1
|
Broeders TAA, Linsen F, Louter TS, Nawijn L, Penninx BWJH, van Tol MJ, van der Wee NJA, Veltman DJ, van der Werf YD, Schoonheim MM, Vinkers CH. Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants. Psychiatry Res 2024; 334:115774. [PMID: 38341928 DOI: 10.1016/j.psychres.2024.115774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/13/2024]
Abstract
Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.
Collapse
Affiliation(s)
- T A A Broeders
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - F Linsen
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - T S Louter
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - L Nawijn
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M J van Tol
- Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - N J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - D J Veltman
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Y D van der Werf
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M M Schoonheim
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - C H Vinkers
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands; GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
| |
Collapse
|
2
|
Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Subject-specific maximum entropy model of resting state fMRI shows diagnostically distinct patterns of energy state distributions. bioRxiv 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported regional brain activation differences (first-order properties) and alterations in functional connectivity based on pairwise correlations in activation (second-order properties). This study used a generalized Ising model, also called a pairwise maximum entropy model (MEM), to integrate first- and second-order properties to provide a comprehensive picture of BOLD patterns and a system-wide summary measure called energy. This study examines the usefulness of individual level MEMs, attempts to identify image-derived counterparts of the model, and explores potential applications to psychiatry. Method MEMs are fit to resting state fMRI data of each individual of a sample of 132 participants consisting of schizophrenia/schizoaffective disorder, bipolar disorder, and major depression, and a demographically matched 132 participants without these diagnoses from the UK Biobank to examine the default mode network (DMN). Results The model explained observed brain state occurrence probabilities well across all participants, and model parameters were highly correlated to image-derived parameters for all groups. Within clinical groups, schizophrenia/schizoaffective disorder and bipolar disorder patients showed significant differences in averaged energy distribution compared to controls for all sub-systems of the DMN except for depression, where differences in the energy distributions were only detected in the DMN of the regions from the right hemisphere. Conclusions Subject-specific Ising modeling may offer an improved measure of biological functional correlates relative to traditional approaches. The observation of distinct patterns of energy distribution among the three clinical groups compared to controls suggests relative diagnostic specificity and potential for clinical translation.
Collapse
Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| |
Collapse
|
3
|
Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
Collapse
Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| |
Collapse
|
4
|
Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
Collapse
Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| |
Collapse
|
5
|
Sanchez-Romero R, Ito T, Mill RD, Hanson SJ, Cole MW. Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations. Neuroimage 2023; 278:120300. [PMID: 37524170 PMCID: PMC10634378 DOI: 10.1016/j.neuroimage.2023.120300] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/06/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023] Open
Abstract
Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists' preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.
Collapse
Affiliation(s)
- Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Stephen José Hanson
- Rutgers University Brain Imaging Center (RUBIC), Rutgers University, Newark, NJ 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| |
Collapse
|
6
|
van Lingen MR, Breedt LC, Geurts JJG, Hillebrand A, Klein M, Kouwenhoven MCM, Kulik SD, Reijneveld JC, Stam CJ, De Witt Hamer PC, Zimmermann MLM, Santos FAN, Douw L. The longitudinal relation between executive functioning and multilayer network topology in glioma patients. Brain Imaging Behav 2023; 17:425-435. [PMID: 37067658 PMCID: PMC10435610 DOI: 10.1007/s11682-023-00770-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.
Collapse
Affiliation(s)
- Marike R van Lingen
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Mathilde C M Kouwenhoven
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Mona L M Zimmermann
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
| |
Collapse
|
7
|
Madole JW, Buchanan CR, Rhemtulla M, Ritchie SJ, Bastin ME, Deary IJ, Cox SR, Tucker-Drob EM. Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain. Neuroimage 2023; 275:120160. [PMID: 37169117 DOI: 10.1016/j.neuroimage.2023.120160] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023] Open
Abstract
Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.
Collapse
Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; VA Puget Sound Health Care System, Seattle Division, Seattle, WA, USA.
