die zu Beginn eines Neurofeedback-Trainings bei
Kindern mit ADHS aktiv sind. Es zeigte sich zudem, dass unterschiedliche Endophänotypen bei der Bearbeitung kognitiver Aufgaben ein wichtiger Faktor sein könnten, der bei der
Klassifizierung von ADHS-Subtypen zu berücksichtigen ist. Wenn ADHS-Symptome gezielter beurteilt werden könnten, kann ein Neurofeedback-Training möglicherweise auf das spezifische Defizit abgestimmt werden, was zu einer erhöhten therapeutischen Wirksamkeit führen würde.
Die Metaanalyse zeigt, dass diese frühen, nach nur wenigen Sitzungen erzielten Effekte von
Neurofeedback auch langfristig anhalten können. Die Ergebnisse sind besonders relevant für die Therapie von Patienten, für die eine medikamentöse Behandlung nicht indiziert ist. Hier 6 kann ein Neurofeedback-Training eine wirkungsvolle Alternative darstellen. Es ist wichtig darauf hinzuweisen, dass die in die Metaanalyse eingeschlossenen Studien vornehmlich standardisierte Neurofeedback-Protokolle nutzten und dass andere, nicht-standardisierte
Neurofeedback-Trainingsmethoden mit Vorsicht einzusetzen sind, da Untersuchungen zu deren Wirksamkeit fehlen.
Schließlich stützt die Erkenntnis, dass blaues Licht die Aufmerksamkeitsleistung gesunder
Jugendlicher positiv beeinflussen kann die Annahme, dass die Umgebungsbeleuchtung ein wichtiger Faktor bei der Gestaltung therapeutischer Einrichtungen ist und die Licht-
Exposition zudem als wirksame Behandlungsmethode bei Symptomen von
Unaufmerksamkeit angesehen werden kann. Der gezielte Einsatz von Licht in der ADHS-
Therapie könnte sowohl den Aufmerksamkeitsfokus während einer Therapiesitzung verbessern als auch möglicherweise den Schlaf günstig beeinflussen, was sich wiederum positiv auf die Aufmerksamkeitsleistungen auswirkt.
Zusammengenommen liefern diese drei Studien den Nachweis, dass die
Aufmerksamkeitsleistungen von Kindern und Jugendlichen im klinischen Umfeld durch therapeutische Interventionen, die auf dem Einsatz von Neurofeedback und Licht-Exposition basieren, positiv beeinflusst werden können. Darüber hinaus deuten die Studien darauf hin, dass bei der Auswahl eines Neurofeedback-Protokolls verschiedene Endophänotypen von
ADHS berücksichtigt werden sollten und dass sich die Licht-Exposition als zusätzliche oder eigenständige Therapie bei ADHS eignet.
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1. Theoretical background and state of the art
1.1 Attention
1.1.1 Definitions and Theories Attention is a complex and long studied topic. At the turn of the 19th century it was examined primarily through introspection, in the 50’s the experiments of attention began to be operationalized, and currently attention is still being studied regarding its neural mechanisms and networks. Despite being studied for over a century, a full understanding of attention is still lacking. The definitions that currently exist differ based on the level of specificity. A general definition of attention provided by the Merriam-Webster Dictionary (2019) is: ‘the act or state of applying the mind to something’. In Mosby’s Medical Dictionary (2009), the definition of attention in psychology is the ‘direction of the consciousness to a person, thing, perception or thought’. Neurologically, attention can be defined as ‘the selective prioritization of the neural representations that are most relevant to one’s behavioral goals’ (Buschman &
Kastner, 2015). These different definitions then provide a glimpse into the many different ways attention can be examined. While much of the research has been devoted to understanding how attention works, there has also been an emphasis on how attention can be enhanced.
The original pioneers of attention research were interested in the introspective attentional processes, however this aspect is incredibly difficult to study due to the issues in quantifying self-report and the unconscious nature of many aspects of attention (Ivonin et al., 2015). In the last century of attention research, operationalized methods to quantify aspects of attention have been used; but the development of a unified theory of attention has not yet been successful. This is partially because attention can be examined from so many different aspects: the sensory system being used (visual, auditory, tactile); the target (spatial, object, feature, emotional, intrinsic, salient vs non-salient); consciousness/resources (overt vs covert, 8 voluntary vs automatic, executive, nonvolational (enforced vs spontaneous), volational
(implicit vs explicit); or via neurological processing (top-down vs bottom-up).
These varied methods of studying attention in healthy participants led to the development of many different theories of attention. These theories are very diverse (for overview see Figure
1), but many are concerned with identifying what elements of stimuli in our environment are identified at which level of processing. Some models of attention include: Filter Models, which suggest that there are multiple streams of sensory information that hit a bottleneck
(Broadbent, 1958; Deutsch & Deutsch, 1963; Treisman, 1960); Feature Integration Theory which suggests early parallel processing for features of an object but late separate processing for the object itself (Treisman & Gelade, 1980); Guided Search Model which suggests that there are parallel processes that can direct attention toward prioritized items (Wolfe, Cave, &
Franzel, 1989); Limited Resources Models, which suggest there are finite perceptual resources available (Kahneman, 1973; Wickens, 1980). These theories are very diverse, but can be briefly summarized as many different ways of examining a few elements of attention that seem to be evident: 1. Attention is selective, not all stimuli can be equally processed; 2.
Attention is limited, there are not an infinite number of resources available to process stimuli;
3. Attention has many levels of precognitive and cognitive processing, from input of stimuli through perception and memory storage.
In contrast there are also theories that focus on the subsystems of attention. One such theory is that of Petersen and Posner (2012) which postulates that there are three neurologically distinct but related networks for attention: alerting, orienting and the executive network. Alerting refers to the maintenance of vigilance in order to complete tasks. Orienting is the network that serves to prioritize inputs. The executive network is also known as focal attention and involves the conscious awareness of stimuli. This theory also suggests that attention can be
9 directed analogues to a spotlight which can engage and disengage on specific areas and then be moved.
Attention can also be viewed through clinical models of deficits seen for different disorders.
One model, which was developed by studying patients with brain injury, defines five areas of attention: focused attention, sustained attention, selective attention, alternating attention, and divided attention (Sohlberg & Mateer, 1987; Sohlberg & Mateer, 1989). By examining clinical populations, the question of how to improve attention became more important. As such, this model was then extended to therapeutic approaches for adults with attention deficit hyperactivity disorder (ADHD) (Sohlberg & Mateer, 2001). In this model they suggest that there are five approaches that can help to manage attention problems: environmental modifications, attention training, self-regulatory strategies, external aids, and psychosocial support.
Due to the wide range of approaches to attention, an in depth literature review of all attention research is outside of the scope of this thesis. Due to this, the focus will be on visual attention, both the findings from classic research as well as new findings regarding visual attention networks. These findings will be reviewed from a psychological, neurological and developmental standpoint as a basis for understanding how the attentional system can be influenced in clinical settings.
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Figure 1: A graphic representation of some theories of attention.
1.1.2 Psychology
When assessing visual attention within the realm of cognitive psychology, there are again many ways in which to begin. Among others aspects, assessments can be made regarding where vision is being directed, what is the purpose of that direction, which resources are being
11 used, or the mechanisms in which those resources allocate attention (see Table 1 for an overview).
First, where visual attention is being directed can be assessed. Under this framework there are three main types of visual attention: spatial attention, feature based attention, and object based attention (Carrasco, 2011). Spatial attention is concerned with where a stimulus is in space.
One of the many paradigms to test this is the Posner Paradigm in which participants are given a valid, invalid or neutral cue as to where a stimulus will occur (Posner, 1980). Assessment is then based on either manual reaction times or eye movement. Feature based attention is the attention given to features of an object (color, speed, luminosity etc.) and can be assessed through monitoring of eye movements as well as the firing of feature specific neurons with in the visual cortex while a participant looks at a target (Mazer, 2011). Object based attention assesses the object as a whole, which may help to define theories of attention that suggest there are a discreet number of items that can be processed, this is often tested with participants attending to overlapping stimuli and being asked to attend to just one of the two (Scholl,
2001).
Next we can assess the purpose of visual attention. In a review from Evans et al. (2011) it is suggested that there are four primary tasks of visual attention: 1. Data reduction/stimulus selection, 2. Stimulus enhancement, 3. Binding (the integration of separate components into an object representation), and 4. Recognition of objects. Data reduction/stimulus selection serves to identify relevant stimuli while suppressing irrelevant stimuli. Stimulus enhancement combines the three different looking patterns described above (spatial, feature based and object based) in order to focus on the relevant target. Binding involves the integration of separate components into an object representation. While some objects may be processed in their whole representation, more complicated stimuli must have their representation created by an integration of their features (aka “Binding Problem”, (Treisman
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& Gelade, 1980)). Finally, these three attentional processes culminate in object recognition, which involves the conscious perception of the stimulus (not simply awareness of it).
In the pursuit of fulfilling the four purposes of visual attention, one can revisit the five clinical divisions of attention (focused, sustained, selective, alternating, and divided) to shed light on the mental resources that are being used during attentive behavior. According to Sohlberg and
Mateer (2001): focused attention involves the response to a discrete stimuli tested via orienting and tracking parameters; sustained attention reflects vigilance and working memory, tested using continuous performance tasks (CPT), digit span, etc.; selective attention reflects the ability to disregard distractors, tested using the test of everyday attention; alternating attention reflects metal flexibly tested with digit symbol and letter number tests; divided attention is the ability to conduct tasks simultaneously, tested via paced auditory serial addition tasks. These different areas and tasks can be used to test attention competencies in both normal and clinical populations.
In relation to these ideas from Evans et al. (2011), there is the question of what mechanisms of attention are in action. To answer this question, the Perceptual Template Model was developed (Lu, 2008; Lu & Dosher, 1999). It suggests that there are three potential perceptual mechanisms of attention: 1. Stimulus enhancement, signal of interest is amplified (thought to function only in settings with little or no noise); 2. External noise exclusion, distracting signals are dampened (for highly noisy environments); 3. Internal multiplicative noise reduction, noise is reduced in relation to the non-noise input of the target (thought to operate in high and low noise environments). However research has shown that the mechanisms for covert attention are primarily stimulus enhancement and external noise exclusion (Lu, 2008).
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Table 1. Different aspects for which attention can be assessed.
1.1.3 Neuroscience
Adding to cognitive psychology, cognitive neuroscience tries to understand what is happening biologically when attentional processes are activated. Multiple signals of attention allocation have been found, such as increased firing on the cellular level, to increased synchrony at a population level. The cortical areas that are activated during attentional tasks have also been assessed.
On a cellular level, increased firing of attending neurons indicates attention. In studies of monkeys it has been shown that a single cell fires preferentially for the target item, in comparison to distractors and that cells attuned to non-relevant stimuli have decreased firing rates. The firing rate can also be further modulated via attention, with increased sensitivity of neurons responding to a stimulus (Reynolds, Pasternak, & Desimone, 2000) and changes in evoked responses (McAdams & Reid, 2005) as well as decreased sensitivity of neurons attending to other areas (Han, Xian, & Moore, 2009). In humans, this is also the case with 14 increased firing in the superior collicular nucleus corresponding to both covert and overt attentional shifts (Krauzlis, Lovejoy, & Zenon, 2013). Suppression of firing rates in the lateral geniculate nucleus, V1, and pulvinar nucleus for irrelevant stimuli have also been demonstrated in humans (Gouws et al., 2014). This simultaneous excitation and inhibition then balances itself in order to maintain the equilibrium of the system (normalization model of attention) (Reynolds & Heeger, 2009).
