DOCSLIB.ORG

Advancing the Neurophysiological Understanding of Delirium †‡ †‡§ Mouhsin M

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Advancing the Neurophysiological Understanding of Delirium †‡ †‡§ Mouhsin M SPECIAL ARTICLE Advancing the Neurophysiological Understanding of Delirium †‡ †‡§ Mouhsin M. Shafi, MD, PhD,* Emiliano Santarnecchi, PhD,* Tamara G. Fong, MD, PhD, || † Richard N. Jones, ScD, # Edward R. Marcantonio, MD, SM, ** Alvaro Pascual-Leone, †‡ †§ MD, PhD,* and Sharon K. Inouye, MD, MPH ** annual costs of $164 billion in the United States.1,2 Delir- Delirium is a common problem associated with substantial ium is associated with cognitive decline, loss of functional morbidity and increased mortality. However, the brain independence, and increased mortality.1 Therefore, identi- dysfunction that leads some individuals to develop delir- fying individuals at risk for developing delirium, differenti- ium in response to stressors is unclear. In this article, we ating episodes of delirium likely to lead to lasting briefly review the neurophysiologic literature characteriz- deleterious consequences, and minimizing the impact of ing the changes in brain function that occur in delirium, delirium have assumed heightened importance. One central and in other cognitive disorders such as Alzheimer’s dis- limitation preventing the achievement of these clinical ease. Based on this literature, we propose a conceptual goals is that current clinical definitions, such as the widely model for delirium. We propose that delirium results from used Confusion Assessment Method (CAM3), are based on a breakdown of brain function in individuals with impair- cognitive tests and bedside observations, and it is not com- ments in brain connectivity and brain plasticity exposed to pletely clear how these signs and symptoms relate to the a stressor. The validity of this conceptual model can be underlying brain dysfunction. Thus, a critical step is to tested using Transcranial Magnetic Stimulation in combi- understand, at a neurophysiologic level, why delirium hap- nation with Electroencephalography, and, if accurate, pens in specific individuals. To this end, we begin by could lead to the development of biomarkers for delirium briefly reviewing the current literature on neurophysiologic risk in individual patients. This model could also be used investigations of delirium and other conditions that affect to guide interventions to decrease the risk of cerebral dys- cognition, such as Alzheimer’s disease (AD). We present a function in patients preoperatively, and facilitate recovery conceptual model advancing the hypothesis that delirium in patients during or after an episode of delirium. JAm is due to a breakdown of normal brain function reflecting Geriatr Soc 2017. impairments in brain connectivity and plasticity. We explain how this model can be tested using the combina- Key words: delirium; electroencephalography; transcra- tion of Transcranial Magnetic Stimulation (TMS) and elec- nial magnetic stimulation; connectivity; plasticity troencephalograpy (EEG), and discuss some of the clinical implications of the model. Neurophysiological Investigations of Delirium to Date Neurophysiology during delirium has traditionally been studied using EEG, which measures the electrical fields elirium is a common and costly problem, affecting up produced by synchronized synaptic activity of cortical neu- to 50% of hospitalized older adults, with estimated D rons. EEG activity is often divided into different spectral frequency bands, and changes in spectral band power (sig- From the *Berenson-Allen Center for Noninvasive Brain Stimulation, Beth † ‡ nal strength) have been reported in different disease states. Israel Deaconess Medical Center; Harvard Medical School; Department § of Neurology, Beth Israel Deaconess Medical Center; Aging Brain Center, The traditional spectral frequency bands evaluated during Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts; EEG recordings include delta (1–4 Hz), theta (4–8 Hz), || Department of Psychiatry and Human Behavior, Brown University alpha (8–13 Hz) and beta (13–30 Hz) bands; alpha activ- # Warren Alpert Medical School; Department of Neurology, Brown ity is the most prominent rhythm in the resting awake University Warren Alpert Medical School, Providence, Rhode Island; and **Department of Medicine, Beth Israel Deaconess Medical Center, state (eyes-closed). More recently, techniques have been Boston, Massachusetts. developed to assess statistical correlations in the EEG sig- Address correspondence to Mouhsin M. Shafi, Department of Neurology, nals recorded from different electrodes, that indicate con- Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, nectivity between different brain regions. Typically, these MA 02215. E-mail: mshafi@bidmc.harvard.edu measures fall into two broad categories: measures of func- DOI: 10.1111/jgs.14748 tional connectivity, which identify correlations in the JAGS 2017 © 2017, Copyright the Authors Journal compilation © 2017, The American Geriatrics Society 0002-8614/17/$15.00 