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mijke Rhemtulla
- Department of Psychology, University of California, Davis, CA, USA
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Austin, TX, USA
| |
Collapse
|
8
|
Liu Y, Seguin C, Mansour S, Oldham S, Betzel R, Di Biase MA, Zalesky A. Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method. Neuroimage 2023; 270:119962. [PMID: 36822248 DOI: 10.1016/j.neuroimage.2023.119962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/13/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.
Collapse
Affiliation(s)
- Yuanzhe Liu
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia.
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Sina Mansour
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Stuart Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew Zalesky
- Department of Biomedical Engineering, Faculty of Engineering & Information Technology, The University of Melbourne, Melbourne, VIC, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia.
| |
Collapse
|
9
|
Kotler S, Mannino M, Kelso S, Huskey R. First few seconds for flow: A comprehensive proposal of the neurobiology and neurodynamics of state onset. Neurosci Biobehav Rev 2022; 143:104956. [PMID: 36368525 DOI: 10.1016/j.neubiorev.2022.104956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Flow is a cognitive state that manifests when there is complete attentional absorption while performing a task. Flow occurs when certain internal as well as external conditions are present, including intense concentration, a sense of control, feedback, and a balance between the challenge of the task and the relevant skillset. Phenomenologically, flow is accompanied by a loss of self-consciousness, seamless integration of action and awareness, and acute changes in time perception. Research has begun to uncover some of the neurophysiological correlates of flow, as well as some of the state's neuromodulatory processes. We comprehensively review this work and consider the neurodynamics of the onset of the state, considering large-scale brain networks, as well as dopaminergic, noradrenergic, and endocannabinoid systems. To accomplish this, we outline an evidence-based hypothetical situation, and consider the flow state in a broader context including other profound alterations in consciousness, such as the psychedelic state and the state of traumatic stress that can induce PTSD. We present a broad theoretical framework which may motivate future testable hypotheses.
Collapse
Affiliation(s)
| | | | - Scott Kelso
- Human Brain & Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, United States; Intelligent Systems Research Centre, Ulster University, Derry∼Londonderry, North Ireland
| | - Richard Huskey
- Cognitive Communication Science Lab, Department of Communication, University of California Davis, United States; Cognitive Science Program, University of California Davis, United States; Center for Mind and Brain, University of California Davis, United States.
| |
Collapse
|
10
|
Xu F, Garai S, Duong-Tran D, Saykin AJ, Zhao Y, Shen L. Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2022; 2022:1323-1328. [PMID: 37041884 PMCID: PMC10082965 DOI: 10.1109/bibm55620.2022.9995657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Graph theoretical measures have frequently been used to study disrupted connectivity in Alzheimer's disease human brain connectomes. However, prior studies have noted that differences in graph creation methods are confounding factors that may alter the topological observations found in these measures. In this study, we conduct a novel investigation regarding the effect of parcellation scale on graph theoretical measures computed for fiber density networks derived from diffusion tensor imaging. We computed 4 network-wide graph theoretical measures of average clustering coefficient, transitivity, characteristic path length, and global efficiency, and we tested whether these measures are able to consistently identify group differences among healthy control (HC), mild cognitive impairment (MCI), and AD groups in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort across 5 scales of the Lausanne parcellation. We found that the segregative measure of transtivity offered the greatest consistency across scales in distinguishing between healthy and diseased groups, while the other measures were impacted by the selection of scale to varying degrees. Global efficiency was the second most consistent measure that we tested, where the measure could distinguish between HC and MCI in all 5 scales and between HC and AD in 3 out of 5 scales. Characteristic path length was highly sensitive to the variation in scale, corroborating previous findings, and could not identify group differences in many of the scales. Average clustering coefficient was also greatly impacted by scale, as it consistently failed to identify group differences in the higher resolution parcellations. From these results, we conclude that many graph theoretical measures are sensitive to the selection of parcellation scale, and further development in methodology is needed to offer a more robust characterization of AD's relationship with disrupted connectivity.
