1 This thesis has been approved by

The Honors Tutorial College and the Department of Biological Sciences

______

Dr. Julie Suhr

Professor, Psychology

Thesis Adviser

______

Dr. Janet Duerr

Director of Studies, Neuroscience

______

Dr. Donal Skinner

2 EXPECTANCY EFFECTS IN ADHD TREATMENT RESEARCH

______

A Thesis

Presented to

The Honors Tutorial College

Ohio University

______

In Partial Fulfillment of the Requirements for Graduation from the Honors Tutorial College with the degree of

Bachelor of Science in Neuroscience

______by

Noelle C. Stroud

June 2021

3 Contents

Introduction

I. Attention Deficit/Hyperactivity Disorder 5

II. Treatment of ADHD 8

III. Expectancies as a Factor in Treatment

A. Analgesics 15

B. Antidepressants 16

C. Treatments for Parkinson’s Disease 18

IV. Implications for Research Design 20

V. Expectancies in ADHD 22

VI. Present Study Aims 24

Study 1

I. Methods 25

II. Results 26

Study 2

I. Selecting the Archival Dataset 27

II. Participants 30

III. Measures 30

IV. Methods 31

V. Statistical Analysis 32

VI. Results

A. Descriptive Statistics 33

B. Auditory Consonant Trigram Test 35

4 C. AX Continuous Performance Test 38

D. State Arousal & Attention Scale - Total 39

E. State Arousal & Attention Scale - Attention 39

Discussion 41

References 47

Appendix 60

5 INTRODUCTION

I. ATTENTION DEFICIT/HYPERACTIVITY DISORDER

Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders, with about 8% of people under 18 in the United States having ever received a diagnosis (Danielson et al., 2018). ADHD is characterized by differences in attention, memory, impulse control, distractibility, and other related functions that cause functional impairment across settings. A diagnosis of ADHD requires that the patient experienced six or more inattentive and hyperactive/impulsive symptoms, beginning in childhood and persisting for at least six months. Table 1 shows the symptoms listed by the Diagnostic and Statistical Manual of Mental

Disorders-5 (American Psychiatric Association, 2013) for the hyperactivity/impulsivity and inattention categories.

Table 1

Diagnostic and Statistical Manual of Mental Disorders-5: Diagnostic Criteria for Attention-

Deficit/Hyperactivity Disorder

Hyperactivity/Impulsivity Inattention

● Fidgeting or squirming ● Careless mistakes or difficulty with ● Restlessness, difficulty remaining attention to detail seated ● Difficulty holding attention to tasks ● Running or restlessness in ● Inattention to conversations or spoken inappropriate situations instructions ● Disruptiveness, loud speaking voice ● Difficulty carrying tasks through to ● Acting “as if driven by a motor” completion ● Excessive talking ● Organizational difficulties ● Interrupting or cutting people off mid ● Dislike or reluctance of tasks requiring sentence sustained mental effort ● Difficulty taking turns ● Frequent misplacement or loss of ● Interrupting or intruding socially objects ● Distractibility ● Forgetfulness Note. Table modified from American Psychiatric Association, 2013.

6 As of 2016, 62% of children diagnosed with ADHD in the US use pharmacological interventions to manage symptoms, 47% use nonpharmacological therapies, and 23% use neither

(Danielson et al., 2018). In some cases, symptoms of ADHD remit over time as the patient matures, but in about one third of cases the symptoms remain at functionally impairing levels into adulthood

(Barbaresi et al., 2013). The severity of the symptoms can range from manageable to very impairing (Center for Disease Control, 2020).

The neural basis of ADHD is not thoroughly understood, but it is thought to be related to atypical functional connectivity between the ventromedial prefrontal cortex and the striatum

(stronger than average) and amygdala (weaker than average) (Rosch, Mostofsky, & Nebel, 2018).

Studies have also found relatively decreased volume of the nucleus accumbens, amygdala, hippocampus, caudate nucleus, and putamen in individuals with ADHD compared to individuals without ADHD, with small effect sizes (d = -.10 to d = -.19) (Hoogman et al., 2017; Makris et al.,

2015; Vieira de Melo et al., 2017). The areas most affected in ADHD are related to one another via the fronto-parieto-striato-cerebellar network (see Figure 1), which together regulates attention, executive function, cognitive processing speed, decision making, emotional regulation, and impulse control (Purper-Ouakil et al., 2011). Altered functional connectivity within these networks, which are largely dopaminergic and noradrenergic, has been shown to be associated with ADHD symptoms in fMRI and diffusion tensor imaging studies (Purper-Ouakil et al., 2011;

Rosch, Mostofsky, & Nebel, 2018). Motor inhibition and control symptoms of ADHD have been associated with differences in the parieto-insular-caudal networks, particularly decreased activation of the superior, middle, and inferior frontal gyri, the prefrontal gyrus, and the insula (Lei et al., 2015).

7 Figure 1

Functional networks affected in Attention-Deficit/Hyperactivity Disorder

Note. Figure from Purper-Ouakil et al., 2011.

Both genetic and environmental factors are believed to impact the development of ADHD.

ADHD is highly heritable, with family, twin, and adoption studies showing about 74% heritability.

However, the genetic basis of ADHD has not been attributed to any individual gene or mutation, but rather to a range of genetic differences that each contribute a small effect to the overall ADHD phenotype. Candidate genes include serotonin (HT) transporter gene 5HTT, dopamine (DA) transporter genes DAT1 and SLC6A3, DA receptor genes DRD4 and DRD5, HT receptor gene

HTR1B, brain angiogenesis regulator gene BAIAP2 and synaptic vesicle regulating gene SNAP25

(Faraone & Larsson, 2019). Bralten et al. (2013) found significant relationships between parent- reported ADHD symptoms and single nucleotide polymorphisms (SNP) in genes related to neuritic outgrowth of the dopamine, norepinephrine, and serotonin pathways. While no particular SNP was

8 individually significantly related to ADHD symptoms, each of the three genetic pathways was significantly related to hyperactive symptoms in particular (Bralten et al., 2013). Genetic associations have also been found between ADHD and decreased total intracranial volume and increased volume of the nucleus accumbens and caudate (Klein et al., 2019).

II. TREATMENT OF ADHD

Pharmacological and behavioral therapies are the most common interventions for ADHD, and both are effective, with small to moderate effect sizes, in adults as well as children (Cunill et al., 2015; Daley et al., 2014; Knouse et al., 2017; Maneeton et al., 2015). Methylphenidate (MPH), also known as Concerta or Ritalin, increases the firing rate of neurons in the frontal cortex by increasing the availability of dopamine (DA) and norepinephrine (NE) in the synaptic cleft. This is achieved through the inhibition of dopamine and norepinephrine transporters DAT and NET by two distinct mechanisms (see Figure 2a), which allows DA and NE to remain in the synaptic cleft after release and activate dopaminergic and noradrenergic receptors, increasing network activation

(Faraone, 2018). With regard to effectiveness in treatment of ADHD, MPH increases activity in the bilateral inferior frontal cortex and insula during inhibition and time discrimination tasks but did not have a significant effect on systems related to working memory (Rubia et al., 2014).

Amphetamines such as Adderall are also stimulants, but work by a slightly different mechanism. Amphetamines can reverse or inhibit DA transporters, resulting in either the outward flow of DA into the synapse or reuptake inhibition (see Figure 2b). They also inhibit NE transporters and monoamine oxidase, which is responsible for the breakdown of catecholamines such as DA and NE, as well as augment vesicular release of DA (Avelar et al., 2013).

Amphetamines act primarily upon DATs, which are heavily concentrated in the striatum, caudate nucleus and parietal and frontal cortices (Faraone, 2018).

9 Figure 2

Mechanisms of action of (a) methylphenidate (MPH) and (b) amphetamine (AMF).

Note. Figure from Hodgkins et al., 2012

A variety of non-stimulant medications are used to treat ADHD symptoms as well.