2 SHAFI ET AL. 2017 JAGS statistical properties of brain signals from two or more A Conceptual Neurophysiological Model of Delirium regions, or measures of effective connectivity, which attempt to identify causal interactions between regions. Based on the above results, we propose a conceptual Changes in EEG spectral power and connectivity have model (Table 1; Figure 1) that delirium is the consequence been identified in patients with diseases that affect cognition of the breakdown in brain network dynamics induced by such as Mild Cognitive Impairment (MCI) and AD. In these insults or stressors in individuals with baseline low brain conditions, for example, loss of frontoparietal EEG connec- resilience due to low connectivity and/or deficient mecha- tivity is correlated with cognitive test results and disease nisms of neuroplasticity, such as may be present in AD. progression over time.4 Extending this work in MCI and Neuroplasticity, defined as the brain’s ability to reorganize AD, EEG measures may be useful in characterizing cerebral itself by forming new neural connections throughout life, dysfunction in patients at risk for delirium. EEG is also use- allows the brain to compensate for injury and disease, and ful in understanding the physiologic changes that occur dur- is often considered necessary for neurologic resilience (the ing delirium, when the most consistent neurophysiological ability to accommodate to or recover from a stressor).14 abnormality is a relative slowing of resting-state EEG Relevant brain stressors can include major surgery, general rhythms, with abnormally decreased background alpha anesthesia, systemic inflammation, infections, and psy- power and increased theta- and delta-frequency activity.5,6 chotropic drugs. Health conditions that might result in The degree of EEG slowing in delirium correlates with impaired plasticity include pre-existing neurodegenerative decline in performance on cognitive tests,6 and both EEG disorders, such as MCI and AD,15 and comorbid condi- slowing and cognitive dysfunction normalize when meta- tions such as diabetes16 or renal impairment. This model bolic derangements leading to delirium (e.g., hypoxemia, predicts that when individuals are confronted with acute hypoglycemia) resolve.5 More recently, increased spectral insults, these stressors will alter brain connectivity (e.g., variability,7 decreased complexity of EEG activity,7 and within the dorsal attention network17) and/or brain net- decreased EEG connectivity in the alpha band8 have also work dynamics (e.g., the relationship between dorsolateral been reported during delirium. EEG changes can differenti- prefrontal cortex activity and posterior cingulate activ- ate individuals with delirium from those without delirium ity12), resulting in symptoms of delirium such as inatten- with an estimated sensitivity of 83.3% and specificity of tion. This impact will be greater in individuals with 77.8% for visual analysis of EEG features,9 up to a sensitiv- pre-existing deficits in brain connectivity, specifically in the ity of 100% and specificity of 96% for a quantitative mea- brain networks involved in resilience—which are linked to sure of EEG spectral power.10 Notably, EEG features may the construct of cognitive reserve.18 Such alterations in help differentiate patients with delirium and dementia from brain connectivity and dynamics will be inadequately com- those with dementia alone with up to 83% accuracy.11 pensated in a brain with impaired plasticity, manifesting as However, in these studies the specific measures were retro- the clinical syndrome of delirium. Supporting this model is spectively identified. Prospective studies validating these the finding of altered connectivity and impaired plasticity measures against reference standard delirium ratings are in AD, which is established as a major risk factor for delir- needed. In current clinical practice, EEG is used to distin- ium.1 This validity of this conceptual model can be guish delirium from nonconvulsive status epilepticus or an assessed using the combination of Transcranial Magnetic underlying psychiatric condition. Stimulation (TMS) and EEG. Another method useful for analysis of functional con- nectivity is resting-state functional magnetic resonance Testing the Conceptual Model of Delirium with TMS- imaging (rs-fMRI). In this method, brain activity is mea- EEG sured while the subject sits in the MRI in a resting state (not doing any tasks, in contrast to active functional neu- EEG and functional MRI passively record brain activity, roimaging); different brain regions that show correlated and therefore are limited in their capacity for
Load more

Recommended publications
  • Resting-State Fmri Reveals Network Disintegration During Delirium T ⁎ Simone J.T