Collapse
Affiliation(s)
- Frederick Xu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, NJ, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA
| | | |
Collapse
|
11
|
Abstract
A primary goal of the field of behavioral neuroendocrinology is to understand how the brain modulates complex behavior. Over the last 20 years we have proposed various brain networks to explain behavioral regulation, however, the parameters by which these networks are identified are often ill-defined and reflect our personal scientific biases based on our area of expertise. In this perspective article, I question our characterization of brain networks underlying behavior and their utility. Using the Social Behavior Network as a primary example, I outline issues with brain networks commonly discussed in the field of behavioral neuroendocrinology, argue that we reconsider how we identify brain networks underlying behavior, and urge the future use of analytical tools developed by the field of Network Neuroscience. With modern statistical/mathematical tools and state of the art technology for brain imaging, we can strive to minimize our bias and generate brain networks that may more accurately reflect how the brain produces behavior.
Collapse
Affiliation(s)
- Aubrey M Kelly
- Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA 30322, United States of America.
| |
Collapse
|
12
|
Ravano V, Andelova M, Fartaria MJ, Mahdi MFAW, Maréchal B, Meuli R, Uher T, Krasensky J, Vaneckova M, Horakova D, Kober T, Richiardi J. Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study. Neuroimage Clin 2022; 32:102817. [PMID: 34500427 PMCID: PMC8429972 DOI: 10.1016/j.nicl.2021.102817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/30/2021] [Accepted: 08/30/2021] [Indexed: 12/01/2022]
Abstract
Structural disconnectomes can be modelled without diffusion using tractography atlases. Atlas-based and DTI-derived disconnectome topological metrics correlate strongly. MS patient disconnectomes relate to clinical scores.
The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.
Collapse
Affiliation(s)
- Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jan Krasensky
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Manuela Vaneckova
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
13
|
Tooley UA, Bassett DS, Mackey AP. Functional brain network community structure in childhood: Unfinished territories and fuzzy boundaries. Neuroimage 2021; 247:118843. [PMID: 34952233 PMCID: PMC8920293 DOI: 10.1016/j.neuroimage.2021.118843] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/01/2021] [Accepted: 12/19/2021] [Indexed: 12/23/2022] Open
Abstract
Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children’s brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9–11 years (48% female, replication sample n = 544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.
Collapse
Affiliation(s)
- Ursula A Tooley
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US; Department of Physics & Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania,USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA; Santa Fe Institute, Santa Fe, 87501, New Mexico, USA
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, US.
| |
Collapse
|
14
|
Conklin BD, Bressler SL. Organization of areal connectivity in the monkey frontoparietal network. Neuroimage 2021; 241:118414. [PMID: 34298082 DOI: 10.1016/j.neuroimage.2021.118414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/15/2021] [Accepted: 07/19/2021] [Indexed: 11/27/2022] Open
Abstract
Activity observed in biological neural networks is determined by anatomical connectivity between cortical areas. The monkey frontoparietal network facilitates cognitive functions, but the organization of its connectivity is unknown. Here, a new connectivity matrix is proposed which shows that the network utilizes a small-world architecture and the 3-node M9 motif. Its areas exhibit relatively homogeneous connectivity with no suggestion of the hubs seen in scale-free networks. Crucially, its M9 dynamical relay motif is optimally arranged for near-zero and non-zero phase synchrony to arise in support of cognition, serving as a candidate topological mechanism for previously reported findings. These results can serve as a benchmark to be used in the treatment of neurological disorders where the types of cognition the frontoparietal network supports are impaired.
Collapse
Affiliation(s)
- Bryan D Conklin
- School of Psychology, Georgia Institute of Technology, 654 Cherry Str. NW, Atlanta, GA, 30332, United States.
| | - Steven L Bressler
- Center for Complex Systems & Brain Sciences, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, United States
| |
Collapse
|
15
|
Allouch S, Yochum M, Kabbara A, Duprez J, Khalil M, Wendling F, Hassan M, Modolo J. Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks. Brain Topogr 2021; 35:54-65. [PMID: 34244910 DOI: 10.1007/s10548-021-00859-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 06/18/2021] [Indexed: 01/04/2023]
Abstract
Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.