Atomoxetine, also known as Strattera, inhibits the norepinephrine transporters primarily concentrated in the prefrontal cortex, increasing local extracellular dopamine and norepinephrine

(Sauer et al., 2005). Bupropion, an antidepressant better known as Wellbutrin, is a nicotinic acetylcholine receptor antagonist and a norepinephrine and dopamine reuptake inhibitor that primarily affects the prefrontal cortex (Stahl et al., 2004).

Overall, these medications are known to be effective at reducing certain symptoms of

10 ADHD for some patients. Pievsky & McGrath (2018) found in their meta-analysis that methylphenidate has a significant normalizing effect on ADHD symptoms in adults in the following cognitive areas associated with frontal lobe functioning, as compared to placebo, with relatively small but clinically significant effect sizes: working memory (p = .05), reaction time variability (p = .02), vigilance (p < .01), response inhibition (p < .01), and driving skills (p < .01).

Lisdexamfetamine, an amphetamine drug, has been found to be effective compared to placebo at reducing the severity of ADHD symptoms in children (Maneeton et al., 2015). In 2016, Cunill et al. found that stimulant medications, such as methylphenidate and amphetamines, were more effective than non-stimulant drugs, such as atomoxetine and bupropion, on ADHD symptom control in adults, but also that stimulant medications were associated with slightly higher discontinuation rates across studies. They found significantly higher symptom improvement in active treatment groups than in placebo groups across studies (p < 0.00001) with low to moderate effect sizes (Cunill et al., 2016).

Non-pharmacological interventions for ADHD include a wide range of behavioral, cognitive, and social therapies, accommodations at work or school, or social and organizational skills training, and neurofeedback (Danielson et al., 2018). Isolating the effects of non- pharmacological interventions can sometimes be difficult due to the difficulty of designing an adequate placebo condition. Daley et al. (2014) found in their review that behavioral interventions were associated with significant improvements in parent-child interactions, conduct, social skills, and academic performance. A review of cognitive-behavioral therapies in ADHD adults found medium to large effect sizes for self-reported symptoms that decreased in studies that included an attention-matched control group rather than a waitlist control group (Knouse et al., 2017).

Neurofeedback, a non-pharmacological therapy in which the patient’s symptom-specific neural

11 biomarkers are monitored via EEG or fMRI and used to control some computer simulation to encourage greater executive control, has also been shown to improve self-reported ADHD symptoms (Holtmann et al., 2014). Using neurofeedback to train self-regulation of attention is thought to help alleviate ADHD symptoms by taking advantage of neuroplasticity through operant conditioning to normalize pathological brain activity (Enriquez-Geppert et al., 2019). Specifically, functional connectivity fMRI analyses showed a significant positive relationship between activity in the right inferior frontal cortex, anterior cingulate cortex, the dorsal caudate and effectiveness of neurofeedback therapy for ADHD. These areas are important in inhibition, attention, working memory, and time estimation, and have been associated with ADHD symptoms (Rubia et al.,

2019). Lam et al. (2020) found that real time feedback regarding blood oxygen-level dependent

(BOLD) response in the cingulate cortex and frontal cortex improves self-regulation and inhibition through increased activation of the left inferior fronto-insular-striatal network and decreased activation of posterior temporal-occipital-cerebellar networks. Altogether, there is evidence that neurofeedback can modulate the improvement of ADHD-related symptoms and functional connectivity abnormalities (Holtmann et al, 2014).

III. EXPECTANCIES AS A FACTOR IN TREATMENT

Although both medications and neurofeedback appear to have a neuroscientific bases for their effect on ADHD, other mechanisms may also contribute to real world outcomes. In the real world, a patient may not care whether an improvement in symptoms is due to the direct biological mechanism of an intervention or from other effects like chance or placebo effect. However, the difference between drug and non-drug effects becomes relevant if the patient encounters negative side effects, sensitivity or tolerance to the drug, or high drug/treatment prices. If the substance itself is not contributing enough to symptom control to justify the negative effects of the drug, then

12 it may be in the patient’s best interest to try different interventions. Researchers also care how the mechanism of a biologically based treatment is related to the condition it targets. If a scientist uses a treatment’s mechanism as their starting point to determine the cause of a disease when in fact the treatment mostly works because of other mechanisms, research will be led astray.

A variety of factors can impact the outcome of a treatment that are not related to the efficacy of the treatment itself. A patient can experience spontaneous symptom changes, habituation to the treatment, observation effects, and effects from unidentified co-interventions

(Benedetti, 2008). One of these factors is expectancy effects: the effects brought about the patient’s beliefs regarding the intervention. While psychologists have named a variety of subtypes of expectancies, some of the most prominent and recurring subtypes are self-efficacy expectancies, behavior-outcome expectancies, and stimulus-outcome expectancies (see Figure 3).

Self-efficacy expectancy refers to belief in one’s ability to successfully perform a behavior.

For example, an experienced driver would have higher self-efficacy expectancy before embarking on a long road trip than a newly licensed driver. This type of expectancy is not discussed as often in placebo effect research, but for a non-pharmacological treatment, like neurofeedback or cognitive therapy, the patient’s belief in their ability to participate in the treatment effectively may have an impact on the outcome. Behavior-outcome expectancy refers to the belief that a behavior is causally linked to some outcome. For example, after watching someone undergo physical therapy and seeing them regain the ability to play a sport, one would form a belief that physical therapy leads to improved mobility. Stimulus-outcome expectancy is similar, but the causal event is external rather than behavior-based. For example, one might observe that people who are drinking coffee do not fall asleep in class, so one would form a belief that coffee leads to wakefulness. Both outcome expectancies are relevant for placebo research. A positive behavior-

13 outcome expectancy could improve the results of behavior-based treatments, and a positive stimulus-outcome expectancy could improve the results of pharmacological treatments (Kirsch &

Maddux, 1999).

Figure 3

Expectancies and Subtypes

Placebo researcher Irving Kirsch breaks stimulus-outcome expectancies into two subcategories: stimulus expectancies and response expectancies (see Figure 3) (Kirsch, 2011).

Stimulus expectancies are beliefs concerning the nature of the stimulus, whereas response expectancies are beliefs concerning involuntary outcomes, or responses, to the stimulus. For example, when someone drinks a cup of coffee, they may have a stimulus expectancy that they are ingesting caffeine and a response expectancy that the caffeine will make them feel more alert. It is clear how these two subcategories interact: people with the same response expectancy for caffeine

(that it will make them feel more alert) will experience different expectancy effects depending on their stimulus expectancy (whether they believe the coffee they drank was caffeinated or not).

Kirsch identifies response expectancies as the primary causal agents of the placebo effect (Kirsch,

2011).

The role of the response expectancy in the placebo effect is made clearer by Kirsch’s distinction between drug response, drug effect, placebo response, and placebo effect. The drug

14 response is the collection of outcomes that are caused by taking the drug. This includes but is distinct from the drug effect, which only encompasses the outcomes of the drug that are directly related to its pharmacological properties. For example, someone taking a puff of an asthma inhaler may experience relief from the corticosteroid and bronchodilator (the drug effect), but that relief is magnified by the practice of breathing in deeply and slowly when using the inhaler, the belief that the inhaler is dispensing medicine (the stimulus expectancy), the belief that the medicine will help (the response expectancy), and the natural remission of the asthma attack (all included in the drug response). Similarly, the placebo response is the collection of outcomes caused by taking the placebo, and the placebo effect describes only the difference between the placebo response and natural course of the ailment with no treatment. Thus, the drug effect is the difference between the drug response and the placebo response. For example, if someone were to use a sham asthma inhaler, they would experience all of the benefits of using a real inhaler except for whatever the corticosteroid and bronchodilator directly contribute (Kirsch, 2011). See Table 2.