    NeuroImage: Clinical 20 (2018) 35–41 Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: www.elsevier.com/locate/ynicl Resting-state fMRI reveals network disintegration during delirium T ⁎ Simone J.T. van Montforta, , Edwin van Dellenb,c, Aletta M.R. van den Boscha,d, Willem M. Ottee,f, Maya J.L. Schutteb, Soo-Hee Choig, Tae-Sub Chungh, Sunghyon Kyeongi, Arjen J.C. Slootera, ⁎⁎ Jae-Jin Kimi, a Department of Intensive Care Medicine, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, The Netherlands b Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, the Netherlands c Melbourne Neuropsychiatry Center, Department of Psychiatry, University of Melbourne, Australia d Faculty of Science, University of Amsterdam, the Netherlands e Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht University, the Netherlands f Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, the Netherlands g Department of Psychiatry, Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea h Department of Radiology, Yonsei University Gangnam Severance Hospital, Seoul, Republic of Korea i Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. ARTICLE INFO ABSTRACT Keywords: Delirium is characterized by inattention and other cognitive deficits, symptoms that have been associated with Delirium disturbed interactions between remote brain regions. Recent EEG studies confirm that disturbed global network fMRI topology may underlie the syndrome, but lack an anatomical basis. The aim of this study was to increase our Resting-state understanding of the global organization of functional connectivity during delirium and to localize possible Brain networks alterations.
    [Show full text]
  • Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes
    JCN Open Access ORIGINAL ARTICLE pISSN 1738-6586 / eISSN 2005-5013 / J Clin Neurol 2019;15(2):211-220 / https://doi.org/10.3988/jcn.2019.15.2.211 Electroencephalographic Resting-State Functional Connectivity of Benign Epilepsy with Centrotemporal Spikes Hyun-Soo Choia* Background and Purpose We aimed to reveal resting-state functional connectivity char- Yoon Gi Chungb* c acteristics based on the spike-free waking electroencephalogram (EEG) of benign epilepsy Sun Ah Choi with centrotemporal spikes (BECTS) patients, which usually appears normal in routine visual d Soyeon Ahn inspection. c Hunmin Kim Methods Thirty BECTS patients and 30 disease-free and age- and sex-matched controls Sungroh Yoona were included. Eight-second EEG epochs without artifacts were sampled and then bandpass Hee Hwangc filtered into the delta, theta, lower alpha, upper alpha, and beta bands to construct the associa- Ki Joong Kime,f tion matrix. The weighted phase lag index (wPLI) was used as an association measure for EEG signals. The band-specific connectivity, which was represented as a matrix of wPLI values of a Department of Electrical and all edges, was compared for analyzing the connectivity itself. The global wPLI, characteristic Computer Engineering, path length (CPL), and mean clustering coefficient were compared. Seoul National University, Seoul, Korea Results The resting-state functional connectivity itself and the network topology differed in ‌bHealthcare ICT Research Center, the BECTS patients. For the lower-alpha-band and beta-band connectivity, edges that showed Seoul National University significant differences had consistently lower wPLI values compared to the disease-free con- Bundang Hospital, Seongnam, Korea c trols.
    [Show full text]
  • Time-Resolved Resting-State Brain Networks
    Time-resolved resting-state brain networks Andrew Zaleskya,b,1, Alex Fornitoa,c, Luca Cocchid, Leonardo L. Golloe, and Michael Breakspeare,f,g aMelbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC 3010, Australia; bMelbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia; cMonash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, VIC 3168, Australia; dQueensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia; and eQIMR Berghofer Medical Research Institute, fThe Royal Brisbane and Women’s Hospital, and gMetro North Mental Health, Brisbane, QLD 4029, Australia Edited by Michael S. Gazzaniga, University of California, Santa Barbara, CA, and approved May 19, 2014 (received for review January 13, 2014) Neuronal dynamics display a complex spatiotemporal structure consistently revealed fluctuations in resting-state functional con- involving the precise, context-dependent coordination of acti- nectivity at timescales ranging from tens of seconds to a few vation patterns across a large number of spatially distributed minutes (19–24). Furthermore, the modular organization of func- regions. Functional magnetic resonance imaging (fMRI) has played tional brain networks appears to be time-dependent in the resting a central role in demonstrating the nontrivial spatial and topolog- state (25, 26) and modulated by learning (27) and cognitive effort ical structure of these interactions, but thus far has been limited in (28, 29). It is therefore apparent that reducing fluctuations in its capacity to study their temporal evolution. Here, using high- functional connectivity to time averages has led to a very useful but ’ resolution resting-state fMRI data obtained from the Human static and possibly oversimplified characterization of the brain s Connectome Project, we mapped time-resolved functional connec- functional networks.