While single cells do respond to stimuli, they rarely respond alone. It has been shown that populations of neurons respond similarly to individual neurons with increased firing rates to attended stimuli (Martinez-Trujillo & Treue, 2004), and that their recruitment reflects attention allocation (Cohen & Maunsell, 2010). Interestingly, different types of attention have been shown to have specific patterns of how neuronal populations are recruited. In a study by
Cohen and Maunsell (2011) they found that spatial attention recruits local populations, while feature attention recruited populations across hemispheres.
These population dynamics lead to synchrony of the elicited signals. This synchrony then modulates the directed attentional resources and is reflected by changes in the electroencephalogram (EEG) bands used to measure activity: delta (1-4 hz), theta (4-8 hz), alpha (8- 12 hz), beta (13-30 hz), and gamma (30 to 150 hz). Low frequency oscillations are thought to arise from local populations of neurons through thalamo-regulation (via pulvinar nucleus of the thalamus) while high frequency oscillations are then created in the cortical areas and reflect an integration of multiple neuronal areas (Buschman & Kastner, 2015).
Changes within the different bands reflect different aspects of attentional processing (See
Figure 2).
Low frequency modulations within the delta band have been found to reflect selective attention as well as to act to modulate activity in the gamma band (Lakatos, Karmos, Mehta,
Ulbert, & Schroeder, 2008). The theta band is seen to be activated during difficult visual 15 attention tasks (Chen, Wang, Wang, Tang, & Zhang, 2017) and it is thought that its modulations influence perceptual outcomes during sustained attention tasks (Helfrich et al.,
2018). Theta is also elicited during directed attention in visual search (Busch & VanRullen,
2010) and search patterns oscillate in a theta rhythm (Fries, 2015). Changes in the alpha band have been found to reflect attentional binding (Nakayama & Motoyoshi, 2019) and top-down attentional control (Connell et al., 2009). Beta band changes reflect achieving an attentional state (Wrobel, 2000) and have been implicated in the feedback of neural information (Fries,
2015) . The gamma activity changes are thought to primarily reflect top-down processing and may be driven by an increased inhibitory drive from these regions (a tuned balance of excitation induced by stimulus and inhibition balancing the networks) (Chalk et al., 2010), but have also been found to be important for bottom-up processing (Riddle, Hwang, Cellier,
Dhanani, & D'Esposito, 2019). The pattern of activation of these neural rhythms provide a model for the coordination of different cortical areas for the recruitment of resources as well as directs how often a specific stimulus is then sampled (Buschman & Kastner, 2015;
Buschman & Miller, 2007).
Figure 2. Attentional correlates of EEG Waves.
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Anatomically, when attending to a visual stimulus, information enters the retina and is passed through to the cortical visual areas (V1V2, V4, middle temporal gyrus, medial superior temporal area, inferior temporal cortex). From there it can be processed in subcortical regions
(laterate geniculate nucleus, pulvinar, reticular nucleus of the thalamus, superior colliculus) and then processed by the associative regions in the parietal and frontal lobes (Buschman &
Miller, 2007). However, while this is how visual information enters the cortex, the attention to the stimulus is in a loop with the prefrontal cortex, which can then modify the response of the visual cortex based on attention (Paneri & Gregoriou, 2017). These loops are considered to be attentional networks (See Figure 3).
Figure 3. Processing of visual stimuli. Stimuli enter the system through the retina, proceed to the visual processing areas, then the cortical association areas in the frontal and parietal lobes. In turn cortical association areas can influence the subcortical and visual processing, creating a loop.
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The integration of these subcortical and cortical areas into attentional networks can be defined as top-down vs bottom-up systems (See Figure 5). Bottom-up processing involves the automatic processes that direct attention to salient stimuli (ex. Size, color, movement); top- down processing is responsible for conscious directing/influence of attention, and with-in the visual system it allows for the selection of relevant stimuli to align with the current goal of the organism (Desimone and Duncan 1995). Each type of processing recruits its own neural network. Top-down processing flows from the frontal to the parietal lobe, while bottom-up processing flows from the parietal to frontal lobe (Buschman & Miller, 2007; Richter,
Thompson, Bosman, & Fries, 2017). More specifically, top-down attention requires the prefrontal, frontal (middle frontal gyri, frontal eye fields) and parietal (intra parietal sulcus, inferior and superior parietal lobule) lobes (Hahn, Ross, & Stein, 2006; Ronconi, Basso, Gori,
& Facoetti, 2014; Smith, Jackson, & Rorden, 2009). These processing areas are also involved in the processing of other executive functions, such as working memory (Feng, Pratt, &
Spence, 2012). Bottom-up attentional processing involves the bilateral temporoparietal junction, cingulate gyrus, right precentral gyrus, anterior and posterior insula, bilateral fusiform gyrus and the cuneus (Hahn et al., 2006).
Figure 5: Top-down vs Bottom-up processing.
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1.1.4 Development In childhood there are three subsets of attention that are often studied: selective attention, sustained attention, and attentional control (executive attention) (Atkinson & Braddick, 2012).
The developmental trajectory of each type of attention is different (McKay, Halperin,
Schwartz, & Sharma, 1994) (See Figure 6), but they also interact with one another dynamically throughout development in order to function normally. Interestingly, all of these types of attention have be shown to have developmental jumps between 8-10 and 12-14 years old (Zhan et al., 2011), which may be related to maturation of the frontal cortex (Fuster,
2002). Specifically the cortical areas involved for each type of attention are: Selective attention, parietal structures project to the frontal eye fields/superior colliculus; Sustained attention, parietal cortex projecting to the right frontal cortex/locus coereleus; Attentional control/executive attention, left and right frontal areas and anterior cingulate cortex (Atkinson
& Braddick, 2012).
Selective attention is the focus on a specific object or location, while ignoring others
(Reynolds, 2015). It is one of the first aspects of attention to appear (orienting to stimuli at three months old) and is quickly developed to be able to address more complex stimuli over the first year of life (Courage, Reynolds, & Richards, 2006). In young children (under 5 years old) selective attention is intact and developing, but difficult to assess due to vigilance issues of current testing methods. In one study assessing 3.5 to 5.5 year olds they found that 46% of children under 4.5 years old were unable to perform a CPT task and those that could perform it had very high rates of omission errors; however at 4.5 years old children could perform the test comparably to adults (Akshoomoff, 2002). They suggested that children are capable of the selective attention demands of the task, but not the vigilance. In later childhood it was found, using a visual search task, that direction of selective attention becomes effective
(indicated by mouse movement) at 9 years old and response times stabilize at 14 years old
(Zhan et al., 2011). Neuronally, during a selective attention task there were only increases in 19 activation of the anterior cingulate and thalamus for 9-12 year olds compared to adults, suggesting that these areas are near neuronal maturation at this time regarding selective attention for simple search tasks (Booth et al., 2003).
Sustained attention is the ability to maintain selective attention for an extended period of time, however a specific time period is not specified by the literature. This ability develops linearly, with increased sustained attention ability progressing with age. It has been found that this progression can start as young as 14 weeks, with this linear relationship being found in a group studied from 14 to 26 weeks of age (Richards, 1985). At 10 months old, changes in both alpha and theta indicate sustained attention (Xie, Mallin, & Richards, 2018). After the first year it continues to develop. In a study of 17 to 24 month old toddlers it was found that sustained attention corresponded to age, with older children having a longer attention span
(Choudhury & Gorman, 2000). This continual improvement in sustained attention in relation to age was also found from 2 to 4.5 years (Graziano, Calkins, & Keane, 2011).
In childhood it has been found that there is rapid development of sustained attention between
5 to 10 years old, but that this rapid development ends around 10 years old, with only small improvements seen between 10 to 12 years old (Betts, McKay, Maruff, & Anderson, 2006).
However, in a study that assessed sustained attentional abilities across the lifetime (assessing
10 to 70 years old) they found that there was rapid developmental change in ability and strategy on a sustained attention task from 10 to 14 years, a transition time shifting to stabilized reaction times and a more conservative strategy from 14 to 17 years, and a continuing improvement in abilities until 43 years, at which point sustained attentional abilities began to decline (Fortenbaugh et al., 2015). Additionally, in a study by Zhan et al.
(2011) they found that target detection in a vigilance task plateus at 14 years old. These differences in results between the studies are most likely due to the different tasks used in each study. Neuronally, it has been found that the Contingent Negative Variation (CNV) and
20 the P3 wave have a frontal shift from 12 to 24 years of age, suggesting that neuronal maturation is still happening during this time span (Thillay et al., 2015).
Attentional control (executive attention) is the selecting/switching targets, inhibition, and selecting of an appropriate behavioral response (Atkinson & Braddick, 2012). These abilities emerge in the first year of life and improve throughout childhood due to improved inhibitory control and decline late in life as one loses this inhibition (Hommel, Li, & Li, 2004). It has been shown that children as young as 4 months old can begin to influence their attentional shifts (Kulke, Atkinson, & Braddick, 2015). From 17 to 24 months this ability increases and is related to sustained attention, with older children demonstrating greater problem solving abilities (Choudhury & Gorman, 2000). This linear improvement is also seen from 6 to 17 years old (Zhan et al., 2011). However, this ability has been found to level out around 10 years of age, with performance in this age group on a street-crossing task similar to that of adults (Nicholls et al., 2019). While attentional control may plateau at around 10 years of age
(possibly reflective of a slowing of grey matter accumulation and a parsing of connections), it is not thought to be fully developed until the frontal lobe completely matures (maturation of white matter), potentially as late as early adulthood (Fuster, 2002).
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Figure 6: Developmental Trajectory of different aspects of Attention.
1.2 Psychopathology Most deficits of attention are first diagnosed as the child enters the school system, however abnormal development of the attentional systems is thought to begin in the womb or infancy and continue through adolescence, with early perturbations of the system having long term and long range consequences. Children at risk for abnormal development of attention include early brain injury (focal lesions, hypoxic-ischaemic encephalopathy), early birth (before 32 weeks) as well as those with genetic predispositions for neurodevelopmental disorders
(Williams Syndrome, Down Syndrome, Fragile x Syndrome, ADHD, Autism). While many disorders have been well classified in regard to their attentional abilities, here the development of attention in ADHD will be described.
ADHD is defined by the DSM-V as “a persistent pattern of inattention and/or hyperactivity- impulsivity that interferes with functioning or development”. Affected children are then classified based on ADHD subtype: Combined, presenting both inattention and hyperactivity criteria, Predominantly inattentive or predominantly hyperactive-impulsive. Symptoms need
22 to be present for at least 6 months and meet a specific number of criteria to be considered a full diagnosis or in partial remission. The DSM suggests that at least some symptoms need to be present before the age of seven; however most of their criteria are specific to school age children and their abilities. Additionally, in recent years it has been suggested that ADHD may have many different Endophenotypes (Doyle et al., 2005), defined as ‘quantitative neurobehavioral traits that index genetic susceptibility for a genetic disorder’(Iacono, 2018), as well as different Biotypes (Barth et al., 2018), defined as ‘groupings of patients with the same underlying genetic mutation’. However these definitions are not yet used in the official clinical diagnosis of the disorder.
Specific developmental trajectories from birth are lacking for ADHD. However, in infants different factors such as increased physical activity (Ilott, Saudino, Wood, & Asherson, 2010), language and motor delays (Gurevitz, Geva, Varon, & Leitner, 2014), as well as irritability
(Sullivan et al., 2015) have been associated with later development of ADHD. So far, these issues have not been investigated regarding their neuronal mechanisms early in life; however it is likely that they are the result of impaired development of the attentional and inhibitory systems.