Kirsch’s breakdown of responses and effects shows where a response expectancy becomes significant for researchers. The response expectancy would theoretically explain the differences between the response and effect for both the drug and the placebo (Kirsch, 2011). This helps researchers isolate the effects of the drugs they study more precisely.

Expectancy effects partially explain how placebos can be effective. For example, if someone takes a placebo painkiller, any analgesia they experience could be explained by two types of expectancies: a stimulus expectancy that the pill is a pharmacologically active analgesic and a response expectancy that it will relieve their pain. Response expectancies have been specifically identified to play a role in drug responses for a number of phenomena. I will focus on three examples: analgesics, antidepressants, and treatments for Parkinson’s disease.

15 Table 2

Breakdown of Placebo and Drug Effects

Drug Placebo

Response Every outcome of taking a Every outcome of taking a drug (placebo response + drug placebo effect)

Effect Outcomes related to the Outcomes related to the pharmacological properties of properties of the placebo the drug (drug response - substance (placebo placebo response) response - baseline)

A. ANALGESICS

One of the most well-studied placebo effects is that of placebo analgesics, drugs for pain reduction. In 1973, Laska and Sunshine found that the effect size of placebo analgesia depended on the patient’s experience with the analogous analgesic. This finding could be interpreted two ways: the placebo effect of an analgesic could be caused by unconscious classical conditioning of pain relief (the unconditioned stimulus) with the analgesic (the conditioned stimulus), or it could be caused by the development of a conscious response expectancy for the analgesic.

Further research has shown that classical conditioning alone is insufficient to explain the observed placebo effects, but that expectancies are necessary mediating factors (Montgomery &

Kirsch, 1997). For example, a study by Colloca and Benedetti (2006) found that when a patient receives a painful stimulus and a placebo analgesic followed by a decrease in pain stimulus intensity, the patient will report a decrease in pain upon the future administration of the placebo.

This suggests that successful prior use of a perceived analgesic, regardless of its analgesic properties, will create an analgesic response expectancy that significantly decreases the patient’s pain even in the complete absence of an analgesic.

16 Analgesic expectancies have one of the best understood mechanisms of placebo action.

There is substantial evidence that the expectation of pain relief causes the release of endogenous opioids and that opioid antagonist naloxone blocks the effect of placebo analgesics (reviewed in

Benedetti, 2008). In 2007, Wager et al. found that administration of a placebo analgesic caused an increase in activity of opioid receptor dense brain regions, including the periaqueductal gray, amygdala, and orbitofrontal cortex. The dorsolateral prefrontal cortex, which is associated with anticipation of pain and evaluation of allocation of cognitive control, is also particularly active during significant analgesic placebo responses (Atlas & Wager, 2014; Miller & Cohen, 2001).

Thus, analgesic expectancies and the analgesic placebo effect can be tied to brain activity in opioid-dense regions and in frontal systems involved in prediction and anticipation.

B. ANTIDEPRESSANTS

In his meta-analysis of placebo-controlled antidepressant studies with Sapirstein (1998),

Kirsch compared 38 studies with over 3000 participants in three treatment groups: a group that received antidepressants, a group that received placebo, and a group that received no treatment.

Their analysis aimed to tease apart drug response, drug effect, placebo response, and placebo effect by comparing the improvement of depression symptoms in each of these groups. They found significant differences in improvement between the no treatment and the placebo group, suggesting that the act of taking a pill and believing that it will help has a significant effect not attributable to regression towards the mean or spontaneous remission. Surprisingly, however, there was only a small difference between the improvement of placebo groups and active treatment groups. Even when controlling for the type of antidepressant taken, they found that

75% of symptom improvement from taking antidepressants could also be achieved by taking a placebo. This indicates that the improvement was not spontaneous, but also that the

17 antidepressant effects of the placebo were twice as large as the antidepressant effects of the drug when placebo effect was subtracted. One of their hypotheses for the seemingly non-specific effect of the wide variety of drugs on depression was that they acted as active placebos. An active placebo, sometimes called an impure placebo, is a drug that is pharmacologically active, but with a mechanism of action unrelated to the pathology being treated. Active placebos could affect the patient’s response expectancy because the presence of side effects may lead them to believe that the drug is working. Alternatively, if they believe from the beginning that the drug will be potent and effective, they may misattribute any perceived side effects and symptom improvements to the drug rather than to chance or other causes (Kirsch, 2011).

Brown and Peciña (2019) reviewed the neuroimaging evidence for the neural basis of the antidepressant placebo effect. Quantitative electroencephalogram (QEEG) analyses found that placebo responders in a randomized control trial (RCT) showed a significant increase in prefrontal cordance, a QEEG measure describing both absolute activation and activation compared to other brain areas. For drug responders, prefrontal cordance initially decreased, but recovered and increased later in the treatment course to eventually match the placebo group.

Both groups showed similar improvements in symptoms, but the drug responders’ cordance matched the traditionally delayed action of antidepressants. Perhaps the earlier increase seen in placebo responders partially explains the benefits some patients experience before the antidepressant reaches its full potency after a few weeks. Positron emission tomography (PET) analyses in a different RCT showed increases in metabolic activity of the prefrontal, anterior cingulate, premotor, and parietal cortices, the posterior insula, and the posterior cingulate in the placebo responder group from pre to post treatment (see Figure 4). They also showed decreases in activity in the subgenual anterior cingulate cortex, parahippocampus, and thalamus. These

18 were similar to the drug responder group, except the drug responders also showed increases in activity in the brainstem, striatum, anterior insula, and hippocampus (Brown & Peciña, 2019).

The similarity between changes in placebo responders and drug responders in these two studies demonstrates the remarkable ability of a placebo to elicit certain drug-like effects. This helps explain why some antidepressant users may experience symptom relief long before the drug is expected to have a noticeable effect: expectancy-induced placebo effects may cause noticeable symptom changes before the drug starts to act.

Figure 4.

Neural correlates of the antidepressant placebo. PET scans show

μ-opioid (top) and dopamine (bottom) induced neurotransmission

Note. Figure from Brown & Peciña, 2019.

C. TREATMENTS FOR PARKINSON’S DISEASE

Placebo effects for Parkinson’s Disease (PD) have been identified in levodopa drug trials and in placebo-controlled surgical trials (Benedetti, 2008). In Pollo et al. (2002), deep-brain stimulation devices were implanted into the subthalamic nuclei of patients (n = 7) with PD and

19 were either turned on or off. The patients were told to expect either that the deep-brain stimulation would improve the speed of hand movements or that it would not help. Patients receiving the same deep-brain stimulation differed significantly in the velocity of their hand movements based only on the result they were told to expect. These effects were very fast acting, measured only minutes after the stimulation of the subthalamic nuclei (Pollo et al., 2002). An RCT on the effects of human embryonic dopaminergic neuron transplant in patients with PD found that the patients who believed they had received real surgery experienced significantly larger improvements in quality of life than those who thought they had received placebo surgery, regardless of the surgery they really received (McRae et al., 2004). In a similar study, patients with PD (n = 23) underwent functional magnetic resonance imaging (fMRI) analysis at 0, 6, and 12 months after a real or sham neurosurgery. Motor improvements were similar between the groups, and fMRI showed that responders to both real and sham surgery showed significantly higher activity in the cerebello- limbic circuit at 6 months than they did at baseline. Interestingly, when the groups were unblinded, this activity persisted in the real surgery group, whereas it decreased in the sham group (Ko et al.

2014).

One explanation for the expectancy effects seen in PD treatment is a dopaminergic mechanism of placebo action. PD is characterized by the death of dopaminergic neurons and a buildup of Lewy bodies, especially in the substantia nigra pars reticulata of the striatum. Motor improvement as a result of placebo in PD is correlated with an increase of dopamine release in the striatum, suggesting that positive response expectancies are causally related to the release of dopamine in an otherwise dopamine-deficient system. Interestingly, dopamine release in the ventral striatum specifically is correlated with positive expectancy, whereas dopamine release in the dorsal striatum is correlated with motor improvement (Benedetti, 2008). These hypotheses are

20 consistent with the brain areas implicated in the improvements seen by Ko et al. (2014). The exact mechanism relating placebo administration to dopamine release is not yet known.