Collapse
Affiliation(s)
- Sahar Allouch
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France. .,Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.
| | - Maxime Yochum
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Aya Kabbara
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Joan Duprez
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.,CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | | | | | - Julien Modolo
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| |
Collapse
|
16
|
Semmel ES, Quadri TR, King TZ. Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review. Neuropsychol Rev 2021. [PMID: 34235627 DOI: 10.1007/s11065-021-09512-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 04/23/2021] [Indexed: 10/20/2022]
Abstract
Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.
Collapse
|
17
|
Luo W, Greene AS, Constable RT. Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain. Neuroimage 2021; 240:118332. [PMID: 34224851 PMCID: PMC8493952 DOI: 10.1016/j.neuroimage.2021.118332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/31/2021] [Accepted: 07/01/2021] [Indexed: 01/24/2023] Open
Abstract
Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.
Collapse
Affiliation(s)
- Wenjing Luo
- Biomedical Engineering, Yale University School of Medicine, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University School of Medicine, United States; MD/PhD program, Yale University School of Medicine, United States
| | - R Todd Constable
- Biomedical Engineering, Yale University School of Medicine, United States; Radiology and Biomedical Imaging, Yale University School of Medicine, United States; Interdepartmental Neuroscience Program, Yale University School of Medicine, United States.
| |
Collapse
|
18
|
Loganathan K, Lv J, Cropley V, Zalesky A, Ho ETW. Valuation system connectivity is correlated with poly-drug use in young adults. Neurosci Res 2021:S0168-0102(21)00147-4. [PMID: 34214618 DOI: 10.1016/j.neures.2021.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 06/03/2021] [Accepted: 06/27/2021] [Indexed: 11/23/2022]
Abstract
Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population sample (n = 992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Collapse
|
19
|
Lioi G, Gripon V, Brahim A, Rousseau F, Farrugia N. Gradients of connectivity as graph Fourier bases of brain activity. Netw Neurosci 2021; 5:322-336. [PMID: 34189367 PMCID: PMC8233110 DOI: 10.1162/netn_a_00183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 01/05/2021] [Indexed: 12/11/2022] Open
Abstract
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.
Collapse
Affiliation(s)
| | | | - Abdelbasset Brahim
- INSERM, Laboratoire Traitement du Signal et de l’Image (LTSI) U1099, University of Rennes, Rennes, France
| | | | | |
Collapse
|
20
|
Perino MT, Yu Q, Myers MJ, Harper JC, Baumel WT, Petersen SE, Barch DM, Luby JL, Sylvester CM. Attention Alterations in Pediatric Anxiety: Evidence From Behavior and Neuroimaging. Biol Psychiatry 2021; 89:726-734. [PMID: 33012520 PMCID: PMC9166685 DOI: 10.1016/j.biopsych.2020.07.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 06/30/2020] [Accepted: 07/08/2020] [Indexed: 01/29/2023]
Abstract
BACKGROUND Pediatric anxiety disorders involve greater capture of attention by threatening stimuli. However, it is not known if disturbances extend to nonthreatening stimuli, as part of a pervasive disturbance in attention-related brain systems. We hypothesized that pediatric anxiety involves greater capture of attention by salient, nonemotional stimuli, coupled with greater activity in the portion of the inferior frontal gyrus (IFG) specific to the ventral attention network (VAN). METHODS A sample of children (n = 129, 75 girls, mean 10.6 years of age), approximately half of whom met criteria for a current anxiety disorder, completed a task measuring involuntary capture of attention by nonemotional (square boxes) and emotional (angry and neutral faces) stimuli. A subset (n = 61) completed a task variant during functional magnetic resonance imaging. A priori analyses examined activity in functional brain areas within the right IFG, supplemented by a whole-brain, exploratory analysis. RESULTS Higher clinician-rated anxiety was associated with greater capture of attention by nonemotional, salient stimuli (F1,125 = 4.94, p = .028) and greater activity in the portion of the IFG specific to the VAN (F1,57 = 10.311, p = .002). Whole-brain analyses confirmed that the effect of anxiety during capture of attention was most pronounced in the VAN portion of the IFG, along with additional areas of the VAN and the default mode network. CONCLUSIONS The pathophysiology of pediatric anxiety appears to involve greater capture of attention to salient stimuli, as well as greater activity in attention-related brain networks. These results provide novel behavioral and brain-based targets for treatment of pediatric anxiety disorders.