In preschool age, there is thought to be a prevalence of ADHD anywhere from 2% to 7.9%
(Egger, Kondo, & Angold, 2006) which then continues to be present at school age for 60-80% of cases, but subtype of ADHD (Hyperactive/impulsive, inattentive or combined) is unstable over this time (Lahey, Pelham, Loney, Lee, & Willcutt, 2005). In the 2 to 5 year old age range, high activity levels, short attention span and poor inhibitory control are normal, but children with ADHD display extremes of these behaviors (Cherkasova, Sulla, Dalena, Pondé,
& Hechtman, 2013). Sjöwall and Thorell (2019) found that some children with ADHD in this age range already show functional and neuropsychological impairments in executive processing and delay related behaviors; however their impairments do not directly reflect one
23 another and 23% of children that were diagnosed with ADHD did not have any neuropsychological impairment. The lack of neuropsychological impairment may reflect the parameters used to test impairments in this age group, be due to different endophenotypes of
ADHD or have to do with differing rates of brain maturation. Neuronally, it has been found in a group of 4 to 5 year olds that children with ADHD had reduced caudate volumes bilaterally and that left caudate volume corresponded to hyperactivity-impulsivity symptoms, but not inattentive symptoms (Mahone et al., 2011). However they found no differences in cortical volume or thickness in children with ADHD compared to controls. These discrepancies are difficult to interpret, since very few studies have been published regarding neuroanatomical differences of ADHD within this age group.
For older children and adolescents, neuropsychological deficits and neurological differences have been well described. Neuropsychologically, children with ADHD are impaired in their executive function and processing speed (Lawrence et al., 2004), motor skill (Karatekin,
Markiewicz, & Siegel, 2003) and working memory (Rapport et al., 2008). Specific to attention, it has been found that children with ADHD switch attention more frequently and have shorter periods of sustained attention that controls (Rapport, Kofler, Alderson, Timko, &
DuPaul, 2009). Additionally, executive functions of attention have been found to be delayed compared to non-ADHD children (Suades-González et al., 2017). Developmentally, children without ADHD show trajectories for response inhibition, error monitoring, attentional disengagement and alerting from 6-9 years of age, but these developmental changes were absent in children with ADHD (Gupta & Kar, 2009). In contrast, selective attention mechanisms do not seem to be impaired for 7 to 13 year old children with ADHD compared to matched controls, however the children with ADHD did make more errors on a visual search task (Mason, Humphreys, & Kent, 2003). In another study with 10 year olds, selective attention was not disturbed, while sustained attention was impaired compared to healthy
24 controls (DeShazo Barry, Klinger, Lyman, Bush, & Hawkins, 2001). These results culminate to suggest that executive and sustained attention are the primary areas for the deficits seen in
ADHD, while the paths for selective attention remain intact.
Neuroanatomical differences for ADHD are explored both regarding global alterations and specific alterations of the frontal cortex, basal ganglia and cerebellum (fronto-striatal- cerebellar circuits). In a review of child and adolescent magnetic resonance imaging (MRI) studies, it was shown that the most consistent finding between individuals with ADHD and neurotypicals was a global decrease in cortical volume, which is correlated with greater symptom severity (Krain & Castellanos, 2006). In a longitudinal study, which used peak cortical thickness as an indicator of neuronal maturity, it was found that when assessing 50% of the cortical points, individuals with ADHD reached peak cortical thickness at 10.5 years compared to 7.5 years for controls (Shaw et al., 2007). Increases of grey matter in comparison to controls have also been found, with increased grey matter volume for children with ADHD in posterior temporal, inferior parietal and right occipital lobes (Sowell et al., 2003), which may not only reflect a later maturation of neural tissue, but also an impairment in the normal pruning of these areas that should happen during childhood.
Regarding the fronto-striatal-cerebellar circuits, Shaw et al. (2007) found that the biggest delay of neuronal maturity was seen for the prefrontal cortex, with 5.9 years peak for controls and 10.9 years for ADHD participants. In a functional imaging study regarding the activation of the brain during a sustained attention task, children with ADHD had decreased bilateral striato-thalamic, left dorsolateral prefrontal cortex and superior parietal cortex activation when compared to age matched controls (Christakou et al., 2013). Christakou et al. (2013) additionally found that the differences in the dorsolateral prefrontal cortex activation were specifically related to sustained attention deficits in the children with ADHD. Striatal immaturity in children with ADHD was also seen in the hypoactivation of the basal ganglia
25 during a go/nogo task as well as the ventral prefrontal cortex and anterior cingulate gyrus
(Durston, van Belle, & de Zeeuw, 2011). Additionally, decreased cerebellar volume is found for children with ADHD compared to controls (Wyciszkiewicz, Pawlak, & Krawiec, 2016), cerebellar contributions to attention here were thought to be specific to spatial attention.
There are also specific neurophysiological differences that have been attributed to ADHD seen both in event related potentials and oscillatory activity. Regarding event related potentials throughout development individuals with ADHD show a decreased CNV (Cheung et al., 2018), which reflects vigilance preparation and has been found to be a particularly stable biomarker for ADHD (Doehnert, Brandeis, Schneider, Drechsler, & Steinhausen,
2013). Additional event related potentials that have been found to be altered during attentional tasks in children and adolescents with ADHD include: reduced frontal N1 and N2, reflective of early attentional processes; Parietal P2 and P3a/b, reflective of impaired allocation of attention resources (for a review Johnstone, Barry, and Clarke (2013)). Oscillatory differences that have been found for ADHD include elevated theta, reduced alpha and beta, and increased theta-beta- and theta-alpha- ratios (Barry, Clarke, & Johnstone, 2003a), however it has also been found that theta, beta, and alpha could be all in excess in ADHD (di Michele, Prichep,
John, & Chabot, 2005). Discrepancies of defining the neurophysiological differences arise because ADHD is highly individual and the different waves could be characteristic of different subtypes of ADHD. Specifically the theta-beta-ratio (TBR) has been an area of focus regarding therapies for ADHD, but it has been found to not be a reliable universal indicator for the disorder (Arns, Conners, & Kraemer, 2013).
By adulthood (assessed at 25 years old) there is a 15% persistence of a full diagnosis of
ADHD, but a 65% persistence of only partial remission (Faraone, Biederman, & Mick, 2006).
Specifically, inattention persists into adulthood (Faraone et al., 2006). While symptoms do tend to improve with increased age, individual in partial remission still have symptoms that
26 interfere with daily life. It has been shown that adults that had ADHD as children still have many risky and impulsive behaviors (sexual behavior, driving, criminal activity) (Fletcher &
Wolfe, 2009; Flory, Molina, Pelham, Gnagy, & Smith, 2006; Gnagy, Thompson, Molina, &
Pelham, 2007), as well as poorer life quality (lower occupations and socioeconomic status)(Agarwal, Goldenberg, Perry, & Ishak, 2012; Galéra et al., 2012; Mannuzza, Klein,
Bessler, Malloy, & LaPadula, 1993).
Neurologically, the deficits in adulthood are similar to those seen in childhood, but with some neural areas tending to normalize in relation to controls as development progresses, which leads to a reduction of symptoms. However, while basal ganglia deficits are more pronounced in children with ADHD, fronto-cortical dysfunction persists into adulthood (for a review see:
Rubia (2018)) as well as deficits in the functional connectivity between the cerebellum and the cortex (Kucyi, Hove, Biederman, Van Dijk, & Valera, 2015). Neurophysiologically, a decreased CNV continues to be seen in adulthood for individuals with ADHD (Doehnert et al., 2013), P3 differences normalize (Doehnert et al., 2013), and the N1/N2 has been found to be greater in adults with ADHD compared to controls (Prox, Dietrich, Zhang, Emrich, &
Ohlmeier, 2007). This increase in the N1/N2 response is thought to reflect a compensation mechanism for attentional difficulties. These long term effects highlight the need for early corrective therapy for deficits in ADHD.
The complex nature of ADHD development and the diversity of underlying mechanisms suggest that refined methods of treatment are needed.
1.3 Enhancing Attention
Since attention is a flexible resource, balancing excitation and inhibition of neural responses in order to create a representation of a stimulus in pursuit of a behavioral goal, any type of neuromodulation through therapy or medication that helped to stabilize this balance should help to improve focus on a task (Thiele & Bellgrove, 2018). In a review by Fortenbaugh, 27
DeGutis, and Esterman (2017), three primary areas of neuromodulation for sustained attention were named: 1. Cognitive/behavioral training, 2. Psychopharmalogical therapy, 3.
Biofeedback/ brain stimulation. However, there are also other factors to be considered that can modulate attention.
Cognitive/behavioral training operates on the idea that symptomology of cognitive disorders can be modified by cognitive factors, with the goal being the correction of maladaptive cognitions using therapeutic strategies (Beck, 1970). The use of such trainings to enhance attentional abilities can be used for individuals with deficits in attentional areas (ex.
ADHD) as well as healthy participants. For example, a meta-analysis of cognitive training for children with ADHD found that inattention improved, when symptoms were rated by a caretaker (Cortese et al., 2015). In healthy participants it has been found that trainings such as behavioral gaze training for infants (Wass, Porayska-Pomsta, & Johnson, 2011), classroom intervention for first graders (Keilow, Holm, Friis-Hansen, & Kristensen, 2019), and mindfulness practices (Chiesa, Calati, & Serretti, 2011) have been shown to improve attentional abilities.
Psychopharmacological influences on attention have been well documented. Based on their mechanisms, different substances have been shown to augment attention for a variety of disorders that have attention deficits (ADHD, depression, Alzheimers disease). Some attention enhancing drugs include: methylphenidate, amphetamine, modafinil, atomoxetine, memantine, caffeine, and nicotine (for a review see: Husain & Mehta (2011)). While medication is considered to be highly effective, the improvements in symptomology have to be weighed against the side effects and long term consequences of their use.
Biofeedback and brain stimulation have also been explored as methods to improve attention. Biofeedback for attention primarily refers to neurofeedback, which is a training that involves the presentation of real-time neural signals and learning how to modulate those 28 signals. Brain stimulation techniques that have also been used to enhance attention, including transcranial direct current stimulation (Clark & Parasuraman, 2014) and transcranial magnetic stimulation (Luber & Lisanby, 2014).
Many other internal and external factors can affect attentional abilities. Some internal factors include mood and motivation. It has been found that mood influences the direction of attention, poor mood results in an internal focus while good moods support more external attention (Sedikides, 1992). Motivation has been shown to modulate attention both behaviorally and neuronally (Engelmann, Damaraju, Padmala, & Pessoa, 2009), however the influence of motivation is also modulated by context (Calcott & Berkman, 2014). External factors include ambient noise, social interaction and ambient light. Ambient noise has been found to influence attention in children differently, those who struggle to pay attention in school had an improved performance with white noise while those who could normally concentrate well were impaired with the added noise (Helps, Bamford, Sonuga-Barke, &
Söderlund, 2014). Social influences can already been found in very young children (1 to 4.5 years old) in that maternal behavior indirectly influences sustained attention (Graziano et al.,
2011; Yu & Smith, 2016). In contrast ambient light has been shown to have a direct effect on attentional abilities (Chellappa, Gordijn, & Cajochen, 2011). The fact that many factors can influence attention needs to be recognized when trying to enhance these abilities. The focus of this work is the effects of neurofeedback and ambient light on attentional abilities.