IV. IMPLICATIONS FOR RESEARCH DESIGN

Thus, studies assessing the efficacy of interventions should be designed to separate drug effects and non-drug effects. Researchers try to control for these factors with a placebo group in a randomized double-blind placebo-controlled trial or RCT (see Table 3). These studies usually aim to determine whether the proposed treatment alleviates symptoms significantly better than the placebo. To isolate the placebo effect, the study would have to include a third group that received no treatment (Benedetti, 2008). However, because leaving study participants untreated can be unethical or unimportant to the research questions, many studies compare the proposed novel intervention to the current standard of treatment. If the researchers measured the participant’s group assignment belief at all, they typically only report whether they correctly guessed their group assignment, not the effect that belief may have had as its own variable.

Table 3

The Randomized Double-Blind Placebo Controlled Design

Group Outcome measured

Active treatment Drug response

Placebo Placebo response

No treatment (occasionally included) Baseline

While these designs meet the goal of determining whether a treatment effect is stronger than placebo, they do not help determine the actual effect of the placebo itself. Further, expectancy effects could operate within the active treatment as well as the placebo group. Even if a study is double-blind in design, patients’ beliefs about whether they are in the placebo or active treatment

21 group may affect their response. If a patient in an RCT expects the active drug to cause side effects, they may notice a lack of side effects and believe that they are in the placebo group, which could affect their response to the treatment. This can also work in the opposite causal direction: if they believe from the beginning that they are in the placebo group, they may misattribute or ignore any side effects that they may experience.

An alternative to the RCT study design that better isolates expectancy effects is the balanced-placebo design or BPD (see Table 4) (Benedetti, 2008). In this design, each patient is given either the active treatment or a placebo and is told that it is definitely one or the other, whereas in an RCT they are not told which group assignment to expect. The group that they are told they are in may or may not match the group they are truly in.

Table 4

The Balanced Placebo Design

Group Group assignment belief Outcome measured

Active treatment Active treatment Treatment response

Active treatment Placebo Treatment effect

Placebo Active treatment Placebo response

Placebo Placebo Placebo effect

No treatment (occasionally No treatment Baseline included)

These two variables, group assignment and patient’s perceived group assignment, generate four experimental conditions: 1. Given active treatment, and told they got active treatment; 2.

Given active treatment, told they got placebo; 3. Given placebo, told they got active treatment; and

4. Given placebo, told they got placebo. Group 1 shows the combined effect of the treatment and positive expectancies, group 2 shows the treatment effect, group 3 shows the placebo effect with

22 positive expectancy effects, and group 4 shows the placebo effect. The BPD produces more clinically relevant results than those of an RCT because it allows for the analysis of each variable separately or of their interaction, which makes the individual effects of the treatment itself and expectancies more evident. However, the design is not as common in research due to the intellectual priorities of the research institution, ethical considerations, or practical constraints. The nature of the condition being studied affects whether it is acceptable to withhold treatment for the purposes of a study.

V. EXPECTANCIES IN ADHD

As in pain, depression, and Parkinson’s disease, many studies have shown that statistically significant expectancy effects exist for a number of ADHD treatments. The size of the placebo effects for these treatments, however, is not well understood. Psychostimulants have also been associated with placebo effects. Studies assessing the effects of illicit psychostimulant use by college students have found that the effects experienced by students can be largely mimicked by the administration of a placebo when the student believes it is a stimulant. In 2017, Lookatch,

Fivecoat, and Moore evaluated college students’ psychostimulant response expectancies before performing an RCT. Each participant chose the stimulant they were most familiar with and was given either that drug or a placebo. There were few differences between how the participants performed on psychological and academic tasks afterward, but participants with more positive response expectancies reported more improved subjective attention and performance (Lookatch,

Fivecoat, & Moore, 2017). A similar study by Cropsey et al. (2017) used a within-patient BPD, meaning that each patient received a placebo and the active drug twice and were told accurately what they were taking for half of the trials. The only effect of the substance actually consumed, placebo or stimulant, was on short term memory. Alternately, stimulus expectancy affected several

23 cognitive outcomes, like long-term memory, process memory, and attention (Cropsey et al., 2017).

However, these results are not necessarily generalizable outside of adults without ADHD.

There has been some placebo research in children with ADHD as well. In 2008, an exploratory crossover study by Sandler and Bodfish evaluated the use of open-label placebos in combination with psychostimulants in children with ADHD. Each child spent one week taking their full dose of stimulant, one week taking half of their dose combined with open-label placebo, and one week taking half of their dose without placebo. The children’s ADHD symptoms did not change significantly between the full dose week and the half dose with placebo week but deteriorated during the week with half dose and no placebo (Sandler & Bodfish, 2008). While this is not the same as a BPD, it does suggest that there is a significant effect related to the administration of placebo independent of the administration of the drug. In 2017, Khan et al. analyzed data from the FDA concerning a variety of common pharmacological treatments for

ADHD to assess whether they showed placebo effects similar to those in antidepressants. The study found a steady increase in both placebo and drug response over the years, the same trend found in antidepressants, as reviewed above. This could indicate a rising response expectancy for stimulant medication as people become more familiar with their use in ADHD. Interestingly, they also found that drug trials in children generally found bigger differences between drug and placebo groups than adult trials, which may be a result of adults’ knowledge regarding stimulants (Khan et al., 2017). This evidence indicates that expectancies may play a significant role in the efficacy of psychostimulants for ADHD symptom management.

Expectancy effects may play a significant role in non-pharmacological interventions as well. Some studies have indicated that neurofeedback is only effective when moderated by the patients’ positive outcome expectancy (Lee & Suhr, 2019; Lee & Suhr, 2020; Perreau-Linck et al.,

24 2010). This corroborates the findings of parallel-design RCTs that found only small distinctions between neurofeedback and placebo (Arnold et al., 2013; Vollebregt et al., 2014). A 2019 pilot study by Lee & Suhr assessed the effects of positive and negative response expectancies for sham neurofeedback on various ADHD symptom measures, such as self-reported inattention and hyperactivity as well as neuropsychological measures. To measure these effects, each participant underwent a false neurofeedback procedure in which they wore a neurofeedback device and were instructed to focus their attention on their breathing for five minutes. The neurofeedback device would sense their brain activity and play wind sounds in response to the quality of their focus: calm winds with gentle bird noises for high concentration, harsher winds for wavering attention.

However, instead of hearing their actual live neurofeedback, participants heard a pre-recorded neurofeedback track that represented either good or bad performance. Participants were randomized to receive either positive or negative feedback, thus manipulating their response expectancies, or their belief about whether the neurofeedback was improving their attention. They found that negative false feedback was associated with increased reported ADHD symptoms

(Cohen’s d = .47), particularly inattentive symptoms (d = .52), after neurofeedback. However, those who received positive false feedback reported a significant decrease in total (d = -1.04) and inattentive symptoms (d = -1.12) after neurofeedback. This is evidence that positive response expectancies for neurofeedback can affect self-reported ADHD symptoms (Lee & Suhr, 2019).

These findings were replicated in a follow-up study (Lee & Suhr, 2020) with a bigger sample size.

VI. PRESENT STUDY AIMS

Because response expectancies have been shown to play a significant role in a variety of

ADHD treatments, it should be common practice to consider it in treatment studies. However, few

ADHD researchers consider the role of expectancy effects when designing their studies. The only

25 proxy for expectancy that is likely to be measured in a typical RCT is collected when researchers ask the participants the group to which they believe they were assigned, placebo or active treatment, to determine the blinded-ness of the study. By asking the participants for their best guess at their group assignment after they have received the treatment, researchers are collecting a coarse measure of the participant’s stimulus outcome expectancy. Usually, this measure is only used to check that the study was sufficiently well blinded. However, it could be useful for researchers to use the participant’s group assignment belief as a moderator in their data analysis to examine the effects of expectancies within each treatment (active treatment or placebo) in their data interpretation, even if the research is not specifically focused on expectancies.

