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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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2
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Lee J, Hussain S, Warnick R, Vannucci M, Menchaca I, Seitz AR, Hu X, Peters MAK, Guindani M. A predictor-informed multi-subject bayesian approach for dynamic functional connectivity. PLoS One 2024; 19:e0298651. [PMID: 38753655 PMCID: PMC11098372 DOI: 10.1371/journal.pone.0298651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/30/2024] [Indexed: 05/18/2024] Open
Abstract
Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.
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Affiliation(s)
- Jaylen Lee
- Department of Statistics, University of California, Irvine, Irvine, California, United States of America
| | - Sana Hussain
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Ryan Warnick
- Microsoft Security Research, Microsoft, Redmond, Washington, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Isaac Menchaca
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Aaron R. Seitz
- Department of Psychology, University of California, Riverside, Riverside, California, United States of America
| | - Xiaoping Hu
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Megan A. K. Peters
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
- Department of Cognitive Sciences, University of California, Irvine, Irvine, California, United States of America
| | - Michele Guindani
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, United States of America
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3
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Chung MK, Huang SG, Carroll IC, Calhoun VD, Goldsmith HH. Topological state-space estimation of functional human brain networks. PLoS Comput Biol 2024; 20:e1011869. [PMID: 38739671 PMCID: PMC11115255 DOI: 10.1371/journal.pcbi.1011869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 05/23/2024] [Accepted: 01/29/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.
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Affiliation(s)
- Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America
| | | | - Ian C. Carroll
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, United States of America
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States of America
| | - H. Hill Goldsmith
- Department of Psychology & Waisman Center, University of Wisconsin, Madison, Wisconsin, United States of America
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4
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Yates TS, Ellis CT, Turk-Browne NB. Functional networks in the infant brain during sleep and wake states. Cereb Cortex 2023; 33:10820-10835. [PMID: 37718160 DOI: 10.1093/cercor/bhad327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023] Open
Abstract
Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.
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Affiliation(s)
- Tristan S Yates
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Cameron T Ellis
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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5
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Bukhari H, Su C, Dhamala E, Gu Z, Jamison K, Kuceyeski A. Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Hum Brain Mapp 2023; 44:3541-3554. [PMID: 37042411 PMCID: PMC10203814 DOI: 10.1002/hbm.26296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/10/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023] Open
Abstract
Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
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Affiliation(s)
- Hussain Bukhari
- Department of NeuroscienceWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chang Su
- Department of BiostatisticsYale UniversityNew HavenConnecticutUSA
| | - Elvisha Dhamala
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Zijin Gu
- Department of Electrical and Computer EngineeringCornell UniversityIthacaNew YorkUSA
| | - Keith Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
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6
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Zhang S, Goodale SE, Gold BP, Morgan VL, Englot DJ, Chang C. Vigilance associates with the low-dimensional structure of fMRI data. Neuroimage 2023; 267:119818. [PMID: 36535323 PMCID: PMC10074161 DOI: 10.1016/j.neuroimage.2022.119818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA.
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin P Gold
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dario J Englot
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catie Chang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
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7
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Riedel P, Lee J, Watson CG, Jimenez AM, Reavis EA, Green MF. Reorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder. Psychiatry Res Neuroimaging 2022; 327:111556. [PMID: 36327867 PMCID: PMC10611423 DOI: 10.1016/j.pscychresns.2022.111556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/13/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Functional connectome organization is altered in schizophrenia (SZ) and bipolar disorder (BD). However, it remains unclear whether network reorganization during a task relative to rest is also altered in these disorders. This study examined connectome organization in patients with SZ (N = 43) and BD (N = 42) versus healthy controls (HC; N = 39) using fMRI data during a visual object-perception task and at rest. Graph analyses were conducted for the whole-brain network using indices selected a priori: three reflecting network segregation (clustering coefficient, local efficiency, modularity), two reflecting integration (characteristic path length, global efficiency). Group differences were limited to network segregation and were more evident in SZ (clustering coefficient, modularity) than in BD (clustering coefficient) compared to HC. State differences were found across groups for segregation (local efficiency) and integration (characteristic path length). There was no group-by-state interaction for any graph index. In summary, aberrant network organization compared to HC was confirmed, and was more evident in SZ than in BD. Yet, reorganization was largely intact in both disorders. These findings help to constrain models of dysconnection in SZ and BD, suggesting that the extent of functional dysconnectivity in these disorders tends to persist across changes in mental state.
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Affiliation(s)
- Philipp Riedel
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Würzburger Straße 35, Dresden 01187, Germany.
| | - Junghee Lee
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Behavioral Neurobiology, School of Medicine, The University of Alabama at Birmingham, SC 560, 1720 2nd Ave S, Birmingham, AL 35294-0017, USA
| | - Christopher G Watson
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Amy M Jimenez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Eric A Reavis
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Michael F Green
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
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8
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Gonzalez-Castillo J, Fernandez IS, Handwerker DA, Bandettini PA. Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness. Neuroimage 2022; 259:119424. [PMID: 35781079 PMCID: PMC9377091 DOI: 10.1016/j.neuroimage.2022.119424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/16/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022] Open
Abstract
Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD.
| | - Isabel S Fernandez
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; Functional MRI Core, National Institutes of Health, Bethesda, MD
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9
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Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EM, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. Neuroimage 2022; 258:119364. [PMID: 35690257 PMCID: PMC9341222 DOI: 10.1016/j.neuroimage.2022.119364] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.
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Affiliation(s)
- Kangjoo Lee
- Department of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States.