1.3.1 Neurofeedback
Neurofeedback is a cognitive training that operates under the concepts of learning theory and utilizes a brain-computer interface to help participants learn to control their neural activity
(neuroregulation) (for a review see: Sherlin et al. (2011)). Neuroregulation is trained over a series of sessions (20+) through the feedback of neural signals to the participant with accompanying rewards if the neuroregulation occurs in the desired direction. Through this
29 feedback, participants learn to identify their inner states and to control them, first with the help of the computer and then in daily life (via transfer trials). Transfer trials involve tasks in which the participant is asked to neuroregulate, but the only feedback information provided is whether the neuroregulation was successful or not. This technique is used in order to help the participant internalize what it feels like to be in the desired neuronal state. By learning to neuroregulate, changes in behavior are induced which can be chosen for based on the protocol used. A few examples of specific neurofeedback protocols for specific issues: Alpha Rhythm for enhanced cognitive functioning; Theta and Sensorimotor Rhythm (SMR) for improved attention; Delta to improve sleep; Gamma for cognitive processing (for a review see:
Marzbani, Marateb, and Mansourian (2016)).
The use of neurofeedback as a way to enhance attention was initially explored by Lubar and
Lubar in the 1980’s. They expanded on previous work that had used neurofeedback to train
SMR activity in children with ADHD to address their hyperkinectivity by adding a beta component to the training in order to address attention and arousal (Lubar & Lubar, 1984).
This protocol was then altered to include theta band modulations (and as such the TBR) based on findings that the TBR was altered in ADHD (Lubar, 1991). From this humble beginning, the use of neurofeedback to address attentional issues in different disorders and for neurotypicals has been extensively explored, often using Slow Cortical Potential (SCP) or
SMR, theta, alpha or beta protocols. SCP protocols involve training the slowly changing positive and negative fluctuations of neural activity. These slow waves reflect increased
(negativity) or decreased (positivity) excitability of the neural activity as shown by decreased reaction times and attenuated startle reflex, respectively. SMR and band trainings serve to influence the excitation of the brain via the manipulation of specific bands that reflect specific excitation and inhibition activities of the brain, specifically most often accessed in the theta and beta bands for ADHD.
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1.3.1.1 Behavioral effects of neurofeedback Neuroenhancement of attention for healthy individuals using neurofeedback has been demonstrated. In a review by Jaing et al (2017), neurofeedback was found to restore attentional abilities in healthy older adults to be comparable to attentional abilities in younger adults. Additionally, a review by Gruzelier (2014) found evidence for attentional enhancement, both sustained and executive attention, in healthy participants that underwent neurofeedback training. In another study with healthy college students, 40 sessions of beta neurofeedback was found to result in higher sustained visual attention scores (Ghaziri et al.,
2013).
Neurofeedback has also been used as a treatment for different disorders including autism, learning disorders, epilepsy and ADHD (Hurt, Arnold, & Lofthouse, 2014). The use of neurofeedback for the different disorders is based in the concept that many psychiatric disorders can be attributed to excitation/inhibition imbalances in the brain. Learning to neuromodulate is meant to address specific regulatory issues in each disorder. The most research addressing specific attentional deficits has been investigated regarding ADHD.
Despite highly heterogeneous neurological deficits described for ADHD, neurofeedback has been found to be an effective treatment for inattention. In a meta-analysis by Arns, de Ridder,
Strehl, Breteler, and Coenen (2009b) they found that from pre- to post- neurofeedback training for children with ADHD, there was a large effect for inattention and impulsivity as well as a medium effects size for hyperactivity on parent rated assessment scales. These effects were also found in other meta-analysis, but with only inattention being significantly improved when using teacher ratings (Micoulaud-Franchi et al., 2014; Riesco-Matías, Yela-
Bernabé, Crego, & Sánchez-Zaballos, 2019). In contrast, other meta-analyses have found effects for inattention when using parent ratings, but not using teacher ratings (Cortese et al.,
2016; Sonuga-Barke et al., 2013). This discrepancy is primarily due to the assumption that 31 teacher ratings, as ‘probably blinded’, are a better indicator of the effectiveness of treatment than non-blinded parent ratings. However, this assumption has been brought into question by a new meta-analysis that has shown that this classification may not be appropriate in the case of neurofeedback due to the high variability of teachers scores and the tendency for teachers to underrate clinical symptoms, both elements that may mask clinical effects (Bussalb et al.,
2019).
Neuropsychological tests of neurofeedback efficacy have also found a positive effect for indices of attention. SCP neurofeedback improved the reaction time (small effect) on a sustained attention task from pre to post-test (Albrecht et al., 2017). While in a sample of children with ADHD who received individualized neurofeedback protocols, it was found that neurocognitive attentional indices improved in visual attention over the course of 40 training sessions (McReynolds, Villalpando, & Britt, 2018). In a study comparing theta suppression and beta vs alpha enhancement for children with ADHD, both protocols produced improvements in omission errors reflecting improved attention (Mohagheghi et al., 2017).
Additionally, in healthy adults, neurofeedback improved attentional abilities regarding orienting after a midline theta training (Wang & Hsieh, 2013).
While improvements in neuropsychological indices have been found, these results are often not tested against controls. In studies that use controls, the findings are mixed. Some have shown a beneficial effect of neurofeedback over control conditions: Theta/beta neurofeedback has been shown to improve reaction time in a CPT task (Bakhshayesh, Hansch, Wyschkon,
Rezai, & Esser, 2011), while SCP neurofeedback decreased impulsivity errors (Heinrich,
Gevensleben, Freisleder, Moll, & Rothenberger, 2004). However, there are also studies in which no difference could be found: neurofeedback and treatment as usual had similar improvements in a test of attention (Bink, van Nieuwenhuizen, Popma, Bongers, & van
Boxtel, 2014); no significant differences found for attentional measures between a
32 neurofeedback and exercise control (Gelade, Bink, et al., 2016). Additionally, a meta-analysis of randomized control trials found no change in neuropsychological measures reflecting attention and inhibition after neurofeedback training (Cortese et al., 2016), however only seven studies were included that assessed neuropsychological measures of attention and measures varied between the studies. This resulted in the need to pool different measures of attention to reflect general domains which may have decreased the validity of this measure. In general, the influence of neurofeedback on neuropsychological measures remains unclear and more randomized control trials with standardized control conditions and protocols are needed to clarify this issue.
The primary focus of the bulk of the literature has been on specific effects of neurofeedback in the short term (i.e. directly after training). It’s effectiveness as a therapy for ADHD has been found to be comparable to non-medication trainings (Arns, de Ridder, Strehl, Breteler, &
Coenen, 2009a). However, while improvements in symptoms during and directly after training are desired, an ideal therapy would also persist in the long term. Neurofeedback is a therapy that does theoretically offer this advantage, since it is thought to be at least partially based in procedural learning and as such any learning that occurred during training should be able to be applied without active neurofeedback training taking place. Some studies have demonstrated that there are persistent effects for inattention symptoms in ADHD after training has ended (Arnold et al., 2013; Bink, Bongers, Popma, Janssen, & van Nieuwenhuizen, 2016;
Duric, Assmus, Gundersen, Duric Golos, & Elgen, 2017; Gevensleben et al., 2010; Li, Yang,
Zhuo, & Wang, 2013; Meisel, Servera, Garcia-Banda, Cardo, & Moreno, 2013; Steiner,
Frenette, Rene, Brennan, & Perrin, 2014), however methods and time spans of follow-up vary. Additionally, Arns and Kenemans (2014) assessed long term follow-up in two studies of neurofeedback and found sustained effects for hyperactivity/impulsivity. An assessment of more studies with follow-up periods might reveal similar effects for inattention.
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1.3.1.2 Neurophysiological effects of neurofeedback
A full understanding of the specific mechanisms that are at work during neurofeedback training and what is then changed after training is not yet fully understood, however multiple studies have started to identify neural areas and patterns that are involved. During neuroregulation, a number of brain areas have been found to be active. Specifically, a meta- analysis of functional Magnetic Resonance Imaging (fMRI) studies using neurofeedback found that there is consistent activation in the anterior insula and striatum, with often activated areas including the anterior cingulate cortex, dorsolateral and ventrolateral prefrontal cortex, temporo-parietal areas and visual association areas (Emmert et al., 2016).
Interestingly these areas have a lot of overlap with the areas known to be active in attention, but it is not yet known whether they are active due to the attention necessary to conduct neuroregulation or due to the neuroregulation itself. However, regardless of why these areas are active it seems that by activating them they can be strengthened and this has effects on the attentional systems.
While the areas that are activated during neuroregulation help to understand what is at work during neurofeedback training, the changes induced by neurofeedback are interesting to investigate in regard to improvements in behavior. It has been shown that various neurophysiological changes occur after neurofeedback training, which can be measured in a variety of ways. Here changes in structural/connectivity components, event-related potentials, and oscillatory power will be described.
Multiple structural and connectivity changes after neurofeedback training have been documented. In a study that assessed fMRI changes in children with ADHD after neurofeedback training, increased right anterior cingulate cortex activation during a counting
Stroop task was found (Lévesque, Beauregard, & Mensour, 2006). This area is important for attentional shifting (Kondo, Osaka, & Osaka, 2004) and attentional preparation (Luks,
34
Simpson, Feiwell, & Miller, 2002). In a study of healthy university students there were both functional white matter (superior longitudinal fasciculous, anterior limb of the internal capsule) and right hemisphere grey matter volume (middle frontal gyrus, inferior temporal gyrus, middle occipital gyrus, thalamus) increases found after neurofeedback training (Ghaziri et al., 2013). Specifically the functional white matter changes were correlated to behavioral improvements in sustained visual attention. Short term training has also been found to change neural connectivity: 30 minutes after a single session of alpha neurofeedback training, increases in salience network connectivity were found for the dorsal anterior cingulate cortex
(Ros et al., 2013), another key area for attention.
Event related potentials are another window into the brain to identify changes after neurofeedback training. In healthy adults, training of beta components resulted in an increased
P3 amplitude which was associated with faster reaction times on an attention task (Egner &
Gruzelier, 2004). In contrast, in healthy adults, a decrease in P3 amplitude was found after theta/beta training, which was thought to reflect the need for fewer attentional resources
(Studer et al., 2014; Wangler et al., 2011) based on the training. However, a study examining theta/beta neurofeedback compared to methylphenidate and physical activity in children with
ADHD found no difference in P3 or N2 amplitudes after neurofeedback training when compared to the physical activity control (Janssen et al., 2016b). After SCP training, an increased CNV has been found for healthy adults as well as children with ADHD, which is thought to reflect an increase of available attentional resources while solving a CPT task.
Assessing power spectrum changes helps to understand the synchrony of brain areas during tasks, and changes have been demonstrated after neurofeedback training. In healthy subjects, neurofeedback of the beta wave induced short and long term (3 years post training) increases of beta activity in comparison to controls (Engelbregt et al., 2016), however these changes were not associated with changes of cognitive ability (tested via IQ measurements). While
35 another study with healthy students found that a training to upregulate beta affected both beta and alpha bands post training, suggesting that band changes may not be dependent on specific training used (Jurewicz et al., 2018), again band changes were not associated with behavioral indices. The lack of correspondence between wave upregulation and attentional parameters may be due to an already maximized efficiency of these waves in the healthy brain before training, making any upregulation obsolete on a behavioral level. However, for individuals whose brains are potentially not yet functioning optimally, such trainings have been shown to be beneficial. Regarding children with ADHD, a 30 session theta/beta training resulted in resting theta decreases (no change in beta) that were comparable to those induced by methylphenidate and were correlated with reduction in parent rated symptoms for both inattention and hyperactivity/impulsivity, however this decrease was not seen during cognitive task performance (Janssen et al., 2016a). Theta reductions were also found in other studies that were reflected in decreases of ADHD symptoms (Gevensleben et al., 2009).
Overall the neurophysiological findings are mixed, but it does seem evident that neurofeedback stimulates biological change in the brain and this biological change can be considered the basis for resulting behavioral differences after neurofeedback training.