In my thesis, I had two goals. First, I reviewed existing RCT studies of ADHD medication to determine the degree to which researchers measure, control for, or even acknowledge the potential expectancy effects associated with group assignment belief. Second, I performed a pseudo-BPD analysis on data from an existing ADHD neurofeedback efficacy study. By assessing whether group assignment belief moderated any outcomes of the study, the role of expectancies was estimated. Understanding the possible effects of expectancies in clinical research will help guide future study design, clarify an area of psychological research full of mixed results, and inform clinicians about a variable that may affect the efficacy of a treatment for their patients.

STUDY I: Review of Existing ADHD Studies

I. METHODS

On October 24, 2020, I entered the following search terms into the National Center for

Biotechnology Information National Library of Medicine website Pubmed

(pubmed.ncbi.nlm.nih.gov): (“Attention deficit hyperactivity disorder” OR “ADHD” OR

“attention deficit disorder” OR “ADD” OR “hyperkinetic disorder” OR “attention deficit*”) AND

26 (“randomized controlled trial” OR “randomised controlled trial” OR “controlled clinical trial” OR

“controlled trial” OR “crossover procedure” OR “crossover study” OR “crossover design” OR

“cross over study” OR “double blind” OR “single blind” OR “random allocation” OR

“randomization” OR “random assignment” OR “RCT”). This returned 8922 results. I then applied the following filters: limiting year of publication to 2000-2020 (removing 1199 results), limited article type to Clinical Trial or Randomized Controlled Trial (removing 2216 results), and limited species to humans (removing 30 results). I skimmed the titles and abstracts of the remaining 5477 studies to determine whether they met the following criteria:

- Assessed the efficacy of a pharmacological therapy for improving ADHD

symptoms in patients with ADHD

- Utilized a randomized, placebo-controlled, double-blind study design

- Published in a reputable, peer-reviewed, scientific journal

- Did not focus on a particular co-intervention, comorbid condition, or other

subpopulation

5206 studies did not meet these criteria, leaving 271 studies for consideration. Next, I examined each study for mentions of expectancies, blindedness check, group assignment belief, or other measures related to response expectancies.

II. RESULTS

See Appendix A for details of the review of each of the 271 studies. Out of the 271 studies that met the inclusion criteria, information regarding blinding procedures or consideration of expectancies was inaccessible for 16 studies, which are marked as “could not be determined” in the table. There were 234 studies (86%) with accessible methods and discussion that made no mention of expectancies, blindedness checks, or effects of participant group assignment belief. Of

27 the remaining 21 studies, 11 mentioned or alluded to expectancy effects in the discussion but did not consider expectancies in their study design or measure them in any way. Two studies mentioned excluding participants who were unblinded but did not explain how the blindedness of the participants was measured or estimated. Two studies that used a crossover design with two active conditions and one placebo asked participants for their preference between the three treatment conditions. One study referenced expectancy effects in the methods, saying that participants were told they were receiving a lower dose of the medication than they really were in an attempt to mitigate the participants’ past experience with the drugs, but did not assess or control for expectancy effects in any other way. Finally, only five studies (2%) controlled for expectancies.

Two studies, Pelham et al (2001) & (2002), used a BPD to assess the effect sizes of medication and expectancy. Pelham et al. (2001) did not find any effects for either medication or expectancy, but Pelham et al. (2002) found an expectancy effect for one variable (along with several medication effects). They found that the participants were more likely to attribute success to the medication when told truthfully that they had taken the medication, whereas they were more likely to attribute success to their own self-efficacy when they were told they had taken placebo but had actually taken the medication. However, neither study found any effects of expectancies on actual symptom or performance measures or clinical outcomes. Three studies assessed expectancies indirectly by including a blindedness measure. In one study, the blindedness measure was not assessed as a moderator of experimental results, but in two studies it was.

STUDY II: Pseudo-BPD Analysis of a Neurofeedback Study

I. SELECTING THE ARCHIVAL DATASET

First, we reached out to the authors of several studies that we knew measured some correlate of response expectancy in their ADHD treatment study (Barkley et al., 2005; Biederman

28 et al., 2006, 2010, 2011; Biehl et al., 2016; Bron et al., 2014; McGough et al., 2019; Verster et al.,

2008). Unfortunately, all of these authors either declined to share their data for analysis or did not respond. Next, we placed a data request with The Yale University Open Data Access (YODA)

Project, an effort to make data from clinical research accessible for other projects. I requested access to the eight studies available through their database that assessed the efficacy of a pharmacological intervention for treating symptoms of ADHD:

● NCT00307684 - An Open International Multicentre Long-Term Follow Up Study to

Evaluate Safety of Prolonged Release OROS Methylphenidate in Adults With Attention

Deficit Hyperactivity Disorder (Buitelaar et al., 2012)

● NCT00246220 - A Multicentre, Randomised, Double-Blind, Placebo-Controlled, Parallel

Group, Dose-Response Study To Evaluate the Safety And Efficacy Of Prolonged Release

OROS Methylphenidate Hydrochloride (18, 36 and 72 mg/Day), With Open-Label

Extension, In Adults With Attention Deficit/Hyperactivity Disorder (Medori et al., 2008)

● NCT00714688 - A Multicentre, Randomized, Double-Blind, Placebo-Controlled, Parallel

Group, Dose-Response Study to Evaluate Efficacy and Safety of Prolonged Release (PR)

OROS Methylphenidate (54 and 72 mg/Day) in Adults With Attention

Deficit/Hyperactivity Disorder (Casas et al., 2013)

● NCT00799409 - The ABC Study: A Double-Blind, Randomized, Placebo-Controlled,

Crossover Study Evaluating the Academic, Behavioral, and Cognitive Effects of

CONCERTA on Older Children With ADHD (Wigal et al., 2011)

● NCT00799487 - Double-Blind, Randomized, Placebo-Controlled, Crossover Study

Evaluating the Academic, Behavioral and Cognitive Effects of CONCERTA on Older

Children With ADHD (The ABC Study) (Murray et al., 2011)

29 ● NCT00937040 - A Placebo Controlled Double-Blind, Parallel Group, Individualizing

Dosing Study Optimizing Treatment of Adults With Attention Deficit Hyperactivity

Disorder to an Effective Response With OROS Methylphenidate (Goodman et al., 2017)

● NCT00326391 - A Placebo-Controlled, Double-Blind, Parallel-Group, Dose-Titration

Study to Evaluate the Efficacy and Safety of CONCERTA (Methylphenidate HCl)

Extended-release Tablets in Adults With Attention Deficit Hyperactivity Disorder at Doses

of 36 mg, 54 mg, 72 mg, 90 mg, or 108 mg Per Day (Adler et al., 2009)

● NCT01323192 - A Double-blind, Placebo-controlled, Parallel-Group Study to Evaluate the

Efficacy and Safety of JNS001 in Adults With Attention-Deficit/Hyperactivity Disorder at

Doses of 18 mg, 36 mg, 54 mg, or 72 mg Per Day (Takahashi et al., 2014)

My data access request was approved, and after reaching an agreement between Ohio University,

Yale University, and Johnson & Johnson, I was granted access to the full participant-level data from those eight clinical trials. I accessed the data through YODA’s clinical data sharing server remotely with a login provided by YODA. However, upon accessing and viewing the participant- level data for those eight clinical trials, I found that none of the datasets included any measure of response expectancy in their raw data. Notably, my data request specifically indicated that the study must include some measure of response expectancy; yet a detailed review of the datasets and of the studies’ methodology found that none reported including such measures in their methods and none included any variable associated with response expectancy in their dataset.