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University
School of Medicine, New Haven, CT 06520, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States
| | | | - Fuyuze Tokoglu
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,The Child Study Center, Yale University School of Medicine,
New Haven, CT 06520, United States,Department of Statistics and Data Science, Yale University,
New Haven, CT 06511, United States
| | - Evelyn M.R. Lake
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - R. Todd Constable
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,Department of Neurosurgery, Yale University School of
Medicine, New Haven, CT 06520, United States
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10
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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11
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Kraft D, Fiebach CJ. Probing the association between resting-state brain network dynamics and psychological resilience. Netw Neurosci 2022; 6:175-195. [PMID: 36605891 PMCID: PMC9810279 DOI: 10.1162/netn_a_00216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/08/2021] [Indexed: 01/07/2023] Open
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
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Affiliation(s)
- Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany,* Corresponding Author:
| | - Christian J. Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany,Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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12
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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13
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Martin CG, He BJ, Chang C. State-related neural influences on fMRI connectivity estimation. Neuroimage 2021; 244:118590. [PMID: 34560268 PMCID: PMC8815005 DOI: 10.1016/j.neuroimage.2021.118590] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/11/2021] [Accepted: 09/16/2021] [Indexed: 12/01/2022] Open
Abstract
The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.
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Affiliation(s)
- Caroline G Martin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Departments of Neurology, Neuroscience & Physiology, and Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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14
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Rué-Queralt J, Stevner A, Tagliazucchi E, Laufs H, Kringelbach ML, Deco G, Atasoy S. Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep. Commun Biol 2021; 4:854. [PMID: 34244598 PMCID: PMC8270946 DOI: 10.1038/s42003-021-02369-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.
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Affiliation(s)
- Joan Rué-Queralt
- grid.5612.00000 0001 2172 2676Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Angus Stevner
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Enzo Tagliazucchi
- grid.7345.50000 0001 0056 1981Instituto de Física de Buenos Aires and Physics Deparment (University of Buenos Aires), Buenos Aires, Argentina
| | - Helmut Laufs
- grid.7839.50000 0004 1936 9721Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany ,grid.9764.c0000 0001 2153 9986Department of Neurology, University Hospital Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Morten L. Kringelbach
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- grid.5612.00000 0001 2172 2676Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain ,grid.425902.80000 0000 9601 989XInstitució Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Spain ,grid.419524.f0000 0001 0041 5028Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.1002.30000 0004 1936 7857School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Selen Atasoy
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
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15
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Mueller SG, Muller AM. Brainstem Dysfunction in Healthy Aging. Neuroimage 2021; 238:118241. [PMID: 34116149 DOI: 10.1016/j.neuroimage.2021.118241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/29/2021] [Accepted: 06/04/2021] [Indexed: 11/16/2022] Open
Abstract
The brainstem controls sub-cortical and cortical activity and influences the processing of incoming information. The goal of this study was to characterize age related alterations of brainstem-brain interactions during different brain states detected by dynamic analysis of task-free fMRI. 79 young (20-40 years) and 51 older adults (55-80 years) were studied. Internal brainstem structures were segmented using a new multi-contrast segmentation approach. Brain and brainstem gray matter segmentations were warped onto a population template. The ICV-corrected Jacobian determinants were converted into z-score maps and the means from 420 cortical/subcortical/brainstem rois extracted. The fMRI was preprocessed in SPM12/Conn18 and the BOLD signal from 420 cortical/subcortical/brainstem rois extracted. A dynamic task-free analysis approach based on hierarchical cluster analysis was used to identify 15 brain states that were characterized using graph analysis (strength, diversity, modularity). Kruskal-Wallis tests and Spearman correlations were used for statistical analysis. One brain state (cluster 21) occurred more often in older adults (p=0.008). It was characterized by a lower mean modular strength and brainstem-cortical strength in older adults compared to younger adults. Global age related gray matter differences were positively correlated with brain state 21's modular strength. Furthermore, brain state 21 duration was negatively correlated with working memory (r = -0.28, p=0.002). The findings suggest an age related weakening of the within and between network synchronization at the brainstem level during brain state 21 in older adults that negatively affects cortical and subcortical synchronization and working memory performance.
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Affiliation(s)
- S G Mueller
- Dept. of Radiology, University of California, San Francisco, CA.
| | - A M Muller
- Dept. of Radiology, University of California, San Francisco, CA.
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16
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Marchitelli R, Paillère-Martinot ML, Bourvis N, Guerin-Langlois C, Kipman A, Trichard C, Douniol M, Stordeur C, Galinowski A, Filippi I, Bertschy G, Weibel S, Granger B, Limosin F, Cohen D, Martinot JL, Artiges E. Dynamic functional connectivity in adolescence-onset major depression: relationships with severity and symptom dimensions. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:385-396. [PMID: 34051395 DOI: 10.1016/j.bpsc.2021.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/14/2021] [Accepted: 05/14/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND The spatial functional chronnectome is an innovative mathematical model designed to capture dynamic features in the organization of brain function derived from resting-state functional magnetic resonance imaging (rs-fMRI) data. Measurements of dynamic functional connectivity (dFC) have been developed from this model to quantify the brain dynamical self-reconfigurations at different spatial and temporal scales. This study examined whether two spatiotemporal dFC quantifications were linked to late adolescence-onset major depressive disorder (AO-MDD), and scaled with depression and symptom severity measured with the Montgomery-Asberg depression rating scale (MADRS) Methods: Thirty-five AO-MDD patients (21±6y) and fifty-three age- and gender-matched healthy young participants (20±3y) underwent 3T MRI structural and rs-fMRI acquisitions. The chronnectome here comprised seven individualized functional networks portrayed along 132 temporal overlapping windows, each framing 110s of resting brain activity Results: Based on voxelwise analyses, AO-MDD patients demonstrated significantly reduced temporal variability within the bilateral prefrontal cortex in five functional networks including the limbic network, the default-mode network (DMN) and frontoparietal network (FPN). Furthermore, the limbic network appeared to be particularly involved in this sample, and was associated with MADRS scores, and its progressive dynamic inflexibility was linked to sadness. DMN and FPN dynamics scaled with negative thoughts and neurovegetative symptoms, respectively Conclusions: This triple-network imbalance could delay spatiotemporal integration, while across-subject symptom variability would be network-specific. Therefore, the present approach supports that brain network dynamics underlie patients' symptom heterogeneity in AO-MDD.