1.3.2 Light
Light is essential for human health. It serves a crucial role in setting the circadian rhythm and the associated melatonin secretion, which drive arousal via the sleep wake cycle. This arousal influences attentional ability and sleep patterns. In turn sleep influences attentional ability
(Kirszenblat & van Swinderen, 2015), however light can also influence attention directly via cellular afferents from the retina to neural areas responsible for attention modulation.
Light exposition has been explored for its therapeutic use for patients with sleep disorders, but also for other disorders that are accompanied by symptoms of sleep disturbance, such as depression (Nutt, Wilson, & Paterson, 2008) and ADHD (Hvolby, 2015). Light therapy not 36 only improves the quality of sleep for patients, but also improves symptoms of the primary psychiatric diagnosis: Depression (Gest et al., 2016); ADHD (Fargason et al., 2017). Such studies highlight the importance of light and sleep for cognitive health.
Light is of course a natural phenomenon of the daily rising and setting of the sun; however in current society we also encounter artificial light, both during the day and at night. The light encountered comes from natural lighting, ambient lighting and electronic devices and varies in duration, intensity and wavelength. All sources of light have an impact on attentional abilities and circadian rhythms, and can support or disturb these processes based on the type of light encountered and time of the day. The effects of light on attention have been investigated at both the behavioral and neurophysiological level.
1.3.2.1 Behavioral effects of light
Cognitive abilities fluctuate throughout the day and night based on circadian rhythms, and are considered to be most effective during daytime (Schmidt, Collette, Cajochen, & Peigneux,
2007). These natural cognitive rhythms can be manipulated by the timing of light exposition, with the greatest effects of light seen at night when they are most impaired (Duffy & Czeisler,
2009). Biological response can also be modified by the physical quality of the light, such as its wavelength (measured in nanometers (nm), color temperature (measured in Kelvins (k)) or luminosity (measured in LUX (lx)). Additionally, light can affect cognition directly (short term) as demonstrated by changes in task performance during exposition or indirectly (long term) as demonstrated by alteration of circadian rhythms. Both direct and indirect effects are a result of interactions between the time of day the light is experienced and the physical characteristics of the light.
Daytime exposure to blue light mimics exposure to bright natural daytime light and has been found to directly enhance cognitive abilities. In a study by Hartstein, LeBourgeois, and
Berthier (2018), it was found that exposure to different color temperatures of light (630 37 nm/3500 k vs. 475 nm/5000 k) during the day had differential effects on the task switching abilities in preschool children, namely task switching had fewer errors under the blue light
(475 nm) condition. However, they found no difference between conditions for sustained attention. For school age children, an emphasis has been put on the effect of ambient lighting in the classroom and its effects on attentiveness and cognitive functioning. It has been found that blue enriched light in the classroom enhances attention (fewer omission errors) and reading speed (Barkmann, Wessolowski, & Schulte-Markwort, 2012) as well as improved concentration and increased cognitive speed (Keis, Helbig, Streb, & Hille, 2014). Similar effects of exposure to enriched blue light have been found for adults in office settings regarding subjective reports of alertness and concentration (Viola, James, Schlangen, & Dijk,
2008), however objective measures of attention performance after daytime workplace light exposition seem to be missing from the literature. Interestingly, blue light has also been found to be comparable and even superior to caffeine for improving attention (Beaven & Ekström,
2013). However, a study investigating daytime exposure to blue vs green light found no difference in attentional abilities for sleep deprived adults (Segal, Sletten, Flynn-Evans,
Lockley, & Rajaratnam, 2016). These conflicting results suggest that the sleep history of the participants may influence how light activates the attentional system.
Similar conflicting findings have been found regarding the effects of luminescence (measured in LUX (lx)) on daytime attention. When comparing bright (1000 lx) vs dim (5 lx) lighting, it was found that the bright condition improved attention and sleepiness measures in sleep- deprived adults (Phipps-Nelson, Redman, Dijk, & Rajaratnam, 2003). Similar effects were also found after one hour daytime exposure to bright light in non-sleep deprived adults, with additional reaction time improvement in a vigilance task during bright light exposure
(Smolders, de Kort, & Cluitmans, 2012). However another study that exposed participants to one hour of white light throughout the day, did not find any differences in attentional abilities
38 in well-rested individuals after daytime bright white light exposure (Lok, Woelders, Gordijn,
Hut, & Beersma, 2018). These differences are difficult to interpret, but do indicate that previous sleep history does need to be considered when interpreting light effects and that wavelength may be more important than luminosity in activating the attentional system. The finding that wavelength is more important than luminosity has already been suggested for sleep parameters (Green, Cohen-Zion, Haim, & Dagan, 2017), but not directly for attention.
Evening and night time exposure to blue enriched light is not in accordance with natural daytime light and is investigated as a method to improve performance during an inherently difficult attentional period. It has been found that constant blue light exposure during night- time driving improves driving performance in adults (Taillard et al., 2012), comparable to the effects of caffeine. Additionally, in night-shift workers, week long exposure to blue enriched light decreased omission errors and improved reaction time on a sustained attention test as well as reducing sleepiness (Motamedzadeh, Golmohammadi, Kazemi, & Heidarimoghadam,
2017). Short term light exposure also has an effect: two hour evening exposure to blue enriched light also improved reaction time on a sustained attention task (Chellappa, Steiner, et al., 2011). Additionally, luminosity differences are important to consider at night: night shift workers demonstrated improved concentration under a bright light (3000 lx) compared to a dim light (300 lx) condition (Kretschmer, Schmidt, & Griefahn, 2011).
While both day and night exposure to blue enriched light have been found to directly improve cognitive performance, the long term effects of light on the circadian rhythm also need to be considered. Light exposure can be circadian effective or ineffective and exposures to light that is effective can help increase attention and improve sleep quality, but ineffective exposure improves attention at the cost of interrupting sleep patterns. Due to this the timing and quality of light exposure is important to consider when assessing the effect of light on cognition and
39 attention, because sleep is essential for proper function of attentional processes (Kirszenblat & van Swinderen, 2015).
Circadian ineffective blue light (evening/night exposure) has been shown to disrupt sleeping patterns and negatively impact subjective evaluations of attention and performance of cognitive tasks the following day (Chellappa et al., 2013; Green, Cohen-Zion, Haim, &
Dagan, 2018; Van der Maren et al., 2018; Wams et al., 2017). In contrast, it has been found that presentation of blue circadian effective light in the morning supports sleep patterns and could be a way to counteract other exposure to light throughout the day (Münch et al., 2016).
Interestingly, bright red light exposure at night was found to be superior to dim white light and comparable to bright white light for improving attentional measures, but the red light setting improved attention without decreasing melatonin levels and as such may have less of an impact on sleep (Figueiro, Sahin, Wood, & Plitnick, 2015). These finding again highlight the importance of timing and light quality when trying to improve attention.
1.3.2.2 Neurophysiological effects of light
There are two systems that are influenced by the physical characteristics of light to affect attention: the visual system and the non-visual system. The visual system refers to the action of light on rods and cones and how those signals are then transformed into internal representations of what we see and what we need to be attentive to. This system is dynamically connected with the attention network (see above sections on general attention), and has been found to give indirect output to the non-visual system (Hankins, Peirson, &
Foster, 2008). However, the effect of light on attention is primarily under the control of the non-visual system, aka the non-image forming system. This system is activated preferentially by blue light and operates via the intrinsically photosensitive melanopsin-containing retinal ganglion cells (ipRGCs) (Brainard & Hanifin, 2005). These cells drive circadian photoentrainment and are the beginning of a signaling pathways targeting subcortical neural
40 structures that are responsible for alertness (hypothalamus, brainstem, thalamus, amygdala, hypocampus) which then extend to cortical structures to modify behavior (Do & Yau, 2010;
Vandewalle, Maquet, & Dijk, 2009). The activation of this pathway can be measured through cortical activation in regions for attention, oscillatory EEG activity, and event related potentials.
The influence of light on attention has been tested in healthy participants as well as in in blind individuals who have damaged visual systems, rendering only the ipRGCs still functional.
Cortical areas activated by ipRGCs include those involved in top-down attentional processes
(dorsolateral prefrontal cortex, superior parietal lobule, intraparietal sulcus) (Perrin et al.,
2004; Vandewalle et al., 2006; Vandewalle et al., 2007) as well as areas responsible for bottom-up processes (right insula, anterior cingulate cortex, superior temporal gyrus)
(Vandewalle et al., 2006; Vandewalle et al., 2013; Vandewalle et al., 2007). The study by
Vandewalle et al. (2006), assessing daytime exposure to light in healthy participants, highlights the direct influence of both these top-down and bottom up area activations by light.
They found that the effects induced by light then rapidly dissipated after light exposition ends
(10 minutes after exposition). Additionally these areas were shown to be preferential for blue light in healthy (Vandewalle et al., 2007) and visually blind individuals (Vandewalle et al.,
2013), supporting the behavioral findings that blue light is more effective for activating the attentional system than other wavelengths.
Blue light has also been found to influence oscillatory activity. In blind individuals with an intact non-image forming system, blue light was found to modulate alpha activity in the occipital cortex (Vandewalle et al., 2018). Alpha activity is also induced during visual stimulation and has been thought to be linked to processing of the visual stimulus such as attentional binding (Nakayama & Motoyoshi, 2019) and top-down spatial attention
(Dombrowe & Hilgetag, 2014). However, Vandewalle’s finding suggests that it is rather the
41 ipRGCs that may be responsible for the impact of light on visual attention, potentially through top-down attentional influences of the parietal cortex (Perrin et al., 2004). Suppression of alpha is also considered to indicate alertness (Sadaghiani et al., 2010), and it has been found that both short and long wavelengths do suppress alpha in a morning exposition (Okamoto,
Rea, & Figueiro, 2014).
Luminosity also changes oscillatory activity, but potentially for a different reason. Alpha suppression was found in response to higher luminosity (700 lx vs 150 lx), regardless of color temperature, however this did not facilitate attention, suggesting that luminosity may be more relevant for early visual processing (Min, Jung, Kim, & Park, 2013). In contrast, suppression of theta/alpha activity (5-9 hz) was found to be correlated with subjective sleepiness and dependent on the intensity of light, with greater magnitude reductions seen for higher luminosities (Cajochen, Zeitzer, Czeisler, & Dijk, 2000). That this suppression of activity correlated with subjective sleepiness does reflect an increased alerting response, and better attentional performance would be anticipated. In another study subjective sleepiness and theta and alpha activity were both suppressed after bright light exposure in sleep deprived participants, however this suppression during a cognitive Stroop Task faded the longer the participants were awake (Yokoi, Aoki, Shiomura, Iwanaga, & Katsuura, 2003). These findings of changes in alpha and theta are highly relevant for neuronal activities during attentional activation and indicate that both wavelength and luminosity can influence the attentional system through the ipRGCs, however the influence of these activations is dependent on sleep.
Event related potential research has also shown effects of light on attentional systems. During an auditory oddball paradigm it was found that the elicited P3 wave was larger after short- wavelength light compared to darkness, while there were no differences to darkness found for medium or long-wavelength light (Okamoto & Nakagawa, 2015). This effect has also been
42 found to be dependent on the time of day of the light exposure. A study examined the effect of short and medium wavelength during the day and at night. They found that the P3 was largest during short wavelength exposition at night (An, Huang, Shimomura, & Katsuura, 2009).
Additionally, simultaneous presentation of light with an auditory sustained attention task modulated cortical EEG responses at Cz in blind individuals with an intact non-image forming system (Vandewalle et al., 2013). These results further support the role of ipRGCs in the attentional activation caused by light exposure and emphasize the importance of timing regarding light effects.