Since I was ultimately unable to access a dataset from a pharmacological clinical trial that met the criteria for my analysis, I re-analyzed data from Lee & Suhr’s (2020) sham neurofeedback study, described in the introduction, instead. Lee and Suhr (2020) did not examine the effects of actual neurofeedback performance. Therefore, I was able to use both actual neurofeedback

30 performance and expectancy effects to conduct the planned pseudo-BPD analysis.

II. PARTICIPANTS

The study participants were undergraduates from an introductory psychology course at

Ohio University seeking non-stimulant treatment for symptoms of ADHD or suspected ADHD.

The archival dataset included 133 individuals, but for the purposes of the present analyses, only

88 eligible study participants were included (see below). The participants all had a history of

ADHD diagnosis or concerns that they had ADHD, had no history of other neurological disorders, reported no visual or auditory impairments, had the ability to write and read English fluently, and reported no prior experience with neurofeedback therapy. All participants refrained from taking stimulant drugs for 24 hours prior to the study and refrained from drinking alcohol, drinking caffeine, taking drugs, and smoking for 12 hours prior to the study.

III. MEASURES

Demographic information, various psychological measures, affect, beliefs about neurofeedback, and baseline ADHD severity according to the Adult ADHD Self-Report Scale

(Adler et al., 2006) were collected for each participant. Self-reported ADHD symptoms were assessed before and after neurofeedback using the State Attention and Arousal Scale (SAAS), an

18-question measure developed from an existing ADHD self-report measure (Barkley, 2011) but modified to quantify the state of ADHD symptoms at a particular time rather than to assess the severity of ADHD over several months. The SAAS includes subscales for Inattention,

Hyperactivity, and Sluggish Cognitive Tempo (SCT). For the present analysis, the total SAAS score and the Inattention subscore pre and post neurofeedback were used.

Neuropsychological experimental measures of ADHD symptoms before and after neurofeedback included the Auditory Consonant Trigram Test (ACT), a measure of verbal

31 working memory, and the AX Continuous Performance Test (AXCPT), a measure of sustained attention (Lezak et al., 2004). For the present analysis, the reaction time for the AXCPT and the total score for the ACT pre and post neurofeedback were used. The ACT measures working memory by requiring participants to recall three letters after performing a distraction task for variable time periods. There are 15 trials; the score can range from 0-33. The AXCPT requires participants to discriminate between target letter combinations (i.e., AX) and distractor, non-target letter combinations (i.e., AY, BX, BY) when presented with irrelevant distractor information. The score used is the % correct.

IV. METHODS

Participants received a session of neurofeedback using the MUSE brain sensing headband

(https://choosemuse.com/). MUSE measures brain waves using EEG sensors across the forehead and behind the ears to transform data on gamma, beta, alpha, theta, and delta waves to auditory feedback. Unlike other EEG neurofeedback devices, complex combinations of brainwave patterns are subjected to machine learning analyses to provide feedback on calmness and alertness.

Prior to the neurofeedback session, each participant was instructed on how the neurofeedback device works and the different sounds they would hear in response to different brain activity (gentle breeze with birds if they focused successfully, harsher winds with no birds if their attention was not focused). The participants then completed a neurofeedback practice round to calibrate the device and familiarize themselves with the procedure, during which they heard sounds that corresponded with their real brain activity. After the one-minute practice session, they began a five-minute neurofeedback session where they were randomized to one of three groups: positive, negative, or neutral false feedback. Regardless of actual neurofeedback performance, those in the positive false feedback group were told that they were focusing well and heard birds

32 chirping and calm winds, representing very good focus. Those in the negative false feedback were told that they were not focusing well and heard loud winds with no birds, representing poor focus.

Finally, those in the neutral false feedback were told that they were concentrating normally and heard calm winds with occasional birds, representing a level of focus between the extremes. The participants’ actual brain activity was recorded by the neurofeedback device.

V. STATISTICAL ANALYSIS

Lee & Suhr’s (2020) results suggested that the neutral false feedback expectancy manipulation may not have been truly neutral, but rather perceived as positive. Thus, we chose to leave it out of our analysis and to instead focus only on the positive (n = 44) and negative (n = 44) false feedback groups, leaving out the 45 individuals who had received neutral feedback. We also coded the actual neurofeedback performance as “good” versus “poor” based on MUSE data. These scores were quantified across all the neurofeedback data from the one neurofeedback session into a single score representing the percentage of time that they were alert and attentive as defined by the MUSE software. On average, “good” performers were alert and attentive for 50% of the session, while “poor” performers only achieved this state for an average of 11% of the session.

Thus, the basic research design resulted in two independent variables, false feedback (with two levels, positive and negative) and actual neurofeedback performance (two levels, based on output from the MUSE).

Demographic characteristics of the participants were compared by group using independent sample t-tests and chi-square tests. Outcome measures were screened for missing data, presence of outliers, normality of the distributions, sphericity, and homogeneity of variances and covariances, and violations of assumptions. Data were then analyzed in 2 (false feedback group: positive vs. negative) x 2 (actual neurofeedback performance: good vs poor) x 2 (time of

33 measurement: pre- vs. post-sham neurofeedback) mixed factorial ANOVA tests to evaluate hypothesized interactions between expectancy manipulation, neurofeedback performance, and

ADHD symptom measures. Follow up comparisons were used to test significant interactions, and effects sizes were calculated to show the magnitude of group differences. All analyses were conducted using the Statistical Package for the Social Sciences (SPSS).

VI. RESULTS

A. DESCRIPTIVE STATISTICS

Table 5 shows the mean and standard deviation of age and year in college in the positive and negative false neurofeedback groups. It also shows differences in assigned sex at birth

(gender identity excluded because all matched sex at birth), history of ADHD diagnosis, and meeting symptom cutoff for an ADHD diagnosis in the positive and negative false neurofeedback groups. This was determined using the Adult ADHD Self Report Scale, where scores greater than 14 were considered positive ADHD screeners (Adler et al., 2006). Groups did not differ in age, F(1, 86) = .41, p = .52, sex at birth, χ2(1) = 1.19, p = .28, White/non-White race/ethnicity, χ2(1) = .60, p = .44, or current year in college p = .79. The two groups were significantly different in history of ADHD diagnosis, χ2(1) = 4.95, p = .03, where there were more participants with a past ADHD diagnosis in the negative false feedback group than in the positive false feedback group. However, groups did not differ in the number of individuals with scores above the clinical cutoff on the ADHD screener at baseline, χ2(1) = .05, p = .82.

Next, data from ADHD symptom measures and neuropsychological measures were evaluated to determine the characteristics of the distributions of each outcome variable. Table 6

34 Table 5

Demographic Comparison of Study Groups

Characteristics Group

Positive False Feedback Negative False Feedback

(n = 44) (n = 44) Mean (standard deviation) Mean (standard deviation)

Age 19.02 (1.00) 19.09 (1.03)

Year in college 1.59 (0.84) 1.5 (0.79)

N (%) N (%)

Assigned sex at birth Female 29 (66%) 24 (55%) Male 15 (34%) 20 (45%)

ADHD diagnosis 1 (2%) 7 (16%)

Positive ADHD screener 13 (30%) 14 (32%)

Note. ADHD - Attention Deficit/Hyperactivity Disorder shows the mean, standard deviation, range, skew, and kurtosis of the Auditory Consonant

Trigram (ACT) total score, the AX Continuous Performance Test (AXCPT) percent correct,

AXCPT mean reaction time, the State Attention and Arousal Scale (SAAS) total score, and

SAAS inattentive, hyperactive, and sluggish cognitive tempo (SCT) subscores post neurofeedback. Since the distributions for ACT total score, AXCPT mean reaction time, SAAS total score, and SAAS inattentive subscale were neither kurtotic nor skewed, we could use the raw data for analyses. Performance on these outcome measures neurofeedback (NF) performance group (good or poor) and type of feedback (positive or negative) are described in Table 7.