Collapse
Affiliation(s)
- Michael T Perino
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
| | - Qiongru Yu
- Department of Psychology, San Diego State University, San Diego, California; Department of Psychiatry, University of California San Diego School of Medicine, San Diego, California
| | - Michael J Myers
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Jennifer C Harper
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - William T Baumel
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Neuroscience, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Joan L Luby
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Chad M Sylvester
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
| |
Collapse
|
21
|
Bekele BM, Luijendijk M, Schagen SB, de Ruiter M, Douw L. Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy. Breast Cancer Res Treat 2021; 189:787-796. [PMID: 34259949 PMCID: PMC8505321 DOI: 10.1007/s10549-021-06326-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/05/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE This longitudinal study aimed to disentangle the impact of chemotherapy on fatigue and hypothetically associated functional brain network alterations. METHODS In total, 34 breast cancer patients treated with chemotherapy (BCC +), 32 patients not treated with chemotherapy (BCC -), and 35 non-cancer controls (NC) were included. Fatigue was assessed using the EORTC QLQ-C30 fatigue subscale at two time points: baseline (T1) and six months after completion of chemotherapy or matched intervals (T2). Participants also underwent resting-state functional magnetic resonance imaging (rsfMRI). An atlas spanning 90 cortical and subcortical brain regions was used to extract time series, after which Pearson correlation coefficients were calculated to construct a brain network per participant per timepoint. Network measures of local segregation and global integration were compared between groups and timepoints and correlated with fatigue. RESULTS As expected, fatigue increased over time in the BCC + group (p = 0.025) leading to higher fatigue compared to NC at T2 (p = 0.023). Meanwhile, fatigue decreased from T1 to T2 in the BCC - group (p = 0.013). The BCC + group had significantly lower local efficiency than NC at T2 (p = 0.033), while a negative correlation was seen between fatigue and local efficiency across timepoints and all participants (T1 rho = - 0.274, p = 0.006; T2 rho = - 0.207, p = 0.039). CONCLUSION Although greater fatigue and lower local functional network segregation co-occur in breast cancer patients after chemotherapy, the relationship between the two generalized across participant subgroups, suggesting that local efficiency is a general neural correlate of fatigue.
Collapse
Affiliation(s)
- Biniam Melese Bekele
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands ,Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Maryse Luijendijk
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands ,Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sanne B. Schagen
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands ,Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Michiel de Ruiter
- Department of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| |
Collapse
|
22
|
Seguin C, Tian Y, Zalesky A. Network communication models improve the behavioral and functional predictive utility of the human structural connectome. Netw Neurosci 2020; 4:980-1006. [PMID: 33195945 PMCID: PMC7655041 DOI: 10.1162/netn_a_00161] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/03/2020] [Indexed: 12/11/2022] Open
Abstract
The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
Collapse
Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
| |
Collapse
|
23
|
Lehmann BCL, Henson RN, Geerligs L, White SR. Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models. Neuroimage 2020; 225:117480. [PMID: 33099009 PMCID: PMC7613122 DOI: 10.1016/j.neuroimage.2020.117480] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 09/07/2020] [Accepted: 10/15/2020] [Indexed: 11/29/2022] Open
Abstract
The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain’s functional connectivity structure across a group of young individuals and a group of old individuals.