2. Optimizing therapeutic techniques for improving attentional abilities as the goal of this dissertation
Based on the literature, attention can be considered a flexible resource that is essential for healthy human functioning. Developmental delays in attention begin to emerge as early as infancy and over the lifetime can result in poor life quality. Optimizing therapies for attention deficits is an ongoing effort and many different aspects of current therapies need to be explored. As seen above, two potential options to influence attention in a clinical setting include neurofeedback training and light exposition. These methods are of particular interest due to the lack of side effects (in comparison to medication), however more evidence is still needed to support their use as clinical methods to treat attention deficits. It is the goal of this dissertation to investigate open questions regarding these methods in order to support an optimization of therapeutic techniques for attentional abilities in a clinical setting.
In an attempt to improve symptoms of inattention, specifically for ADHD, neurofeedback has been developed and shows a lot of promise as an effective therapy. However, despite its widespread use, neurofeedback is still questioned as a valid therapeutic technique primarily due to a lack of information regarding its mechanisms of action, its time and financial costs,
43 as well as doubts regarding its efficacy. In order to address these questions, more information regarding the neural effects as well as the long term symptomatic effects are greatly needed.
To this end, two projects of this dissertation attempted to contribute to these open questions: the Short-term Neurofeedback Study and the Meta-analysis.
In the Short-term Neurofeedback Study, the primary goal was to understand what is happening in the brain when it first encounters neurofeedback. This information can potentially then be used to optimize neurofeedback procedures in order to make them a more economical option. A few such short term studies have been performed in adults (Kluetsch et al., 2014; Ros, Munneke, Ruge, Gruzelier, & Rothwell, 2010; Ros et al., 2013), but this was the first of such studies to look at short term-effects of neurofeedback in children. It is important to assess the short term effects of neurofeedback in children due to the developmental changes that occur during childhood, which may result in different findings than in adults, as well as the need for effective clinical treatments early in life in order to improve later life quality.
In addition to information regarding the neural mechanisms, information regarding the long- term effects of neurofeedback training are of interest. There are many studies that have investigated the effects of neurofeedback on ADHD symptoms directly after training, however there are only a few studies that have assessed a follow-up period. These studies have issues such as low numbers of participants and variable follow-up time periods that make the found effects difficult to interpret. In order to better understand whether neurofeedback has long term benefits for ADHD symptoms, the Meta-analysis was conducted including randomized controlled neurofeedback studies that assessed symptoms at three time points: pre-, post- and follow-up to treatment.
Regarding other aspects that can influence attention, the use of light in a clinical setting is an interesting tool that requires more information in the child/adolescent demographic. Effects of 44 light on attention have primarily been described in adults, but before light can be applied for children as a therapeutic tool for attentional enhancement, it is important to see if there are effects for this demographic in healthy participants. To this end, in the Light paper, the effect of light on the cognition and sleep of healthy adolescents was tested.
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3. Peer reviewed papers
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3.1 Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects
Van Doren, J., Heinrich, H., Bezold, M., Reuter, N., Kratz, O., Horndasch, S., Berking, M.,
Ros, T., Gevensleben, H., Moll, G. H., Studer, P. (2017). Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects. Int J
Psychophysiol, 112, 80-88. doi: 10.1016/j.ijpsycho.2016.11.004
3.2 Sustained effects of neurofeedback in ADHD: a systematic review and meta- analysis
Van Doren, J., Arns, M., Heinrich, H., Vollebregt, M. A., Strehl, U., & Loo, S. K. (2019).
Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. Eur
Child Adolesc Psychiatry, 28(3), 293-305. doi: 10.1007/s00787-018-1121-4
3.3 Effects of blue- and red-enriched light on attention and sleep in typically developing adolescents
Studer, P., Brucker, J. M., Haag, C., Van Doren, J., Moll, G. H., Heinrich, H., & Kratz, O.
(2019). Effects of blue- and red-enriched light on attention and sleep in typically developing adolescents. Physiol Behav, 199, 11-19. doi: 10.1016/j.physbeh.2018.10.015
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4. Discussion
4.1 Overview of the results
Theta/beta neurofeedback in children with ADHD: Feasibility of a short-term setting and plasticity effects.
In the evaluation of the short-term effects of neurofeedback in children with ADHD, children participated in two neurofeedback sessions. During each session they solved a neurofeedback puzzle task (three puzzles per session), in which puzzle pieces were added to a board on the computer screen based on the child’s neuroregulation, as well as a paper and pencil picture search task and reading task (pre- and post- neurofeedback). It was found that attentional resources, reflected by suppression of the theta wave, were able to be influenced after only two neurofeedback sessions.
During neuroregulation in the second session, both theta and the TBR decreased from the first to the third puzzle for the group as a whole. Additionally, this time effect had an interaction with group, in which the poor regulators had an increase of theta while the good regulators had a decrease in theta; a similar effect was not seen when examining the beta wave. This group interaction suggests that the decrease in theta and the TBR for the group as a whole was primarily driven by the performance of the good regulators. Visual inspection of the theta activity during the trials showed that the theta effect was primarily seen in the beginning of the trial and then disappeared towards the end; a pattern which was more evident with good performers than poor performers. This pattern of activity can be interpreted as reflecting the need for concentrated effort in order to neuroregulate, an effort which dissipates throughout the course of the trial, especially after only a few sessions of training. Baselines for both groups remained unchanged throughout the experiment, reflecting that good and poor regulators did not differ in this measure, which was somewhat surprising considering baseline theta has been found to differentiate clinical outcome after theta/beta neurofeedback treatment
48
(Gevensleben et al., 2009). However, neurophysiological profiles of ADHD are highly individual, and our sample may have been too heterogeneous to replicate those findings.
Alternatively, our lack of a baseline theta difference between good and poor performers may indicate that baseline theta and neuroregulation ability are not inherently related to one another.
Behavioral influence of the two sessions of neurofeedback was not significant, as measured by performance on the picture search task. There was a trend toward significance for time, in which more pictures were found in the post- than in the pre-test, but this was most likely due to practice effects. However, spectral measures assessed during the cognitive tasks found that good regulators and poor regulators had differences in theta activity at Cz in the second session during the reading task: Good regulators theta activity decreased while poor regulators theta increased from pre- to post- test. This finding can be interpreted as evidence for neuroplasticity of elements of attention after only two sessions of neurofeedback and is the first such finding in children. Overall this study demonstrates that neurofeedback begins to affect neural function early in training, indicating a very flexible and reactive attentional system in children with ADHD.
Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis.
In order to assess the long-term effect of neurofeedback as a therapy for ADHD, a meta- analysis was conducted of neurofeedback studies which included a pre-, post, and follow-up period. Inclusion criteria were met by 10 studies, of which only nine had control groups which could be included. A total of 506 participants with ADHD (256 neurofeedback, 250 control) were included in the analysis. Follow-up time periods of the papers were varied with the bulk of the papers reporting follow-up from 6 months (6 papers) and the remaining papers using varied follow-up time periods: two months ( 2 papers), 3 months (one paper), twelve months
(one paper). Within group analyses were used to assess the symptom domains inattention and 49 hyperactivity/impulsivity. Due to high heterogeneity of the control conditions when grouped together, controls were analyzed separately based on active and non-active control conditions.
The within group meta-analysis for inattention found that there was a significant medium effect of neurofeedback treatment from pre- to post- treatment, and a large effect from pre- to follow-up measurement, but no significant effect found for post- treatment to follow-up; Non- active controls had a small significant effect from pre- to post-, but not from pre- to follow-up or post- to follow-up; Active controls had significant effects from pre- post and pre- to follow- up, but not post- to follow-up. For hyperactivity/impulsivity: Neurofeedback had significant medium effects sizes for both pre- to post- and pre- to follow-up, but no effect was found for post- to follow-up; Non-active controls were not significant for any of the measurements;
Active controls had significant medium effects for pre- to post- and pre- to follow-up, but not post- to follow-up. This method of meta-analysis is less common than a between group analysis, but does demonstrate robust long-term effects of neurofeedback that are missing for the non-active controls. Active controls have similarly robust symptom decreases; however the influence of medication is known to be more effective than neurofeedback alone, but with the risk of permanent side effects due to long term medication use.
These findings are reconfirmed in the between group analysis, which was conducted in order to improve comparability of this study to previously conducted meta-analyses. For inattention, the comparison of neurofeedback to non-active controls revealed that neurofeedback was favored: There was a small significant effect for pre- to post- and a medium significant effect for pre- to follow-up, with no significant effect found for post- to follow-up. When considering inattention comparing neurofeedback to active controls, active controls were favored at pre- to post- with a medium effect size, but there was no significant effect from pre- to follow-up nor post- to follow-up. Regarding hyperactivity/impulsivity in the between group analysis, heterogeneity allowed a comparison of neurofeedback to all controls, which
50 revealed a small significant effect size for neurofeedback at pre- to follow-up, but pre- to post- and post- to follow-up were not significant. When only including non-active control groups both pre- to post- and pre- to follow-up favored neurofeedback with small effect sizes.
Comparison of neurofeedback to active controls resulted in no significant findings. These findings support those of the within group analysis, that neurofeedback can be considered superior regarding sustained clinical effects for both inattention and hyperactivity/impulsivity when compared to non-active controls. Neurofeedback remains less effective than active controls from pre to post, but is then comparable to active treatments regarding long term effects (pre- to follow-up) for the hyperactivity/impulsivity domain.
Effects of blue- and red-enriched light on attention and sleep in typically developing adolescents.
Within the context of situational influences on attention, the effect of light exposure (red vs blue light, aligned or misaligned with the natural circadian rhythm) in neurotypical adolescents was assessed for three tests of attention: arithmetic tests (addition, number comparison), a reading task (checking sentences for content), and the Attention Network Test
(ANT) (number of hits, reaction time, variability of reaction time). These tests revealed better performance in two of the attention tasks (math test performance and ANT reaction time variability) based on light exposition.
For the arithmetic task, reading task and ANT reaction time, there was a significant interaction of type of light and order that the light was presented. These results can be attributed primarily to the group that completed the tasks for the second time under the blue light condition, and as such are thought to be primarily related to the repeated measures design (practice effects, rather than light effects). However, specifically for the arithmetic task performance, there was a significant effect of light, with a better performance under the blue light compared to red light condition. An effect of light was missing for the reading task and 51 the ANT reaction time measures, suggesting that the improved arithmetic performance seen under blue light conditions cannot only be attributed to repetition effects, but rather that blue light may have enhanced the attentional ability during this task.
Other parameters of the ANT were found to have been influenced by light exposition. Namely there was a trend for an effect of light for mean number of correct responses, with more correct responses under the blue light condition. However, this parameter tends to result in a ceiling effects, with low variability of correct responses seen between participants (Sarter,
Givens, & Bruno, 2001). More interestingly, there was a significant effect found for light on reaction time variability, a way to assess sustained attention. Specifically, the blue enriched light condition resulting in lower reaction time variability.
In addition to the effects on attention, light exposition also affected some sleep parameters.
There were no differential effects of light on total sleep time, sleep efficiency, or wake after sleep onset, but these values were within normal ranges reported by previous research. A trend toward significance was found for light regarding the number of phases of movement activity after sleep onset, specifically that there was less movement after red-light exposition in the evening. Subgroup analysis of later exposition time in the evening revealed a trend for light regarding sleep onset latency: sleep onset latency was shorter after evening red-light exposition compared to evening blue-light exposition.