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Affiliation(s)
- Rocco Marchitelli
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
| | - Marie-Laure Paillère-Martinot
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France; Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, Paris, France
| | - Nadège Bourvis
- Maison des Adolescents du Var (MDA83), Pôle de Psychiatrie Infanto-Juvénile, Centre Hospitalier Intercommunal Toulon - la Seyne sur mer, Toulon, France
| | - Christophe Guerin-Langlois
- Department of Psychiatry and Addictology, Hôpital Corentin Celton, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
| | - Amélie Kipman
- Psychiatry Department, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Christian Trichard
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France; Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Marie Douniol
- Centre médico-psychologique pour adolescents, Sceaux, France
| | - Coline Stordeur
- Service de Psychiatrie de l'Enfant et de l'Adolescent, Hôpital Robert Debré, Paris, France
| | - André Galinowski
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Irina Filippi
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France
| | - Gilles Bertschy
- Psychiatry Department, Hôpital Civil de Strasbourg, Strasbourg University, Strasbourg, France; Institut National de la Santé et de la Recherche Médicale U1114, Strasbourg University, Strasbourg, France
| | - Sébastien Weibel
- Psychiatry Department, Hôpital Civil de Strasbourg, Strasbourg University, Strasbourg, France; Institut National de la Santé et de la Recherche Médicale U1114, Strasbourg University, Strasbourg, France
| | - Bernard Granger
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France; Psychiatry Department, Tarnier Hospital, Assistance Publique-Hôpitaux de Paris, University Paris Descartes, Paris, France
| | - Frédéric Limosin
- Department of Psychiatry and Addictology, Hôpital Corentin Celton, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France
| | - David Cohen
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, Paris, France
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France.
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale U1299 "Trajectoires développementales & psychiatrie", Centre Borelli, Ecole Normale Supérieure Paris-Saclay, University Paris-Saclay/Centre National de la Recherche Scientifique, Gif-sur-Yvette, France; Psychiatry Department, EPS Barthélémy Durand, Etampes, France
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17
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Soon CS, Vinogradova K, Ong JL, Calhoun VD, Liu T, Zhou JH, Ng KK, Chee MWL. Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes. Neuroimage 2021; 237:118129. [PMID: 33951513 DOI: 10.1016/j.neuroimage.2021.118129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 04/04/2021] [Accepted: 04/28/2021] [Indexed: 01/16/2023] Open
Abstract
Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.
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Affiliation(s)
- Chun Siong Soon
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore.
| | - Ksenia Vinogradova
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, USA
| | - Thomas Liu
- UCSD Center for Functional MRI and Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, USA
| | - Juan Helen Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Imaging, Yong Loo Lin School of Medicine, National Unviersity of Singapore, Singapore.
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18
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Chang C, Chen JE. Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021; 18. [PMID: 34095643 DOI: 10.1016/j.cobme.2021.100279] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Advances in the acquisition and analysis of functional magnetic resonance imaging (fMRI) data are revealing increasingly rich spatiotemporal structure across the human brain. Nonetheless, uncertainty surrounding the origins of fMRI hemodynamic signals, and in the link between large-scale fMRI patterns and ongoing functional states, presently limits the neurobiological conclusions one can draw from fMRI alone. Electroencephalography (EEG) provides complementary information about neural electrical activity and state change, and simultaneously acquiring EEG together with fMRI presents unique opportunities for studying large-scale brain activity and gaining more information from fMRI itself. Here, we discuss recent progress in the use of concurrent EEG-fMRI to enrich the investigation of neural and physiological states and clarify the origins of fMRI hemodynamic signals. Throughout, we outline perspectives on future directions and open challenges.
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Affiliation(s)
- Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jingyuan E Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
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19
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Moguilner S, García AM, Perl YS, Tagliazucchi E, Piguet O, Kumfor F, Reyes P, Matallana D, Sedeño L, Ibáñez A. Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study. Neuroimage 2021; 225:117522. [PMID: 33144220 PMCID: PMC7832160 DOI: 10.1016/j.neuroimage.2020.117522] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 10/14/2020] [Accepted: 10/27/2020] [Indexed: 02/07/2023] Open
Abstract
From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.
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Affiliation(s)
- Sebastian Moguilner
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; Fundación Escuela de Medicina Nuclear (FUESMEN) and Comisión Nacional de Energía Atómica (CNEA), Buenos Aires, Argentina
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Faculty of Education, National University of Cuyo (UNCuyo), Mendoza, Argentina
| | - Yonatan Sanz Perl
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Department of Physics, University of Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Department of Physics, University of Buenos Aires, Argentina
| | - Olivier Piguet
- School of Psychology and Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Fiona Kumfor
- School of Psychology and Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Pablo Reyes
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana; Mental Health Unit, Hospital Universitario Fundación Santa Fe, Bogotá, Colombia, Hospital Universitario San Ignacio. Bogotá, Colombia
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana; Mental Health Unit, Hospital Universitario Fundación Santa Fe, Bogotá, Colombia, Hospital Universitario San Ignacio. Bogotá, Colombia
| | - Lucas Sedeño
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
| | - Agustín Ibáñez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, US; & Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad de San Andrés, Buenos Aires, Argentina; Universidad Autónoma del Caribe, Barranquilla, Colombia; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile.
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20
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Tarun A, Wainstein-Andriano D, Sterpenich V, Bayer L, Perogamvros L, Solms M, Axmacher N, Schwartz S, Van De Ville D. NREM sleep stages specifically alter dynamical integration of large-scale brain networks. iScience 2020; 24:101923. [PMID: 33409474 PMCID: PMC7773861 DOI: 10.1016/j.isci.2020.101923] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/07/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023] Open
Abstract
Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.