35 Table 6

Descriptive Statistics for Study Measures

Measure Mean (standard error) Range Skew Kurtosis

ACT post total 32.48 (0.87) 12-45 -1.90 -1.00

AXCPT post % correct 86.08 (1.67) 34-99 -6.72 4.74

AXCPT post mean 400 (7.59) 233-630 1.91 1.62 reaction time (msecs)

SAAS post - total 32.57 (0.91) 21-53 2.73 -0.76

SAAS post - attention 12.94 (0.51) 7-25 2.38 -1.06

SAAS post - 4.62 (0.22) 3-11 5.34 2.84 hyperactivity

SAAS post - SCT 12.75 (0.42) 8-25 3.31 0.42

Note. ACT - Auditory Trigram Test. AXCPT - AX Continuous Performance Test. SAAS - State

Attention and Arousal Scale. SCT - Sluggish cognitive tempo.

B. AUDITORY CONSONANT TRIGRAM TEST

Since Mauchly’s test indicated no violation of the assumption of sphericity, corrections were not required for the degrees of freedom. The data did not violate assumptions of homogeneity either, as shown by Levene’s tests.

Although not quite statistically significant, there was a trend for a three way interaction

2 between time, expectancy manipulation, and actual NF performance, F(1,83) = 3.66, p = .06, ηp

= .04. Follow up statistical tests showed that, for those who actually did well on the NF session, those who received positive feedback significantly improved in ACT performance over time, p <

36 Table 7

Performance of Study Groups on Measures of Interest

Measure Time Good NF Performance Poor NF Performance

Positive Negative Positive Negative feedback feedback feedback feedback

N = 22 N = 25 N = 22 N = 18

Mean (SD) Mean (SD) Mean (SD) Mean (SD)

ACT Pre 29.14 (6.32) 29.80 (7.91) 30.91 (8.17) 30.89 (7.47)

Post 34.41 (7.87) 30.56 (8.68) 32.68 (7.30) 32.56 (8.63)

AXCPT reaction Pre 422 (65) 405 (80) 436 (72) 408 (58) time Post 414 (74) 389 (55) 427 (83) 367 (58)

SAAS - total Pre 33.73 (9.46) 33.68 (9.25) 34.36 (9.74) 30.42 (6.92)

Post 28.46 (5.27) 35.92 (8.41) 29.77 (7.72) 34.63 (10.11)

Pre 13.36 (5.22) 13.48 (5.19) 13.82 (4.55) 11.68 (3.95) SAAS - attention Post 10.86 (2.92) 15.12 (4.78) 10.68 (3.71) 14.79 (5.49)

Note. ACT - Auditory Trigram Test. AXCPT - AX Continuous Performance Test. SAAS - State

Attention and Arousal Scale. NF - neurofeedback.

.001, d = .74, while those who received negative feedback did not change in performance, p =

.56 (See Figure 5, top). However, for those who did not do well on the NF session, although both those who received positive feedback and negative feedback both appeared to improve over time,

37 Figure 5

Auditory Consonant Trigram test scores before and after neurofeedback by actual neurofeedback performance, good (top) or poor (bottom). the improvement was not significant (p = .12 for positive feedback, p = .05 for negative feedback) (See Figure 5, bottom). Note that the scale of the y axis differs between the top and

38 bottom.

There was a significant interaction between time and expectancy manipulation, F(1, 83) =

2 4.03, p = .05, ηp = .05, such that, regardless of good or poor NF performance, those who received positive feedback improved significantly more in ACT performance (d = .48) than those who received negative feedback (d = .14). There was no significant interaction between time and

2 actual NF performance, F(1,83) = 1.27, p = .26, ηp = .02. There was a significant main effect of

2 time, F(1,83) = 16.93, p < .001, ηp = .17, showing that there was a large practice effect for this measure. There were no main effects for expectancy manipulation collapsed across time, F(1,83)

2 2 < 1, p = .60, ηp = .003, actual NF performance, F(1,83) < 1, p = .62, ηp = .003, or their

2 interaction, F(1,83) < 1, p = .63, ηp = .003.

C. AX CONTINUOUS PERFORMANCE TEST REACTION TIME

Since Mauchly’s test indicated no violation of the assumption of sphericity, corrections were not required for the degrees of freedom. The data did not violate assumptions of homogeneity either, as shown by Levene’s tests.

There was no significant effect for the three way interaction between time, expectancy

2 manipulation, and actual NF performance, F(1,82) = .68, p = .41, ηp = .01, time and expectancy

2 manipulation, F(1, 82) = 1.92, p = .17, ηp = .02, or time and actual NF performance, F(1,82) =

2 2 .93, p = .34, ηp = .01. There was a significant effect of time, F(1,82) = 6.64, p = .01, ηp = .08, with reaction time after neurofeedback being faster on average than before, collapsed across groups. Collapsed across time, there was a main effect for expectancy manipulation, F(1,82) =

2 4.43, p = .04, ηp = .05, with the positive feedback group performing better than the negative feedback group regardless of time (pre or post neurofeedback). There was no main effect for

39 2 actual NF performance, F(1,82) < 1, p = .87, ηp = .000, or their interaction, F(1,82) = .57, p =

2 .45, ηp = .01.

D. STATE AROUSAL & ATTENTION SCALE - TOTAL

Since Mauchly’s test indicated no violation of the assumption of sphericity, corrections were not required for the degrees of freedom. The data did not violate assumptions of homogeneity either, as shown by Levene’s tests.

There was no significant effect for a three way interaction between time, expectancy

2 manipulation, and actual NF performance, F(1,83) = .20, p = .65, ηp = .002. There was a

2 significant interaction between time and expectancy manipulation, F(1,83) = 32.66, p < .001, ηp

= .28. Collapsed across actual NF performance, those who received positive feedback significantly decreased their report of ADHD symptoms after NF, p < .001, d = -.60, while those who received negative feedback significantly increased their report of ADHD symptoms after

NF, p = .004, d = .35 (See Figure 6). There was no significant interaction between time and

2 actual NF performance, F(1,83) = .86, p = .36, ηp = .01. There was no significant effect of time,

2 F(1,83) = 1.43, p = .24, ηp = .08. There were no main effects for expectancy manipulation,

2 2 F(1,83) = 1.55, p = .22, ηp = .02, actual NF performance, F(1,83) = 18.31, p = .70, ηp = .002, or

2 their interaction, F(1,83) = 115, p = .34, ηp = .01 collapsed across time.

E. STATE AROUSAL & ATTENTION SCALE - ATTENTION

Since Mauchly’s test indicated no violation of the assumption of sphericity, corrections were not required for the degrees of freedom. The data did not violate assumptions of homogeneity either, as shown by Levene’s tests.

40 There was no three way interaction between time, expectancy manipulation, and actual

2 NF performance, F(1,83) = 1.40, p = .24, ηp = .02. There was a significant interaction between

Figure 6

State Arousal & Attention Scale total scores before and after neurofeedback by group, positive or negative feedback

2 time and expectancy manipulation, F(1, 83) = 34.22, p < .001, ηp = .29. Follow-up statistical tests showed that, collapsed across actual NF performance, those who received positive feedback

41 significantly decreased their report of attention symptoms, p < .001, d = -.68 while those who received negative feedback significantly increased their report of attention symptoms, p = .003, d

= .47 (See Figure 7). There was no significant interaction between time and actual NF

2 performance, F(1,83) = .22, p = .64, ηp = .003. There was no significant effect of time, F(1,83) =

2 .25, p = .62, ηp = .003. There were no main effects across time for expectancy manipulation,

2 2 F(1,83) = 3.33, p = .07, ηp = .04, actual NF performance, F(1,83) = 9.36, p = .60, ηp = .003, or

2 their interaction, F(1,83) = 15.68, p = .49, ηp = .01.