4.2 Theoretical and Practical Implications
The results of these three papers culminate to add evidence to the existing literature that attentional abilities can be influenced by external factors in a clinical setting, here specifically neurofeedback training and ambient light. While neurofeedback has been studied for over 30 years as a therapy for the rehabilitation of attentional deficits seen in ADHD, results of the short term neurofeedback paper are the first to identify specific neural mechanisms that are active at the very beginning of training in children with ADHD. In compliment to the early 52 changes identified in the attentional systems of children with ADHD, the meta-analysis found that neurofeedback also has a long-term effect: improvements in inattention symptoms were found both directly after training and at follow-up. These findings support the idea that early changes to the attentional system, even in clinical populations, result in lasting effects. Finally the findings that blue light can influence attentional parameters in a clinical setting in healthy adolescents supports the assumption that ambient lighting is an important factor to consider when designing a therapeutic setting, and that it could be considered as an additive treatment method. Taken together, these studies provide evidence for potential optimizations of neurofeedback as a therapeutic technique for ADHD.
The work of the short term neurofeedback paper supports previous results that theta waves can be externally influenced, and that this could be a mechanism with which to enhance attentional abilities. Theta activity has been found to be a correlate of attention in many studies and high theta activity is suggested to be a possible mechanism for the deficits seen in
ADHD, more so than the beta wave (Barry, Clarke, & Johnstone, 2003b; Heinrich et al.,
2014). However, our separation of the groups based on regulation ability was not then reflected in the baseline activity, in that there was no group difference in TBR, theta, or beta activity before the influence of neurofeedback. This finding is supported by recent research which has not found TBR or theta activity at rest to be definitive markers of ADHD, but rather suggestive of different ADHD neurophysiological endophenoptypes (Arns et al., 2013).
What our findings of differential patterns of theta activity both during neuroregulation and during the reading task for good and poor regulators suggests, is that there may also be neurophysiological endophenotypes of ADHD that could be assessed during cognitive tasks.
By conducting a neurophysiological assessment early in neurofeedback training it may be possible to predict the success of training for the individual. An early assessment of possible success would allow for the identification of children that can or cannot regulate and either
53 provide non-regulators with a change in protocol or terminate training, if it is thought that beneficial effects are unable to be reached. This issue of identifying those who can benefit from training is highly relevant, since it is estimated that up to 20% of the population cannot engage with brain-computer interfaces (Allison & Neuper, 2010) and since ADHD is such a heterogeneous disorder (Barth et al., 2018; Doyle et al., 2005). If those individuals that will benefit from neurofeedback training can be identified, then the cost/benefit ratio can be optimized and criticism of neurofeedback as an excessively expensive and time consuming training for what it provides could be addressed. Much more research is needed to support this idea, but it could be a way to optimize neurofeedback trainings to treat ADHD.
The identification of non-responders to neurofeedback therapy would also potentially allow for better comparison of this therapy to the use of medication for the treatment of ADHD.
Neurofeedback is already often directly compared to methylphenidate since it is currently the most effective ADHD treatment, and is often seen as inferior to medication when assessed via pre- to post- treatment symptom change. Interestingly, neurofeedback and psychostimulants have similar rates of non-responders (20%) (Allison & Neuper, 2010; Briars & Todd, 2016), but the inclusion of these non-responders in randomized control trials may differ between the treatments. In most studies, psychostimulant doses are optimally titrated before the onset of the study and during this titration process the profile of participants that drop out due to side effects may alter the number of included participants that are non-responders to medication, while all participants receiving neurofeedback are included in the analysis. As such, the inclusion of potential non-responders to neurofeedback may skew the current available data regarding neurofeedback efficacy in relation to medication. If neurofeedback non-responders could be identified early and taken out of the participant pool, neurofeedback may be more comparable to treatment with medication than current data suggests. Additionally, if a non- response to a specific neurofeedback protocol could be seen early in training, a different
54 training could be potentially implemented and/or a protocol selection process could be developed to identify the optimal neurofeedback training for the specific participant, in an attempt to match the methylphenidate titration process.
One often heard criticism of neurofeedback is that its effects may be non-specific and rather be attributable to the one-on-one therapeutic nature of the setting. While an ideal study to definitively prove the effects of neurofeedback would conduct a full length neurofeedback training in isolation without the help of a therapist, motivational and instructional elements
(especially regarding transfer trials) remain import and continuing to provide the neurofeedback therapy with a trainer or therapist present remains unavoidable. The trainer element is arguably even more essential for the treatment of ADHD, where a main tenant of the disorder is distractibility. However, doubts about trainer influenced non-specific effects of neurofeedback are addressed by the short term neurofeedback study: trainer interaction and instructions were minimal and standardized and no therapeutic effects were expected in such a short term interaction, but there was still a change seen in the neural fluctuations of the theta wave in the second session of training. As such this study can be seen as a proof of concept that neurofeedback alone does induce neural changes in children with ADHD independent of the influence of therapeutic elements.
The neural changes that begin in the early stages of neurofeedback are the beginning of potential long lasting effects on neural functioning and the symptomology of ADHD.
Unfortunately long term follow-up assessments of the neurophysiological changes that are attributable to neurofeedback training are scarce (one study found that demonstrated sustained alpha increase 6 months after neurofeedback training for children with ADHD (Bazanova,
Auer, & Sapina, 2018)), but the effects on symptomology have been assessed. While there have been a number of meta-analyses analyzing the effects of neurofeedback directly after training, which have found mixed evidence of the efficacy of neurofeedback compared to
55 controls (Cortese et al., 2016; Micoulaud-Franchi et al., 2014; Riesco-Matías et al., 2019;
Sonuga-Barke et al., 2013), a meta-analysis assessing the long term effects of neurofeedback was missing from the literature until now. The importance of conducting an assessment of studies that included a follow-up time point lies in the foundation of neurofeedback as a training which operates under learning theory. As such, learning to neuroregulate can then be applied even when training is not taking place and the benefits could theoretically outlast those of medication or other cognitive trainings.
The results of the long term meta-analysis in this dissertation do indeed demonstrated that neurofeedback has long term effects regarding inattention and hyperactivity/impulsivity symptoms in ADHD. Effects for neurofeedback were superior to those of non-active controls and, partially, comparable to active controls. Additionally, inattention effects were stronger than hyperactivity/impulsivity effects. These findings maintain that neurofeedback should continue to be considered as a therapy for ADHD and that it is specifically helpful in the area of attention. This conclusion is particularly poignant in the current atmosphere of neurofeedback research since multiple recent studies have questioned the efficacy of neurofeedback, especially when compared to methylphenidate as a treatment (Gelade, Bink, et al., 2016; Gelade, Janssen, et al., 2016; Janssen et al., 2016a, 2016b). These papers highlight that neurofeedback should not be used as a stand-alone treatment, but in addition to other forms of therapy. However, while a combined approach to treating ADHD is preferable, there are cost and time limitations on how many therapies can be provided as well as situations in which medication is not an option: parents prefer to not medicate their children, child cannot tolerate side effects, child does not respond to medication. Especially in light of these limitations, the results of the meta-analysis support that neurofeedback should be considered as an effective independent treatment option.
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Despite the wide range of current neurofeedback paradigms in use, the bulk of the papers used for the meta-analysis were indeed oriented on ‘standard’ protocols (theta/beta, SCP, SMR)
(Arns, Heinrich, & Strehl, 2014). That these protocols do indeed provide a benefit of neurofeedback over non-active control conditions does support that these paradigms are beneficial to learning neuroregulation in order to alter symptoms, specifically regarding attention. However, it is still worthwhile to continue to explore which protocols may be best suited to which endophenotypes. As suggested above, these endophenotypes may be identifiable via their resting EEG patterns, but also through their active EEG signatures during neuroregulation and cognitive tasks. Based on this information, the standard protocols could be adjusted or changed to best access the specific neurophysiological subtypes.
Currently there is an attempt to create individualized neurofeedback trainings using z-score training or Low-resolution Brain Electromagnetic Tomography (LORETA) neurofeedback.
Z-score training compares an individual’s neural activity (amplitudes, power ratios or coherence) to an averaged quantitative electroencephalography (QEEG) neural signal, with the goal being to ‘normalize’ the patient’s neural activity to that of the average; LORETA neurofeedback involves using an inverse solution technique to train specific neural targets.
These methods have become very popular among commercial neurofeedback clinics and are being offered for neuro-optimization as well as to treat a wide range of clinical disorders.
While these attempts are a starting point for conducting individualized training and have been suggested to be comparable or more efficacious than standard neurofeedback trainings
(Cannon et al., 2014; Krigbaum & Wigton, 2015; Thatcher, 2013), there is very little peer reviewed scientific evidence to support that these methods are effective in clinical populations
(Coben, Hammond, & Arns, 2019). Our meta-analysis additionally found no papers regarding these trainings that had a follow-up assessment. Despite the recent boom in the use of these types of neurofeedback for therapeutic use, the fact that so little scientifically rigorous
57 information is available to support them greatly suggests that the use and efficacy of such methods should be rigorously questioned.
After the data for our meta-analysis was collected, there has been one study published that would have met our inclusion criteria (Aggensteiner et al., 2019). The study by Aggensteiner et al. (2019) found that there were no group differences between SCP neurofeedback training and Electromyogram (EMG) biofeedback training at a six month follow-up, but that the SCP group had more stable improvements over time while the EMG group had relapses in symptoms between two post testing time points (directly after training and 1 month follow- up). These results are in contrast to the findings of our meta-analysis that neurofeedback effects do persist to follow-up while non-active control effects do not, since EMG biofeedback would be classified as a non-active control condition for our study. However, the statistical power of the meta-analysis is a more powerful tool for the evaluation of the efficacy of neurofeedback compared to controls and should be considered a more reliable indicator of the specific effects of neurofeedback.
The results of the short term neurofeedback study and the meta-analysis conducted for this dissertation highlight the need to optimize neurofeedback regarding the protocols used, the types of controls and the time points of neurophysiological and symptomatic assessment;
However, there are other aspects of a clinical setting that also warrant examination. One important aspect that has been highlighted in recent research is the effect that ambient lighting has on attention, emotion and sleep; all elements that can be considered important for the success of therapy. Current research has primarily evaluated these components in response to light in school and work settings, and the bulk of research has investigated adult participants.
Our findings that blue light exposure in a clinical setting does enhance attentional abilities in healthy adolescents and that circadian effective light may help to promote better sleep quality in this age group begins to fill a gap in the literature, both regarding the age of our participants
58 and the clinical setting of the exposure. These findings support the idea that light could be used as a therapy or to optimize clinical therapeutic settings, both for attentional problems as well as sleep disturbances (which can in turn create attentional problems).
To date, light exposure has primarily been explored for adults as a therapeutic method to treat affective symptoms in depression (Sikkens, Riemersma-Van der Lek, Meesters, Schoevers, &
Haarman, 2019), dementia (Missotten et al., 2019), and Parkinson’s Disease (Fifel &
Videnovic, 2018). Bright light therapy has also started to be explored for adolescents with depression (Niederhofer & von Klitzing, 2011), and sleep disorders (Richardson et al., 2018) and a new study explores it specifically for ADHD in regard to depressive symptoms (Mayer et al., 2018). While most studies focus primarily on the effects of light on the affective symptoms of these disorders, our finding of the specific influence of the blue enriched light on improved arithmetic performance and decreases of reaction time variability and is a new contribution to the research and suggests that other aspects of these disorders could also be addressed by light therapy. The improvement seen in the arithmetic performance may reflect better attention allocation. The decreases in reaction time-variability can be considered to be a representation of a reduced neuronal rate variability that indicates a more stable attentional state (Thiele & Bellgrove, 2018). Based on this interpretation, improvements of this parameter due to light exposition may be of specific relevance to populations that show attentional deficits, such as ADHD. Light therapy has been investigated in one study regarding adult
ADHD, in which one hour morning exposure to 10,000 lx at home resulted in improvements in core ADHD symptoms, attentional measures, and general mood (Rybak, McNeely,
Mackenzie, Jain, & Levitan, 2006). To date there has been no comparable study conducted regarding childhood ADHD. Our findings that attentional parameters are improved based on clinical light exposition in healthy adolescents are the first to suggest that light therapy may also be suitable to treat attentional deficits in adolescents with ADHD.