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Affiliation(s)
- Anjali Tarun
- École Polytechnique Fédérale de Lausanne (Institute of Bioengineering, Medical Image Processing Laboratory), Geneva 1202, Switzerland.,University of Geneva (Department of Radiology and Medical Informatics), Geneva 1202, Switzerland
| | - Danyal Wainstein-Andriano
- University of Cape Town (Psychology Department, Faculty of Humanities), Cape Town 7701, South Africa.,Ruhr-Universität Bochum (Institute of Cognitive Neuroscience, Faculty of Psychology), Ruhr 44801, Germany
| | - Virginie Sterpenich
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland
| | - Laurence Bayer
- University Hospitals of Geneva (Center for Sleep Medicine, Department of Medicine), Geneva 1202, Switzerland
| | - Lampros Perogamvros
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland.,University Hospitals of Geneva (Center for Sleep Medicine, Department of Medicine), Geneva 1202, Switzerland
| | - Mark Solms
- University of Cape Town (Psychology Department, Faculty of Humanities), Cape Town 7701, South Africa
| | - Nikolai Axmacher
- Ruhr-Universität Bochum (Institute of Cognitive Neuroscience, Faculty of Psychology), Ruhr 44801, Germany
| | - Sophie Schwartz
- University of Geneva, (Department of Basic Neurosciences), Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- École Polytechnique Fédérale de Lausanne (Institute of Bioengineering, Medical Image Processing Laboratory), Geneva 1202, Switzerland.,University of Geneva (Department of Radiology and Medical Informatics), Geneva 1202, Switzerland
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21
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Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals. Neuroimage 2020; 226:117470. [PMID: 33137478 DOI: 10.1016/j.neuroimage.2020.117470] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 09/07/2020] [Accepted: 10/14/2020] [Indexed: 02/05/2023] Open
Abstract
During the sleep-wake cycle, the brain undergoes profound dynamical changes, which manifest subjectively as transitions between conscious experience and unconsciousness. Yet, neurophysiological signatures that can objectively distinguish different consciousness states based are scarce. Here, we show that differences in the level of brain-wide signals can reliably distinguish different stages of sleep and anesthesia from the awake state in human and monkey fMRI resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as functional connectivity, structure-function relationship, integration and segregation across vigilance states. We demonstrate that the awake brain is close to a Hopf bifurcation, which naturally coincides with the emergence of globally correlated fMRI signals. Furthermore, simulating lesions of individual brain areas highlights the importance of connectivity hubs in the posterior brain and subcortical nuclei for maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.
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22
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Damaraju E, Tagliazucchi E, Laufs H, Calhoun VD. Connectivity dynamics from wakefulness to sleep. Neuroimage 2020; 220:117047. [PMID: 32562782 DOI: 10.1016/j.neuroimage.2020.117047] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 06/09/2020] [Indexed: 11/16/2022] Open
Abstract
Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.
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Affiliation(s)
- Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
| | - Enzo Tagliazucchi
- Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Germany; Department of Neurology, Christian Albrechts University, Kiel, Germany
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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23
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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy. J Neurosci 2020; 40:5572-5588. [PMID: 32513827 PMCID: PMC7363471 DOI: 10.1523/jneurosci.0905-19.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/18/2022] Open
Abstract
Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas. SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC.
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24
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Betzel RF, Byrge L, Esfahlani FZ, Kennedy DP. Temporal fluctuations in the brain's modular architecture during movie-watching. Neuroimage 2020; 213:116687. [PMID: 32126299 PMCID: PMC7165071 DOI: 10.1016/j.neuroimage.2020.116687] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/07/2020] [Accepted: 02/24/2020] [Indexed: 11/26/2022] Open
Abstract
Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA; Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, USA
| | | | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA
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25
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Liu TT, Falahpour M. Vigilance Effects in Resting-State fMRI. Front Neurosci 2020; 14:321. [PMID: 32390792 PMCID: PMC7190789 DOI: 10.3389/fnins.2020.00321] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 03/18/2020] [Indexed: 12/02/2022] Open
Abstract
Measures of resting-state functional magnetic resonance imaging (rsfMRI) activity have been shown to be sensitive to cognitive function and disease state. However, there is growing evidence that variations in vigilance can lead to pronounced and spatially widespread differences in resting-state brain activity. Unless properly accounted for, differences in vigilance can give rise to changes in resting-state activity that can be misinterpreted as primary cognitive or disease-related effects. In this paper, we examine in detail the link between vigilance and rsfMRI measures, such as signal variance and functional connectivity. We consider how state changes due to factors such as caffeine and sleep deprivation affect both vigilance and rsfMRI measures and review emerging approaches and methodological challenges for the estimation and interpretation of vigilance effects.
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Affiliation(s)
- Thomas T. Liu
- Center for Functional MRI, University of California, San Diego, La Jolla, CA, United States
- Departments of Radiology, Psychiatry, and Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Maryam Falahpour
- Center for Functional MRI, University of California, San Diego, La Jolla, CA, United States
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26
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Pasquini L, Toller G, Staffaroni A, Brown JA, Deng J, Lee A, Kurcyus K, Shdo SM, Allen I, Sturm VE, Cobigo Y, Borghesani V, Battistella G, Gorno-Tempini ML, Rankin KP, Kramer J, Rosen HH, Miller BL, Seeley WW. State and trait characteristics of anterior insula time-varying functional connectivity. Neuroimage 2020; 208:116425. [PMID: 31805382 PMCID: PMC7225015 DOI: 10.1016/j.neuroimage.2019.116425] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/28/2019] [Accepted: 11/30/2019] [Indexed: 12/12/2022] Open
Abstract
The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.
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Affiliation(s)
- Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Gianina Toller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Adam Staffaroni
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jersey Deng
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Alex Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katarzyna Kurcyus
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Isabel Allen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Virginia E Sturm
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Yann Cobigo
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Giovanni Battistella
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joel Kramer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Howard H Rosen
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Memory and Aging Center, Department of Pathology, University of California, San Francisco, CA, USA.