Collapse
Affiliation(s)
- B C L Lehmann
- MRC Biostatistics Unit, University of Cambridge, UK; Big Data Institute, University of Oxford, UK; Department of Statistics, University of Oxford, UK.
| | - R N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - L Geerligs
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, UK
| | - S R White
- MRC Biostatistics Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK
| |
Collapse
|
24
|
Bertolero MA, Bassett DS. On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists. Top Cogn Sci 2020; 12:1272-1293. [PMID: 32441854 PMCID: PMC7687232 DOI: 10.1111/tops.12504] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/16/2020] [Accepted: 04/16/2020] [Indexed: 12/31/2022]
Abstract
Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior.
Collapse
Affiliation(s)
- Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania
- Santa Fe Institute
| |
Collapse
|
25
|
Morgan SE, Young J, Patel AX, Whitaker KJ, Scarpazza C, van Amelsvoort T, Marcelis M, van Os J, Donohoe G, Mothersill D, Corvin A, Arango C, Mechelli A, van den Heuvel M, Kahn RS, McGuire P, Brammer M, Bullmore ET. Functional Magnetic Resonance Imaging Connectivity Accurately Distinguishes Cases With Psychotic Disorders From Healthy Controls, Based on Cortical Features Associated With Brain Network Development. Biol Psychiatry Cogn Neurosci Neuroimaging 2020; 6:1125-1134. [PMID: 32800754 DOI: 10.1016/j.bpsc.2020.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/27/2020] [Accepted: 05/31/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Machine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, but it is not yet clear which MRI metrics are the most informative for case-control ML, or how ML algorithms relate to the underlying biology. METHODS We analyzed multimodal MRI data from 2 independent case-control studies of psychotic disorders (cases, n = 65, 28; controls, n = 59, 80) and compared ML accuracy across 5 selected MRI metrics from 3 modalities. Cortical thickness, mean diffusivity, and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify nonpsychotic siblings of cases (n = 64) and to distinguish cases from controls in a third independent study (cases, n = 67; controls, n = 81). RESULTS In both principal studies, the most informative metric was functional MRI connectivity: The areas under the receiver operating characteristic curve were 88% and 76%, respectively. The cortical map of diagnostic connectivity features (ML weights) was replicable between studies (r = .27, p < .001); correlated with replicable case-control differences in functional MRI degree centrality and with a prior cortical map of adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and was replicated in the third case-control study. CONCLUSIONS ML most accurately distinguished cases from controls by a replicable pattern of functional MRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development.
Collapse
Affiliation(s)
- Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom.
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; IXICO plc, London, United Kingdom
| | - Ameera X Patel
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Alan Turing Institute, London, United Kingdom
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of General Psychology, University of Padova, Padova, Italy
| | - Thérèse van Amelsvoort
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands
| | - Machteld Marcelis
- Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry and Psychology, Maastricht University, Maastricht, The Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Gary Donohoe
- School of Psychology, National University of Ireland, Galway, Ireland
| | - David Mothersill
- School of Psychology, National University of Ireland, Galway, Ireland
| | - Aiden Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - Celso Arango
- Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Michael Brammer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Forensic and Development Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| |
Collapse
|
26
|
Rodríguez-Cruces R, Bernhardt BC, Concha L. Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. Neuroimage 2020; 213:116706. [PMID: 32151761 DOI: 10.1016/j.neuroimage.2020.116706] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/14/2020] [Accepted: 03/03/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE. METHODS We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies. RESULTS Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did. CONCLUSIONS Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.