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The fact that short term (< 1 hour) light exposure already affected attentional parameters in this study also suggests that light could be used as a supportive factor during cognitive or behavioral therapy sessions. The stabilized attentional state induced by the exposure to blue light then could result in better focus during therapy and in turn produce a better outcome.
This concept has already been applied in a classroom setting (Keis et al., 2014) regarding improvements in academic performance, but needs to be expanded to the clinical setting. Blue light is also thought to immediately activate the limbic system and stimulate an affective response (Minguillon, Lopez-Gordo, Renedo-Criado, Sanchez-Carrion, & Pelayo, 2017;
Vandewalle et al., 2010). In adults participants with seasonal affective disorder it has been found that short term light exposure (20 minutes) improves mood indices (Virk, Reeves,
Rosenthal, Sher, & Postolache, 2009). Based on these findings, light could potentially be used to bolster mood in children in the beginning of a therapy session. However this assumption needs to be cautiously evaluated, since one study has found a negative effect of blue lighting on emotion in a clinical setting (Han & Lee, 2017). Additionally, it has been found that 30 minute blue light exposure improved response speed in a working memory task and continued to affect dorsolateral and ventrolateral prefrontal cortex function and even after the light was removed (Alkozei et al., 2016). In a study with mice it has been found that exposure to brief pulses of light (30 minutes) prior to learning resulted in better memory consolidation (Shan et al., 2015). Based on these findings and our results of changes in attentional functioning after blue light exposure, it may be effective to use blue light as a medium to “prime” the attentional system for therapeutic intervention. These factors could be specifically helpful in a neurofeedback therapy session: blue light could be used during setting of electrodes to prime the attentional system to learn neuroregulation, or during training to support focus on the neurofeedback task.
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While the emphasis of this dissertation is the manipulation of attention directly, the impact that sleep has on attention cannot be ignored. It has been found that the symptoms of ADHD vs sleep impairment are difficult to separate and mistakes in diagnosis between the two have been considered to be a potential problem (Hvolby, 2015). Not only does lack of sleep cause symptoms that mimic those of ADHD, but sleep disturbance is also a characteristic of ADHD
(Cassoff, Wiebe, & Gruber, 2012). Our finding that circadian effective light possibly supported adolescent’s sleep patterns is indicative that the timing of light exposure in a clinical setting needs to be considered. In this context a therapy session using blue light to support attentive function would have to be conducted in the morning, in order to not interfere with natural circadian rhythms and maximize therapeutic effects.
Specifically regarding disturbed sleep in ADHD, neurofeedback and light exposition could be combined to provide maximal therapeutic benefit. Neurofeedback has already been used to target sleep disorder in ADHD, resulting in beneficial effects on ADHD symptoms (Arns,
Feddema, & Kenemans, 2014; Arns & Kenemans, 2014). While there has not yet been a study investigating light exposure effects on sleep in children with ADHD, the sleep supporting effects of our light study in healthy adolescents suggests that light exposition could possibly support the neurofeedback effects. One complication of this idea is that, if it is only red light that supports sleep, neurofeedback would have to be trained at night and the activating effects that have been demonstrated for blue light exposure may be lost. However in the design of the light study involved exposure to blue circadian effective light in the morning combined with red circadian effective light in the evening, which does not allow the assumption that the effects are only due to the evening light exposure. It has been seen that blue light at circadian effective times does support better sleep (Figueiro et al., 2017), and even be protective against circadian ineffective light exposure later in the day (Münch et al., 2016). As such, a blue light exposition early in the day combined with a neurofeedback task could have a two-fold effect:
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1. It could help with the immediate attention allocated to the neurofeedback task and 2. It could facilitate better sleep for the participants which would in turn impact their attentional abilities.
4.3 Limitations and future research
A limitation of the short term neurofeedback study was that the design ended up being too long for the children. This resulted in an inability to evaluate tests conducted in the second half of the experiment, including neurocognitive attention parameters (CPT) and resting EEG changes. It is still unclear how fast neurophysiological change impacts neurocognitive functioning, a design in which these effects could be compared is still needed. The lack of information regarding resting EEG limits the comparability of our study to others that have included this parameter. Changes in resting EEG parameters have already been demonstrated in adults after a single neurofeedback session, however similar data has not yet been collected for children and needs to be included in future studies. Additionally, the hypothesis that there may be active vs resting EEG endophenotypes for ADHD needs to be further evaluated in a study that is able to analyze both of these neural states.
An often criticized aspect of neurofeedback is that its efficacy may be a result of non-specific or placebo effects resulting for the one-on-one nature of the setting. We attempted to address this issue in the short term neurofeedback experiment by eliminating any therapeutic elements, however the sessions were still conducted in a one-on-one stetting and a definitive answer to whether or not the found neural effects were a result of the setting cannot be answered. Placebo effects have recently been suggested to actually be counter effective to neurofeedback results (Kober, Witte, Grinschgl, Neuper, & Wood, 2018), but non-specific effects may still be present. Future studies should try to evaluate whether setting type (one-on- one, group, no trainer present) may alter the results. These concerns about the source of neurofeedback effects should also be considered for the meta-analysis, even though our
62 inclusion of randomized controlled trials as well as the use of meta-analytical techniques are meant to minimize the impact of placebo and non-specific effects. However, the control of these effects, even in a laboratory setting, remains incredibly difficult. This is because trying to create a double-blind- placebo controlled study for a neurofeedback setting, as suggested by good clinical practice in pharmaceutical studies, results in practical and ethical issues (La
Vaque & Rossiter, 2001; Lansbergen, van Dongen-Boomsma, Buitelaar, & Slaats-Willemse,
2011). In attempting to create such a study, the available research has included a wide range of controls, from waiting list to medication, which could potentially compromise the quality of the results of the meta-analysis. We attempted to address this by separately evaluating active and non-active controls, but future research should emphasize the use of standardized controls for neurofeedback to better understand its true effects.
The results of the meta-analysis, while very promising, do need to be interpreted with caution due to the relatively low number of papers that were able to be included in the analysis, as well as the naturalistic nature of the follow-up designs. Since the compilation of our data there has been one additional study published that meets our inclusion criteria and hopefully more will be conducted in the next few years. This meta-analysis should be updated to include recent studies in order to reflect the most recent advances in the field and to improve its statistical power. Regarding the naturalistic follow-up designs of most studies, there are concerns regarding the medication use from post- to follow-up time points that may skew the results. A change in medication was allowed in two studies, but for both the neurofeedback and control groups; this naturalistic follow-up approach assumes that the same medication effects should be present in both groups and as such offset each other without skewing the data. Additionally, when considering dose change, there was actually larger increases in medication dose for the already medicated controls, which may result in larger effects in the active control group than if medication were kept constant.
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When evaluating the light study, two limitations are at the forefront: 1. The change of evening session time in the middle of the study; 2. Not providing the light exposition in the same location the participants slept. The changing of the evening session time does somewhat limit the results since a change in protocol may have caused non-specific effects. Since the study had such a small number of participants any non-specific effects could influence the findings.
Additionally, not providing light exposition in the same location in which the participants slept limits the control that could be had over influencing factors that may have been present between light exposition and bedtime, despite the specific instructions given regarding behavior in the time period (light exposure, exercise, food/drink). Future studies should strive to have a uniform protocol for all participants and conduct light exposure in the same place the participant will sleep.
Another factor that could not be assessed in our light study is whether sleep was affected by the blue enriched light in the morning, or indeed the red enriched light in the evening. Since both light expositions were included on the same day, additional research is needed to determine if one or the other has a stronger influence on sleep or attention. This is especially relevant considering recent research that found daytime exposure was more important than evening exposure in predicting sleep quality (Münch et al., 2016). Additionally the high luminosity of both light conditions may have had an alerting effect, regardless of the light profile. While sleep is thought to be more effected by wavelength than luminosity, a future study should be designed to be allow to isolate these factors. Additionally, we did not measure the photon density of the light in this study, but rather the luminescence. These aspects of light activate different processing pathways (visual vs non-visual system) and both measurements should be included to better understand which qualities of the light presented influenced which system, in order to better understand how attention is being enhanced.
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A limitation of all studies is the lack of comprehensive neurocognitive and neurophysiological measurements. Regarding the meta-analysis we did intend to address these aspects, however there were too few studies that met our inclusion criteria and had these elements. For the short-term neurofeedback study we did include neurocognitive tests (CPT) however the length of our study was too taxing for the children and this parameter could not be evaluated. The light study was considered a pilot study and, in order to minimize the time demands of the participants, neurophysiological measures were not included. In the future it would be beneficial for our understanding the effects found here to be also assessable from other aspects, and all studies should ideally contain behavioral, neurocognitive and neurophysiological measurements.
4.4 Conclusion
The results of this dissertation support previous research that attention can be modified in a clinical setting. The neurofeedback research has supported that neurofeedback does change neuronal activity even after only two training sessions and that it should continue to be considered an effective therapeutic option for ADHD, specifically regarding inattention symptoms. However, neurofeedback and other forms of therapy should continue to be optimized, with a few options being the selection of neurofeedback protocols dependent on the endophenotypes of ADHD being treated and the use of ambient light before or during therapy sessions.
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6. Acknowledgements
I would first like to thank Prof. Dr. Berking for being my advisor for this dissertation and supporting my endeavor to obtain a doctorate in Psychology, despite my previous degrees being in Neuroscience.
Without his support this dissertation would not have been possible. In addition I would like to thank
Prof. Dr. Rohleder for taking the time to be my second reviewer and Prof. Dr. Kratz for being my third examiner for my defense.
In the Department of Child and Adolescent Mental Health, there are many people who have contributed to this work. I would like to thank Prof. Dr. Moll, Prof. Dr. Heinrich and Prof. Dr. Kratz for allowing me to work in their department, both in a scientific and therapeutic roll. This opportunity is one that I will always be grateful for. Thank you to Prof. Dr. Heinrich for working with me so closely as a supervisor and helping me to better understand neurofeedback and develop my scientific skillset. Thank you to Prof. Dr. Krazt for always supporting the Neurofeedback Program. A special thanks to Dr. Petra Studer and Dr. Anna Eichler for always supporting me, reviewing my work, and giving me advice on how to proceed. Thank you to Anne-Christine Plank for her assistance with my
Zusammenfassung. A huge thank you to Valeska Stonawski for helping me to find motivation when I was sure there was none to be found and for listening and providing advice when conflicts occurred.
Thank you to Regine Czech and Georg Gremer for sharing their Neurofeedback Therapy experience and helping me understand neurofeedback from another viewpoint. Additionally thank you to all of the families and participants that volunteered to be a part of these projects.
I thank my family and friends for listening to me as I told about my progress in this project and for bolstering me up when I was not sure if I should continue. A special thank you to Bojana Jankovic who took the time to read my first draft and whose comments were immensely helpful. Thank you to my parents who raised me to believe I could do anything that I set my mind to. Thank you to my siblings who have dubbed me a brain wizard and have always been supportive of my endeavors.
Finally, thank you to my husband Lukas. He has provided me with endless love and support so that I could find the motivation to continue, despite any and all obstacles that have crossed in my path.
Without him, I have no doubt I would have never been able to finish this dissertation. 84