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27
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Huang SG, Samdin SB, Ting CM, Ombao H, Chung MK. Statistical model for dynamically-changing correlation matrices with application to brain connectivity. J Neurosci Methods 2020; 331:108480. [PMID: 31760059 PMCID: PMC7739896 DOI: 10.1016/j.jneumeth.2019.108480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 10/22/2019] [Indexed: 01/26/2023]
Abstract
BACKGROUND Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However, the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI. NEW METHOD To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm. RESULTS The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified. COMPARISON WITH EXISTING METHODS We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states. CONCLUSIONS We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.
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Affiliation(s)
- Shih-Gu Huang
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA
| | - S Balqis Samdin
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Chee-Ming Ting
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; School of Biomedical Engineering & Health Sciences, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53706, USA.
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28
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 279] [Impact Index Per Article: 69.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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29
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Kumral D, Şansal F, Cesnaite E, Mahjoory K, Al E, Gaebler M, Nikulin VV, Villringer A. BOLD and EEG signal variability at rest differently relate to aging in the human brain. Neuroimage 2019; 207:116373. [PMID: 31759114 DOI: 10.1016/j.neuroimage.2019.116373] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/17/2019] [Accepted: 11/17/2019] [Indexed: 01/22/2023] Open
Abstract
Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability - identified with either hemodynamic or electrophysiological methods - reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 ± 3 years, N = 135) and older (67 ± 4 years, N = 54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15-25 Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.
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Affiliation(s)
- D Kumral
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - F Şansal
- International Graduate Program Medical Neurosciences, Charité-Universitätsmedizin, Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - E Cesnaite
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - K Mahjoory
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - E Al
- MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - M Gaebler
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - V V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité Universitätsmedizin Berlin, Berlin, Germany; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; MindBrainBody Institute at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
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30
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Özbay PS, Chang C, Picchioni D, Mandelkow H, Chappel-Farley MG, van Gelderen P, de Zwart JA, Duyn J. Sympathetic activity contributes to the fMRI signal. Commun Biol 2019; 2:421. [PMID: 31754651 PMCID: PMC6861267 DOI: 10.1038/s42003-019-0659-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/21/2019] [Indexed: 12/15/2022] Open
Abstract
The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.
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Affiliation(s)
- Pinar Senay Özbay
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Dante Picchioni
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | - Hendrik Mandelkow
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Peter van Gelderen
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
| | | | - Jeff Duyn
- Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD USA
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Soehner AM, Chase HW, Bertocci M, Greenberg T, Stiffler R, Lockovich JC, Aslam HA, Graur S, Bebko G, Phillips ML. Unstable wakefulness during resting-state fMRI and its associations with network connectivity and affective psychopathology in young adults. J Affect Disord 2019; 258:125-132. [PMID: 31401540 PMCID: PMC6710159 DOI: 10.1016/j.jad.2019.07.066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 07/19/2019] [Accepted: 07/29/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND Drifts between wakefulness and sleep are common during resting state functional MRI (rsfMRI). Among healthy adults, within-scanner sleep can impact functional connectivity of default mode (DMN), task-positive (TPN), and thalamo-cortical networks. Because dysfunctional arousal states (i.e., sleepiness, sleep disturbance) are common in affective disorders, individuals with affective psychopathology may be more prone to unstable wakefulness during rsfMRI, hampering the estimation of clinically meaningful functional connectivity biomarkers. METHODS A transdiagnostic sample of 150 young adults (68 psychologically distressed; 82 psychiatrically healthy) completed rsfMRI and reported whether they experienced within-scanner sleep. Symptom scales were reduced into depression/anxiety and mania proneness dimensions using principal component analysis. We evaluated associations between within-scanner sleep, clinical status, and functional connectivity of the DMN, TPN, and thalamus. RESULTS Within-scanner sleep during rsfMRI was reported by 44% of participants (n = 66) but was unrelated to psychiatric diagnoses or mood symptom severity (p-values > 0.05). Across all participants, self-reported within-scanner sleep was associated with connectivity signatures akin to objectively-assessed sleep, including lower within-DMN connectivity, lower DMN-TPN anti-correlation, and altered thalamo-cortical connectivity (p < 0.05, corrected). Among participants reporting sustained wakefulness (n = 84), depression/anxiety severity positively associated with averaged DMN-TPN connectivity and mania proneness negatively associated with averaged thalamus-DMN connectivity (p-values < 0.05). Both relationships were attenuated and became non-significant when participants reporting within-scanner sleep were included (p-values > 0.05). LIMITATIONS Subjective report of within-scanner sleep. CONCLUSIONS Findings implicate within-scanner sleep as a source of variance in network connectivity; careful monitoring and correction for within-scanner sleep may enhance our ability to characterize network signatures underlying affective psychopathology.
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Affiliation(s)
| | | | | | | | | | | | | | - Simona Graur
- University of Pittsburgh, Department of Psychiatry
| | - Genna Bebko
- University of Pittsburgh, Department of Psychiatry
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Vigilance declines following sleep deprivation are associated with two previously identified dynamic connectivity states. Neuroimage 2019; 200:382-390. [DOI: 10.1016/j.neuroimage.2019.07.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/26/2019] [Accepted: 07/01/2019] [Indexed: 11/20/2022] Open
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33
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Zhou S, Zou G, Xu J, Su Z, Zhu H, Zou Q, Gao JH. Dynamic functional connectivity states characterize NREM sleep and wakefulness. Hum Brain Mapp 2019; 40:5256-5268. [PMID: 31444893 PMCID: PMC6865216 DOI: 10.1002/hbm.24770] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/31/2019] [Accepted: 08/13/2019] [Indexed: 12/18/2022] Open
Abstract
According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow‐wave activity. We collected both eyes‐closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow‐wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.
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Affiliation(s)
- Shuqin Zhou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Guangyuan Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Jing Xu
- Laboratory of Applied Brain and Cognitive Sciences, College of International Business, Shanghai International Studies University, Shanghai, China
| | - Zihui Su
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Huaiqiu Zhu
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China.,Shenzhen Institute of Neuroscience, Shenzhen, China
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34
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Marquetand J, Vannoni S, Carboni M, Li Hegner Y, Stier C, Braun C, Focke NK. Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics. Brain Connect 2019; 9:539-553. [PMID: 31115272 DOI: 10.1089/brain.2019.0662] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.