    [Show full text]
  • Psychedelic Resting-State Neuroimaging: a Review and Perspective on Balancing Replication and Novel Analyses
    Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses Drummond E-Wen McCulloch1, Gitte Moos Knudsen1,2, Frederick Streeter Barrett3, Manoj Doss3, Robin Lester Carhart-Harris4,5, Fernando E. Rosas5,6,7, Gustavo Deco8,9,10,11,12, Katrin H. Preller13, Johannes G. Ramaekers14, Natasha L. Mason14, Felix Müller15, Patrick MacDonald Fisher1 1Neurobiology Research Unit, Rigshospitalet, 2100 Copenhagen, Denmark 2Institute of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark 3Department of Psychiatry and Behavioral Sciences, Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA 4Neuroscape, Weill Institute for Neurosciences, University of California San Francisco, USA 5Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London SW7 2DD, UK 6Data Science Institute, Imperial College London, London SW7 2AZ, UK 7Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK 8Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain, 9Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain, 10Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain, 11Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 12School of Psychological Sciences, Monash University, Melbourne, Australia 13Pharmaco-Neuroimaging
    [Show full text]
  • DIFFUSION TENSOR IMAGING (DTI) – Microstructural Brain Abnormalities SAGES Study / MRI Measures
    Neuroimaging and Delirium 5th Annual Delirium Boot Camp November 2017 Michele Cavallari, M.D., Ph.D. Center for Neurological Imaging, Department of Radiology, BWH, HMS MRI : What Do We Measure? Historical Perspective ventriculography and pneumoencephalagraphy (Walter Dandy) “delirium phrenesis cerebral arteriogram caused by (Moniz) disease of the brain and its membranes” (Bartholomeus Anglicus) CT applied to clinical diagnosis “delirium” (Hounsfield) (Celsus) “phrenitis” first MRI (Hippocrates) (Lauterbur) 1st CENTURY 13th 500 BC 1918 1927 1973 MID 1970s AD CENTURY Role of Neuroimaging DELIRIUM (syndrome) Predisposing Factors What Happens Long-term Effect Vulnerability During Delirium (Methodologically Challenging) 1. QUANTIFY 1. LOCALIZE Acquisition Analysis Safety Image Analysts PMK, metal, kidney function Supervised postdoc/RA for contrast-MRI Motion Computer Scientist ≥30 min exam Non-conventional analyses, $$$ improve workflows Price/hour Physicist Time-consuming Non-conventional sequences Human-interactive tools, quality control Is MRI the Right Tool to Answer Your Scientific Question? Successful AGing after Elective Surgery Study Participants • Older Individuals ≥70 years • Dementia-free • Non-cardiac Elective Surgery Successful AGing after Elective Surgery Study Design and Aims 2 1 DELIRIUM N=146 N=126 (32 with delirium) (28 with delirium) PREDISPOSING SUBSTRATES LONG TERM EFFECT Analysis controlled for Age, Sex, Vascular Comorbidity, Baseline Cognitive Performance time T0 (surgery) T1 (1 year) Successful AGing after Elective
    [Show full text]
  • Salience Network and Self-Control Rosa S
    bioRxiv preprint doi: https://doi.org/10.1101/129676; this version posted April 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Salience network dynamics underlying successful resistance of temptation Running Title: Salience network and self-control Rosa Steimke1,2,3,4,5* Jason S. Nomi5, Vince D, Calhoun6, Christine Stelzel1,7, Lena M. Paschke1, Robert Gaschler8, Henrik Walter1,3,#, Lucina Q. Uddin5,9*# 1Department of Psychiatry & Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany 2Department of Psychology, Technische Universität Dresden, Dresden, Germany 3Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany 4Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany 5Department of Psychology, University of Miami, Coral Gables, FL 6The Mind Research Network, Albuquerque, New Mexico, USA 7International Psychoanalytic University Berlin, Germany 8Department of Psychology, FernUniversität, Hagen, Hagen, Germany 9Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL # Equal contribution *Correspondence to: Dr. Rosa Steimke [email protected] or Dr. Lucina Q. Uddin Department of Psychology University of Miami P.O. Box 248185 Coral Gables, FL 33124, USA Email: [email protected] Phone: +1 305-284-3265 bioRxiv preprint doi: https://doi.org/10.1101/129676; this version posted April 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Salience Network and Self-control Abstract Self-control and the ability to resist temptation are critical for successful completion of long-term goals.