DISCUSSION

Study 1

Despite evidence for the importance of blinding efficacy measures and expectancy effect controls in clinical trials, we found that the vast majority of studies assessing Attention

Deficit/Hyperactivity Disorder (ADHD) treatment efficacy do not report anything on these topics.

This supports our claim that expectancy effects are overlooked in clinical trials for ADHD interventions. Of the only two studies that did, both of young males with ADHD, one (Pelham et al., 2001) examined performance on an experimental social measure, not clinical outcomes, but found no effect of either medication or expectancies, nor any interaction effect. Of the other, there was an effect for medication on several clinical outcomes measured during a summer treatment program, but no significant effect of expectancy and no interaction (Pelham et al., 2002).

To paint a more complete picture of the role of expectancies in ADHD treatment outcomes, researchers should start to collect and report data from expectancy measures when assessing the efficacy of a treatment. Expectancy measures would not only allow for the efficacy of the blinding efforts to be assessed, but also allow for a pseudo balanced placebo design (BPD) style analysis of the outcome measures. By including a measure of stimulus expectancy, expectancy effects could

42 Figure 7

State Attention & Arousal Scale Attention Scores before and after Neurofeedback by Group

be reported for randomized clinical trials (RCTs) when the BPD is not possible or ethical.

This literature review faced several limitations. First, I searched for studies on Pubmed only in the interest of time. Thus, it is possible there are studies describing expectancy effects in ADHD treatment research on other platforms and databases. Second, I was the only person involved in the literature review. Double coding the data may have helped identify any relevant studies that I missed.

43

Study 2

Our hypotheses, that expectancy manipulation and actual neurofeedback (NF) performance moderate ADHD symptom severity, were partially supported by the data. For Auditory Consonant

Trigram test (ACT) total score, we saw a trend for a three-way interaction between expectancy manipulation, actual neurofeedback time, and time. Those who received positive expectancy manipulation and actually did well on the neurofeedback significantly improved on the ACT total score, with an effect size that falls in the medium to large range. In a 2x2 pseudo-BPD analysis, this is analogous to the treatment response: the treatment effect (actual NF performance) with the positive expectancy effect (see Table 4). But it is notable that there was no ACT effect for those who actually had good neurofeedback performance but who received negative feedback. In the pseudo-BPD model, this group is analogous to the group that is told they received the placebo but received the active treatment. While this three-way interaction did not reach significance, the presence of the trend indicates that it would be useful for researchers to check for such three way interactions in future research studies that have larger sample sizes. For the other neuropsychological measure, AX Continuous Performance Test (AXCPT) reaction time, the only significant effect was a practice effect for time.

While neither neuropsychological measure showed a significant effect of expectancy or

NF performance from pre to post neurofeedback, both of the self-report measures showed significant effects for expectancy manipulation. That is, regardless of whether they actually focused well during the neurofeedback session, participants’ self-rated ADHD symptoms were significantly affected by the type of false feedback they received. For both the positive and negative false feedback groups, the changes in State Attention and Arousal Scale (SAAS) score

44 were in the hypothesized direction: positive feedback reduced scores, negative feedback increased scores. The positive and negative false feedback conditions had medium (d = .60) and small (d =

0.35) effect sizes respectively for SAAS total score. On the inattentive subscore of the SAAS, positive and negative false feedback conditions both had medium effect sizes (d = -0.64 and d =

0.47) in the expected directions as well.

The limitations of the original study carry over into the present study. The sample was predominantly white college students, most without any history of ADHD diagnosis, which limits the generalizability of the results to other demographic groups and clinical populations. However, despite the lack of ADHD diagnosis history in the full sample, 30% of the participants scored high enough on the ADHD screener to be considered at a clinical level of impairment. In addition, the neurofeedback session was not a full intervention as in other neurofeedback treatment studies (one session for five minutes), which may have also affected the observability of the effects and the statistical power of the study. The study was also limited by the use of two experimental attention tasks for which there are no clinical cutoffs. More sensitive clinical sustained attention and working memory tasks could be used in future studies.

Unexpected obstacles in obtaining suitable data from other sources made it necessary to use Lee and Suhr’s (2020) sham neurofeedback study rather than a double blind, placebo controlled pharmacological efficacy study. Thus, the mixed factorial analysis did not exactly mimic the BPD as planned. Rather than analyzing data by experimental condition (active treatment or placebo) and stimulus expectancy (active treatment or placebo), data were analyzed by experimental condition (positive or negative false feedback) and actual neurofeedback performance (good or poor). Thus, our analysis expands upon Lee and Suhr’s findings regarding expectancy effects in neurofeedback, but does not reveal as much about the contribution of

45 expectancy effects to the overall treatment effects of authentic neurofeedback therapy as the planned pseudo-BPD analysis would have.

Despite the limitations, analysis of the combined effects of false feedback and actual neurofeedback performance revealed multiple interesting trends that support the need for further research regarding expectancy effects in ADHD intervention research. The ACT, a working memory measure, was significantly positively related to experimentally manipulated stimulus expectancy (false feedback group). There was also a combined effect of neurofeedback performance and stimulus expectancy that trended towards but did not reach significance. That is,

ACT-measured working memory improved significantly with a positive response expectancy, and that improvement was likely to be slightly better for participants who also achieved brain states that the MUSE apparatus would have interpreted as good focus. Neurofeedback performance alone did not have a significant effect on working memory; its only notable effect was as a moderator of expectancy.

The expectancy manipulation had a significant effect on both the total score and inattention subscore of the SAAS in the expected direction: positive feedback was associated with a greater decrease in self-reported ADHD symptoms than negative feedback. Actual neurofeedback performance did not have a significant effect on either measure alone or in combination with expectancy. This adds to existing evidence that expectancy effects have a significant impact on self-reported ADHD symptom measures (Lee & Suhr, 2019; Lookatch, Fivecoat, & Moore, 2017;

Perreau-Linck et al., 2010). Since so many ADHD treatment efficacy studies depend on self-report to determine treatment effects, the recurring findings of expectancy-related effects on these measures ought to raise some concerns for the lack of expectancy measures in the research (Du

Rietz et al., 2016). Currently, ADHD treatment efficacy research standards do not require any

46 measure of expectancy effects, and thus the treatment effect is indistinguishable from the expectancy effect in the results. By using a BPD or including an expectancy measure when a BPD is not possible, practical, or ethical, the effects of ADHD treatments could be more thoroughly understood. Future research could also be done to retroactively assess expectancy effects in existing research that included a blindedness or expectancy measure but did not explicitly analyze data for expectancy x experimental condition interactions. The effects of expectancies on neuropsychological outcomes other than the ACT and AXCPT require further research as well.

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60 Appendix

Expectancy or blindedness

Citation Intervention Design measure

in discussion: "Parents’

experience during the titration

phase presumably heightened

their awareness of the behavioral

differences associated with active

and placebo medication. Such

Abikoff, H. B., Vitiello, B., Riddle, M. A., Cunningham, C., knowledge, in conjunction with

Greenhill, L. L., Swanson, J. M., Chuang, S. Z., Davies, M., study guidelines that allowed

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H., McCracken, J. T., McGough, J. J., Murray, D. W., Posner, participation in the double-blind,

K., Skrobala, A. M., & Wigal, T. (2007). Methylphenidate parallel-group phase and have effects on functional outcomes in the Preschoolers with 2 arm parallel RCT their child move directly to open

Attention-Deficit/Hyperactivity Disorder Treatment Study with open label maintenance treatment with MPH,

(PATS). Journal of Child and Adolescent Psychopharmacology, methylphenidate + dose optimization likely contributed to dropout

17(5), 581–592. https://doi.org/10.1089/cap.2007.0068 placebo lead in period decisions for some parents."

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