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Affiliation(s)
- Justus Marquetand
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Silvia Vannoni
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,MEG-Center, University of Tübingen, Tübingen, Germany.,Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neurosciences (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Margherita Carboni
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Yiwen Li Hegner
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,MEG-Center, University of Tübingen, Tübingen, Germany
| | - Christina Stier
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany
| | | | - Niels K Focke
- Department of Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany
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35
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Filippi M, Spinelli EG, Cividini C, Agosta F. Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings. Front Neurosci 2019; 13:657. [PMID: 31281241 PMCID: PMC6596427 DOI: 10.3389/fnins.2019.00657] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 06/07/2019] [Indexed: 12/12/2022] Open
Abstract
In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Edoardo G Spinelli
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
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36
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Ferré P, Benhajali Y, Steffener J, Stern Y, Joanette Y, Bellec P. Resting-state and Vocabulary Tasks Distinctively Inform On Age-Related Differences in the Functional Brain Connectome. LANGUAGE, COGNITION AND NEUROSCIENCE 2019; 34:949-972. [PMID: 31457069 PMCID: PMC6711486 DOI: 10.1080/23273798.2019.1608072] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 03/05/2019] [Indexed: 05/23/2023]
Abstract
Most of the current knowledge about age-related differences in brain neurofunctional organization stems from neuroimaging studies using either a "resting state" paradigm, or cognitive tasks for which performance decreases with age. However, it remains to be known if comparable age-related differences are found when participants engage in cognitive activities for which performance is maintained with age, such as vocabulary knowledge tasks. A functional connectivity analysis was performed on 286 adults ranging from 18 to 80 years old, based either on a resting state paradigm or when engaged in vocabulary tasks. Notable increases in connectivity of regions of the language network were observed during task completion. Conversely, only age-related decreases were observed across the whole connectome during resting-state. While vocabulary accuracy increased with age, no interaction was found between functional connectivity, age and task accuracy or proxies of cognitive reserve, suggesting that older individuals typically benefits from semantic knowledge accumulated throughout one's life trajectory, without the need for compensatory mechanisms.
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Affiliation(s)
- Perrine Ferré
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Yassine Benhajali
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Jason Steffener
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
- PERFORM Center, Concordia University
- Interdisciplinary School of Health Sciences, University of Ottawa, 200 Lees, Lees Campus, Office # E-250C, Ottawa, Ontario. K1S 5S9, CANADA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Columbia University, 710 W 168th St, New York, NY 10032, USA
| | - Yves Joanette
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
| | - Pierre Bellec
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Université de Montréal, 4545 Queen Mary Road, Montréal, Qc, H3W 1W3, CANADA
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37
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Kung YC, Li CW, Chen S, Chen SCJ, Lo CYZ, Lane TJ, Biswal B, Wu CW, Lin CP. Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration. Hum Brain Mapp 2019; 40:3192-3202. [PMID: 30941797 DOI: 10.1002/hbm.24590] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 03/05/2019] [Accepted: 03/11/2019] [Indexed: 02/01/2023] Open
Abstract
Nonrapid eye movement (NREM) sleep is associated with fading consciousness in humans. Recent neuroimaging studies have demonstrated the spatiotemporal alterations of the brain functional connectivity (FC) in NREM sleep, suggesting the changes of information integration in the sleeping brain. However, the common stationarity assumption in FC does not satisfactorily explain the dynamic process of information integration during sleep. The dynamic FC (dFC) across brain networks is speculated to better reflect the time-varying information propagation during sleep. Accordingly, we conducted simultaneous EEG-fMRI recordings involving 12 healthy men during sleep and observed dFC across sleep stages using the sliding-window approach. We divided dFC into two aspects: mean dFC (dFCmean ) and variance dFC (dFCvar ). A high dFCmean indicates stable brain network integrity, whereas a high dFCvar indicates instability of information transfer within and between functional networks. For the network-based dFC, the dFCvar were negatively correlated with the dFCmean across the waking and three NREM sleep stages. As sleep deepened, the dFCmean decreased (N0~N1 > N2 > N3), whereas the dFCvar peaked during the N2 stage (N0~N1 < N3 < N2). The highest dFCvar during the N2 stage indicated the unstable synchronizations across the entire brain. In the N3 stage, the overall disrupted network integration was observed through the lowest dFCmean and elevated dFCvar, compared with N0 and N1. Conclusively, when the network specificity (dFCmean ) breaks down, the consciousness dissipates with increasing variability of information exchange (dFCvar ).
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Affiliation(s)
- Yi-Chia Kung
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Shuo Chen
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Sharon Chia-Ju Chen
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Yi Z Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey.,Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang-Ho Hospital, New Taipei, Taiwan.,Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
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38
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Fede SJ, Grodin EN, Dean SF, Diazgranados N, Momenan R. Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users. Neuroimage Clin 2019; 22:101782. [PMID: 30921611 PMCID: PMC6438989 DOI: 10.1016/j.nicl.2019.101782] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/07/2019] [Accepted: 03/14/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences. METHODS Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24). RESULTS The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model. CONCLUSIONS These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
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Affiliation(s)
- Samantha J Fede
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
| | - Erica N Grodin
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Sarah F Dean
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
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39
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Stevner ABA, Vidaurre D, Cabral J, Rapuano K, Nielsen SFV, Tagliazucchi E, Laufs H, Vuust P, Deco G, Woolrich MW, Van Someren E, Kringelbach ML. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun 2019; 10:1035. [PMID: 30833560 PMCID: PMC6399232 DOI: 10.1038/s41467-019-08934-3] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 02/11/2019] [Indexed: 12/02/2022] Open
Abstract
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep. Sleep is composed of a number of different stages, each associated with a different pattern of brain activity. Here, using a data-driven Hidden Markov Model (HMM) of fMRI data, the authors discover a more complex set of neural activity states underlying the conventional stages of non-REM sleep.