    [Show full text]
  • The Quest for Identifiability in Human Functional Connectomes
    www.nature.com/scientificreports OPEN The quest for identifability in human functional connectomes Enrico Amico1,2 & Joaquín Goñi1,2,3 The evaluation of the individual “fngerprint” of a human functional connectome (FC) is becoming Received: 5 January 2018 a promising avenue for neuroscientifc research, due to its enormous potential inherent to drawing Accepted: 9 April 2018 single subject inferences from functional connectivity profles. Here we show that the individual Published: xx xx xxxx fngerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a fnite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs through connectivity eigenmodes maximizes subject identifability across resting-state and all seven tasks evaluated. The identifability of the optimally reconstructed individual connectivity profles increases both at the global and edgewise level, also when the reconstruction is imposed on additional functional data of the subjects. Furthermore, reconstructed FC data provide more robust associations with task-behavioral measurements. Finally, we extend this approach to also map the most task- sensitive functional connections. Results show that is possible to maximize individual fngerprinting in the functional connectivity domain regardless of the task, a crucial next step in the area of brain connectivity towards individualized connectomics. Te explosion of publicly available neuroimaging datasets in the last years have provided an ideal benchmark for mapping functional and structural connections in the human brain. At the same time, quantitative analysis of connectivity patterns based on network science have become more commonly used to study the brain as a network1, giving rise to the area of research so called Brain Connectomics2,3.
    [Show full text]
  • Sex-Specific Differences in Resting-State Functional Connectivity
    www.nature.com/scientificreports OPEN Sex‑specifc diferences in resting‑state functional connectivity of large‑scale networks in postconcussion syndrome Reema Shaf1,2*, Adrian P. Crawley3,4, Maria Carmela Tartaglia5,6,7,8, Charles H. Tator4,6,7,9,10, Robin E. Green1,2,3,4,6, David J. Mikulis3,4,6 & Angela Colantonio1,2,11 Concussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network‑based connectivity post‑injury. To date, a signifcant knowledge gap remains regarding the sex‑specifc impact of concussion on resting state functional connectivity (rs‑FC). The aims of this study were to (1) investigate the injury‑ based rs‑FC diferences across three large‑scale neural networks and (2) explore the sex‑specifc impact of injury on network‑based connectivity. MRI data was collected from a sample of 80 concussed participants who fulflled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were signifcantly diferent from healthy controls across both salience and fronto‑parietal network nodes. More specifcally, distinct subnetwork components were identifed in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto‑parietal networks, when compared to the healthy controls. Node‑to‑region analyses showed sex‑specifc diferences across association cortices, however, driven by distinct networks.
    [Show full text]
  • Altered Structural Balance of Resting-State Networks in Autism
    www.nature.com/scientificreports OPEN Altered structural balance of resting‑state networks in autism Z. Moradimanesh1, R. Khosrowabadi1, M. Eshaghi Gordji1,2 & G. R. Jafari1,3* What makes a network complex, in addition to its size, is the interconnected interactions between elements, disruption of which inevitably results in dysfunction. Likewise, the brain networks’ complexity arises from interactions beyond pair connections, as it is simplistic to assume that in complex networks state of a link is independently determined only according to its two constituting nodes. This is particularly of note in genetically complex brain impairments, such as the autism spectrum disorder (ASD), which has a surprising heterogeneity in manifestations with no clear‑ cut neuropathology. Accordingly, structural balance theory (SBT) afrms that in real‑world signed networks, a link is remarkably infuenced by each of its two nodes’ interactions with the third node within a triadic interrelationship. Thus, it is plausible to ask whether ASD is associated with altered structural balance resulting from atypical triadic interactions. In other words, it is the abnormal interplay of positive and negative interactions that matters in ASD, besides and beyond hypo (hyper) pair connectivity. To address this question, we explore triadic interactions based on SBT in the weighted signed resting‑state functional magnetic resonance imaging networks of participants with ASD relative to healthy controls (CON). We demonstrate that balanced triads are overrepresented in the ASD and CON networks while unbalanced triads are underrepresented, providing frst‑time empirical evidence for the strong notion of structural balance on the brain networks. We further analyze the frequency and energy distributions of diferent triads and suggest an alternative description for the reduced functional integration and segregation in the ASD brain networks.
