Large-Scale Dcms for Resting-State Fmri
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The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults Yuan Zhou1,2,3,4, Karl J
Cerebral Cortex, February 2018;28: 726–737 doi: 10.1093/cercor/bhx307 Advance Access Publication Date: 17 November 2017 Original Article ORIGINAL ARTICLE The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults Yuan Zhou1,2,3,4, Karl J. Friston4, Peter Zeidman4, Jie Chen3,5, Shu Li1,2,3 and Adeel Razi4,6,7 1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China, 2Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China, 3Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China, 4The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK, 5CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China, 6Monash Biomedical Imaging and Monash Institute of Cognitive & Clinical Neurosciences, Monash University, Clayton 3800, Australia and 7Department of Electronic Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan Address correspondence to Yuan Zhou, CAS Key Laboratory of Behavioral Science Institute of Psychology, Chinese Academy of Sciences No. 16 Lincui Road, Chaoyang District, Beijing 100101, PR China. Email: [email protected]; Adeel Razi, The Wellcome Trust Centre for Neuroimaging, Instituteof Neurology, UCL Queen Square, London WC1N 3BG, UK. Email: [email protected]. Abstract An important characteristic of spontaneous brain activity is the anticorrelation between the core default network (cDN) and the dorsal attention network (DAN) and the salience network (SN). This anticorrelation may constitute a key aspect of functional anatomy and is implicated in several brain disorders. We used dynamic causal modeling to assess the hypothesis that a causal hierarchy underlies this anticorrelation structure, using resting-state fMRI of healthy adolescent and young adults (N = 404). -
Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: a Stochastic Dynamic Causal Modeling Study with Functional Magnetic Resonance Imaging
Schizophrenia Bulletin Advance Access published June 17, 2014 Schizophrenia Bulletin doi:10.1093/schbul/sbu080 Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging António J. Bastos-Leite*,1,6, Gerard R. Ridgway2,6, Celeste Silveira3,4,6, Andreia Norton5, Salomé Reis3, and Karl J. Friston2 1Department of Medical Imaging, Faculty of Medicine, University of Porto, Porto, Portugal; 2Wellcome Trust Centre for Neuroimaging, 3 Institute of Neurology, University College London, London, UK; Department of Psychiatry, Hospital de São João, Porto, Portugal; Downloaded from 4Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal; 5Hospital de Magalhães Lemos, Porto, Portugal 6These authors contributed equally to the article. *To whom correspondence should be addressed; Department of Medical Imaging, Faculty of Medicine, University of Porto, Alameda do Professor Hernâni Monteiro, 4200-319 Porto, Portugal; tel: +351938382287, fax: +351225500531, e-mail: [email protected] http://schizophreniabulletin.oxfordjournals.org/ We report the first stochastic dynamic causal modeling magnetic resonance imaging (fMRI)/resting (sDCM) study of effective connectivity within the default state/stochastic dynamic causal modeling (DCM) mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophre- Introduction nia—according to the Diagnostic and Statistic Manual Schizophrenia is a complex psychiatric disorder of of Mental Disorders, 4th edition, revised criteria—were unknown etiology, with significant clinical and patho- studied. Fifteen healthy control subjects (4 women, mean physiological heterogeneity, for which biomarkers are still age = 24.6 years, SD = 4) were included for comparison. -
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. -
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. -
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. -
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 -
The Salience Network and Its Functional Architecture in a Perceptual Decision: an Effective Connectivity Study
BRAIN CONNECTIVITY ORIGINAL ARTICLE Volume 5, Number XX, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/brain.2014.0282 The Salience Network and Its Functional Architecture in a Perceptual Decision: An Effective Connectivity Study Bidhan Lamichhane1 and Mukesh Dhamala1,2 Abstract The anterior insulae (INSs) are involved in accumulating sensory evidence in perceptual decision-making inde- pendent of the motor response, whereas the dorsal anterior cingulate cortex (dACC) is known to play a role in choosing appropriate behavioral responses. Recent evidence suggests that INSs and dACC are part of the salience network (SN), a key network known to be involved in decision-making and thought to be important for the co- ordination of behavioral responses. However, how these nodes in the SN contribute to the decision-making pro- cess from segregation of stimuli to the generation of an appropriate behavioral response remains unknown. In this study, the authors scanned 33 participants in functional magnetic resonance imaging and asked them to decide whether the presented pairs of audio (a beep of sound) and visual (a flash of light) stimuli were synchronous or asynchronous. Participants reported their perception with a button press. Stimuli were presented in block of eight pairs with a temporal lag (DT) between the first (audio) and the second (visual) stimulus in each pair. They used dynamic causal modeling (DCM) and the Bayesian model evidence technique to elucidate the func- tional architecture between the nodes of SN. Both the synchrony and the asynchrony perception resulted in strong activation in the SN. Most importantly, the DCM analyses demonstrated that the INSs were integrating as well as driving hubs in the SN. -
Disruptions in Effective Connectivity Within and Between Default Mode Network and Anterior Forebrain Mesocircuit in Prolonged Disorders of Consciousness
brain sciences Article Disruptions in Effective Connectivity within and between Default Mode Network and Anterior Forebrain Mesocircuit in Prolonged Disorders of Consciousness Sean Coulborn 1 , Chris Taylor 1, Lorina Naci 2 , Adrian M. Owen 3 and Davinia Fernández-Espejo 1,* 1 Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham B15 2TT, UK; [email protected] (S.C.); [email protected] (C.T.) 2 Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, D02 PN40 Dublin, Ireland; [email protected] 3 Brain and Mind Institute, Western University, London, ON N6A 5B7, Canada; [email protected] * Correspondence: [email protected] Abstract: Recent research indicates prolonged disorders of consciousness (PDOC) result from struc- tural and functional impairments to key cortical and subcortical networks, including the default mode network (DMN) and the anterior forebrain mesocircuit (AFM). However, the specific mech- anisms which underpin such impairments remain unknown. It is known that disruptions in the striatal-pallidal pathway can result in the over inhibition of the thalamus and lack of excitation to the cortex that characterizes PDOC. Here, we used spectral dynamic causal modelling and parametric Citation: Coulborn, S.; Taylor, C.; empirical Bayes on rs-fMRI data to assess whether DMN changes in PDOC are caused by disruptions Naci, L.; Owen, A.M.; in the AFM. PDOC patients displayed overall reduced coupling within the AFM, and specifically, Fernández-Espejo, D. Disruptions in decreased self-inhibition of the striatum, paired with reduced coupling from striatum to thalamus. Effective Connectivity within and This led to loss of inhibition from AFM to DMN, mostly driven by posterior areas including the between Default Mode Network and precuneus and inferior parietal cortex. -
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 -
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. -
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. -
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.