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Affiliation(s)
- A B A Stevner
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK. .,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark. .,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.
| | - D Vidaurre
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - J Cabral
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
| | - K Rapuano
- Department of Psychological and Brain Sciences, Dartmouth College, 03755, Hanover, NH, USA
| | - S F V Nielsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark
| | - E Tagliazucchi
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - H Laufs
- Department of Neurology, University Hospital Schleswig Holstein, Christian-Alrbrechts-Universität, 24105, Kiel, Germany.,Department of Neurology and Brain Imaging Center, Goethe University, 60528, Frankfurt am Main, Germany
| | - P Vuust
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC, 3800, Australia
| | - M W Woolrich
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK
| | - E Van Someren
- Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, The Netherlands.,Departments of Integrative Neurophysiology and Psychiatry GGZ-InGeest, Amsterdam Neuroscience, VU University and Medical Center, 1081 HV, Amsterdam, The Netherlands
| | - M L Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, OX3 7JX, Oxford, UK.,Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, 8000, Aarhus, Denmark.,Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057, Braga, Portugal
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40
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Xie H, Zheng CY, Handwerker DA, Bandettini PA, Calhoun VD, Mitra S, Gonzalez-Castillo J. Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. Neuroimage 2019; 188:502-514. [PMID: 30576850 PMCID: PMC6401299 DOI: 10.1016/j.neuroimage.2018.12.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/13/2018] [Accepted: 12/17/2018] [Indexed: 10/27/2022] Open
Abstract
Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA; Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Charles Y Zheng
- Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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41
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Vergara VM, Damaraju E, Turner JA, Pearlson G, Belger A, Mathalon DH, Potkin SG, Preda A, Vaidya JG, van Erp TGM, McEwen S, Calhoun VD. Altered Domain Functional Network Connectivity Strength and Randomness in Schizophrenia. Front Psychiatry 2019; 10:499. [PMID: 31396111 PMCID: PMC6664085 DOI: 10.3389/fpsyt.2019.00499] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/24/2019] [Indexed: 01/12/2023] Open
Abstract
Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. Domain level refers to the integration of data from several brain networks grouped by their functional relationship. Data integration provides more consistent and accurate information compared to an individual brain network. This work considers two domain level measures: functional connectivity strength and randomness. The first measure is simply an average of connectivities within the domain. The second measure assesses the unpredictability and lack of pattern of functional connectivity within the domain. Domains with less random connectivity have higher chance of exhibiting a biologically meaningful connectivity pattern. Consistent with prior observations, individuals with schizophrenia showed aberrant domain connectivity strength between subcortical, cerebellar, and sensorial brain areas. Compared to healthy volunteers, functional connectivity between cognitive and default mode domains showed less randomness, while connectivity between default mode-sensorial areas showed more randomness in schizophrenia patients. These differences in connectivity patterns suggest deleterious rewiring trade-offs among important brain networks.
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Affiliation(s)
- Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,2The Mind Research Network, Albuquerque, NM, United States.,Psychology Department Georgia State University, Atlanta, GA, United States
| | - Eswar Damaraju
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A Turner
- Psychology Department Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, United States.,Olin Neuropsychiatry Research Center, Institute of Living, HHC, Hartford, CT, United States
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States
| | - Daniel H Mathalon
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, IA, United States
| | - Theo G M van Erp
- Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Sarah McEwen
- Pacific Neuroscience Institute, Santa Monica, CA, United States
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,2The Mind Research Network, Albuquerque, NM, United States.,Psychology Department Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, United States
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42
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Salzwedel AP, Stephens RL, Goldman BD, Lin W, Gilmore JH, Gao W. Development of Amygdala Functional Connectivity During Infancy and Its Relationship With 4-Year Behavioral Outcomes. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:62-71. [PMID: 30316743 PMCID: PMC6512984 DOI: 10.1016/j.bpsc.2018.08.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 07/21/2018] [Accepted: 08/14/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND The amygdala represents a core node in the human brain's emotional signal processing circuitry. Given its critical role, both the typical and atypical functional connectivity patterns of the amygdala have been extensively studied in adults. However, the development of amygdala functional connectivity during infancy is less well studied; thus, our understanding of the normal growth trajectory of key emotion-related brain circuits during a critical period is limited. METHODS In this study, we used resting-state functional magnetic resonance imaging (N = 233 subjects with 334 datasets) to delineate the spatiotemporal dynamics of amygdala functional connectivity development during the first 2 years of life. Their relationships with 4-year emotional (i.e., anxiety and inhibitory self-control parent report measures) and cognitive (i.e., IQ) behavioral outcomes were also assessed using multivariate modeling. RESULTS Our results revealed nonlinear growth of amygdala functional connectivity during the first 2 years of life, featuring dramatic synchronization during the first year followed by moderate growth or fine tuning during the second year. Importantly, functional connectivity growth during the second year had significant behavioral implications exemplified by multiple significant predictions of 4-year emotional and cognitive developmental outcomes. CONCLUSIONS The delineation of the spatiotemporal dynamics of amygdala functional connectivity development during infancy and their associations with 4-year behavioral outcomes may provide new references on the early emergence of both typical and atypical emotion processing capabilities.
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Affiliation(s)
- Andrew P Salzwedel
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rebecca L Stephens
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Barbara D Goldman
- FPG Child Development Institute and Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California; Department of Medicine, University of California, Los Angeles, Los Angeles, California.
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43
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Kucyi A, Tambini A, Sadaghiani S, Keilholz S, Cohen JR. Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity. Netw Neurosci 2018; 2:397-417. [PMID: 30465033 PMCID: PMC6195165 DOI: 10.1162/netn_a_00037] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/23/2017] [Indexed: 01/20/2023] Open
Abstract
In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual's active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.