    [Show full text]
  • Functional Diversity of Brain Networks Supports Consciousness and Verbal
    www.nature.com/scientificreports OPEN Functional diversity of brain networks supports consciousness and verbal intelligence Received: 21 March 2017 Lorina Naci1, Amelie Haugg2, Alex MacDonald3, Mimma Anello4, Evan Houldin5, Accepted: 15 August 2018 Shakib Naqshbandi6, Laura E. Gonzalez-Lara5, Miguel Arango6, Christopher Harle6, Published: xx xx xxxx Rhodri Cusack1 & Adrian M. Owen5 How are the myriad stimuli arriving at our senses transformed into conscious thought? To address this question, in a series of studies, we asked whether a common mechanism underlies loss of information processing in unconscious states across diferent conditions, which could shed light on the brain mechanisms of conscious cognition. With a novel approach, we brought together for the frst time, data from the same paradigm—a highly engaging auditory-only narrative—in three independent domains: anesthesia-induced unconsciousness, unconsciousness after brain injury, and individual diferences in intellectual abilities during conscious cognition. During external stimulation in the unconscious state, the functional diferentiation between the auditory and fronto-parietal systems decreased signifcantly relatively to the conscious state. Conversely, we found that stronger functional diferentiation between these systems in response to external stimulation predicted higher intellectual abilities during conscious cognition, in particular higher verbal acuity scores in independent cognitive testing battery. These convergent fndings suggest that the responsivity of sensory
    [Show full text]
  • Modeling Functional Resting-State Brain Networks Through Neural Message Passing on the Human Connectome
    1 Modeling functional resting-state brain networks through neural message passing on the human connectome Julio A. Peraza-Goicoleaa, Eduardo Martínez-Montesb,c*, Eduardo Aubertb, Pedro A. Valdés-Hernándezd, and Roberto Muleta a Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, University of Havana, Havana, Cuba b Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba c Advanced Center for Electrical and Electronic Engineering (AC3E), Universidad Técnica Federico Santa María, Valparaíso, Chile d Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, USA *Corresponding author: Calle 190 #15202 entre Ave 25 y 27, Cubanacan, Playa, Habana 11600 Phone: +53 7 263 7252 E-mail: [email protected] (E. Martínez-Montes) 2 Abstract Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the brain, which are defined from the analysis of functional data lack a definitive justification consistent with the anatomical structure of the brain. In this work we show that a possible connection may naturally rest on the idea that information flows in the brain through a neural message-passing dynamics between macroscopic structures, like those defined by the human connectome (HC). In our model, each brain region in the HC is assumed to have a binary behavior (active or not), the strength of interactions among them is encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by a neural message-passing algorithm, Belief Propagation (BP), working near the critical point of the human connectome.
    [Show full text]
  • Large-Scale Dcms for Resting-State Fmri
    METHODS Large-scale DCMs for resting-state fMRI 1,2,3 1,4 1,5 6 1 Adeel Razi , Mohamed L. Seghier , Yuan Zhou , Peter McColgan , Peter Zeidman , 7 8 1,9 1 Hae-Jeong Park , Olaf Sporns , Geraint Rees , and Karl J. Friston 1The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom 2Monash Biomedical Imaging and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton, Australia 3Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan 4 Cognitive Neuroimaging Unit, Abu Dhabi, United Arab Emirates 5CAS Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China 6Huntington’s Disease Centre, Institute of Neurology, University College London, London, United Kingdom 7Department of Nuclear Medicine and BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea Downloaded from http://direct.mit.edu/netn/article-pdf/1/3/222/1092050/netn_a_00015.pdf by guest on 29 September 2021 8Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 9Institute of Cognitive Neuroscience, University College London, London, United Kingdom. an open access journal Keywords: Dynamic causal modeling, Effective connectivity, Functional connectivity, Resting state, fMRI, Graph theory, Bayesian inference, Large-scale networks ABSTRACT This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions.
    [Show full text]