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Affiliation(s)
- Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Arielle Tambini
- Department of Psychology, and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Sepideh Sadaghiani
- Department of Psychology, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA
| | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta, GA, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, NC, USA
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44
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Nalci A, Rao BD, Liu TT. Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI. Neuroimage 2018; 184:1005-1031. [PMID: 30223062 DOI: 10.1016/j.neuroimage.2018.09.024] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/04/2018] [Accepted: 09/08/2018] [Indexed: 11/16/2022] Open
Abstract
In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.
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Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Bhaskar D Rao
- Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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45
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Dynamic functional connectivity and its behavioral correlates beyond vigilance. Neuroimage 2018; 177:1-10. [PMID: 29704612 DOI: 10.1016/j.neuroimage.2018.04.049] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Accepted: 04/21/2018] [Indexed: 11/22/2022] Open
Abstract
Fluctuations in resting-state functional connectivity and global signal have been found to correspond with vigilance fluctuations, but their associations with other behavioral measures are unclear. We evaluated 52 healthy adolescents after a week of adequate sleep followed by five nights of sleep restriction to unmask inter-individual differences in cognition and mood. Resting state scans obtained at baseline only, analyzed using sliding window analysis, consistently yielded two polar dynamic functional connectivity states (DCSs) corresponding to previously reported 'low arousal' and 'high arousal' states. We found that the relative temporal preponderance of two dynamic connectivity states (DCS) in well-rested participants, indexed by a median split of participants, based on the relative time spent in these DCS, revealed highly significant group differences in vigilance at baseline and its decline following multiple nights of sleep restriction. Group differences in processing speed and working memory following manipulation but not at baseline suggest utility of DCS in predicting cognitive vulnerabilities unmasked by a stressor like sleep restriction. DCS temporal predominance was uninformative about mood and sleepiness speaking to specificity in its behavioral predictions. Global signal fluctuation provided information confined to vigilance. This appears to be related to head motion, which increases during periods of low arousal.
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46
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Fukushima M, Betzel RF, He Y, van den Heuvel MP, Zuo XN, Sporns O. Structure-function relationships during segregated and integrated network states of human brain functional connectivity. Brain Struct Funct 2018; 223:1091-1106. [PMID: 29090337 PMCID: PMC5871577 DOI: 10.1007/s00429-017-1539-3] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 10/09/2017] [Indexed: 01/12/2023]
Abstract
Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.
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Affiliation(s)
- Makoto Fukushima
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ye He
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
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47
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Falahpour M, Chang C, Wong CW, Liu TT. Template-based prediction of vigilance fluctuations in resting-state fMRI. Neuroimage 2018; 174:317-327. [PMID: 29548849 DOI: 10.1016/j.neuroimage.2018.03.012] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 01/31/2018] [Accepted: 03/05/2018] [Indexed: 01/12/2023] Open
Abstract
Changes in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions, finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as "templates" for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject's vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.
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Affiliation(s)
- Maryam Falahpour
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA.
| | - Catie Chang
- National Institutes of Health, Bethesda, MD 20892, USA.
| | - Chi Wah Wong
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA.
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA; Department of Radiology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
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48
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Tashjian SM, Goldenberg D, Monti MM, Galván A. Sleep quality and adolescent default mode network connectivity. Soc Cogn Affect Neurosci 2018; 13:290-299. [PMID: 29432569 PMCID: PMC5836271 DOI: 10.1093/scan/nsy009] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 01/23/2018] [Accepted: 01/25/2018] [Indexed: 11/12/2022] Open
Abstract
Sleep suffers during adolescence and is related to academic, emotional and social behaviors. How this normative change relates to ongoing brain development remains unresolved. The default mode network (DMN), a large-scale brain network important for complex cognition and socioemotional processing, undergoes intra-network integration and inter-network segregation during adolescence. Using resting state functional connectivity and actigraphy over 14 days, we examined correlates of naturalistic individual differences in sleep duration and quality in the DMN at rest in 45 human adolescents (ages 14-18). Variation in sleep quality, but not duration, was related to weaker intrinsic DMN connectivity, such that those with worse quality sleep evinced weaker intra-network connectivity at rest. These novel findings suggest sleep quality, a relatively unexplored sleep index, is related to adolescent brain function in a network that contributes to behavioral maturation and undergoes development during adolescence.
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Affiliation(s)
- Sarah M Tashjian
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Diane Goldenberg
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Martin M Monti
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Brain Research Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Adriana Galván
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Brain Research Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
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49
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Cohen JR. The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity. Neuroimage 2017; 180:515-525. [PMID: 28942061 DOI: 10.1016/j.neuroimage.2017.09.036] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 09/12/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022] Open
Abstract
Recent advances in neuroimaging methods and analysis have led to an expanding body of research that investigates how large-scale brain network organization dynamically adapts to changes in one's environment, including both internal state changes and external stimulation. It is now possible to detect changes in functional connectivity that occur on the order of seconds, both during an unconstrained resting state and during the performance of constrained cognitive tasks. It is thought that these dynamic, time-varying changes in functional connectivity, often referred to as dynamic functional connectivity (dFC), include features that are relevant to behavior and cognition. This review summarizes four aspects of the nascent literature directly testing that assumption: 1) how changes in functional network organization on the order of task blocks relate to differences in task demands and to cognitive ability; 2) how differences in dFC variability between different contexts relate to cognitive demands and behavioral performance; 3) how ongoing fluctuations in dFC impact perception and attention; and 4) how different patterns of dFC correspond to individual differences in cognition. The review ends by discussing promising directions for future research in this field. First, it comments on how dFC analyses can help to elucidate the mechanisms of healthy cognition. Next, it describes how dFC processes may be disrupted in disease, and how probing such dysfunction can increase understanding of neural etiology, as well as behavioral and cognitive impairments, observed in psychiatric and neurologic populations. Last, it considers the potential for computational models to uncover neuronal mechanisms of dFC, and how both healthy cognition and disease emerge from network dynamics.
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Affiliation(s)
- Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, 235 E. Cameron Ave., CB#3270, Chapel Hill, NC 27599, USA.
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