Collapse
Affiliation(s)
- Raúl Rodríguez-Cruces
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico; MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Boris C Bernhardt
- MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Luis Concha
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico.
| |
Collapse
|
27
|
Beynel L, Powers JP, Appelbaum LG. Effects of repetitive transcranial magnetic stimulation on resting-state connectivity: A systematic review. Neuroimage 2020; 211:116596. [PMID: 32014552 PMCID: PMC7571509 DOI: 10.1016/j.neuroimage.2020.116596] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/17/2019] [Accepted: 01/30/2020] [Indexed: 01/02/2023] Open
Abstract
The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these network-level neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanisms-of-action of rTMS on network-level effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent low-frequency-inhibition/high-frequency-facilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMS-induced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize network-level effect that contribute to the efficacy of these rapidly developing noninvasive interventions.
Collapse
Affiliation(s)
- Lysianne Beynel
- Department of Psychiatry and Behavioral Science, Duke University School of Medicine, United States.
| | - John Paul Powers
- Department of Psychology and Neuroscience, Duke University, United States
| | - Lawrence Gregory Appelbaum
- Department of Psychiatry and Behavioral Science, Duke University School of Medicine, United States; Center for Cognitive Neuroscience, Duke University, United States
| |
Collapse
|
28
|
Ekstrom AD, Yonelinas AP. Precision, binding, and the hippocampus: Precisely what are we talking about? Neuropsychologia 2020; 138:107341. [PMID: 31945386 DOI: 10.1016/j.neuropsychologia.2020.107341] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 11/22/2019] [Accepted: 01/11/2020] [Indexed: 02/05/2023]
Abstract
Endel Tulving's proposal that episodic memory is distinct from other memory systems like semantic memory remains an extremely influential idea in cognitive neuroscience research. As originally suggested by Tulving, episodic memory involves three key components that differentiate it from all other memory systems: spatiotemporal binding, mental time travel, and autonoetic consciousness. Here, we focus on the idea of spatiotemporal binding in episodic memory and, in particular, how consideration of the precision of spatiotemporal context helps expand our understanding of episodic memory. Precision also helps shed light on another key issue in cognitive neuroscience, the role of the hippocampus outside of episodic memory in perception, attention, and working memory. By considering precision alongside item-context bindings, we attempt to shed new light on both the nature of how we represent context and what roles the hippocampus plays in episodic memory and beyond.
Collapse
Affiliation(s)
- Arne D Ekstrom
- Department of Psychology, University of Arizona, 1503 E. University Blvd., Tucson, AZ, 85721, USA; Evelyn McKnight Brain Institute, University of Arizona, 1503 E. University Blvd., Tucson, AZ, 85721, USA.
| | - Andrew P Yonelinas
- Center for Neuroscience, University of California, Davis, 1 Shields Ave, Davis, CA, 95618, USA; Department of Psychology, University of California, Davis, 1 Shields Ave, Davis, CA, 95618, USA.
| |
Collapse
|
29
|
Hughes C, Faskowitz J, Cassidy BS, Sporns O, Krendl AC. Aging relates to a disproportionately weaker functional architecture of brain networks during rest and task states. Neuroimage 2020; 209:116521. [PMID: 31926282 DOI: 10.1016/j.neuroimage.2020.116521] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/02/2020] [Accepted: 01/03/2020] [Indexed: 01/01/2023] Open
Abstract
Functional connectivity - the co-activation of brain regions - forms the basis of the brain's functional architecture. Often measured during resting-state (i.e., in a task-free setting), patterns of functional connectivity within and between brain networks change with age. These patterns are of interest to aging researchers because age differences in resting-state connectivity relate to older adults' relative cognitive declines. Less is known about age differences in large-scale brain networks during directed tasks. Recent work in younger adults has shown that patterns of functional connectivity are highly correlated between rest and task states. Whether this finding extends to older adults remains largely unexplored. To this end, we assessed younger and older adults' functional connectivity across the whole brain using fMRI while participants underwent resting-state or completed directed tasks (e.g., a reasoning judgement task). Resting-state and task functional connectivity were less strongly correlated in older as compared to younger adults. This age-dependent difference could be attributed to significantly lower consistency in network organization between rest and task states among older adults. Older adults had less distinct or segregated networks during resting-state. This more diffuse pattern of organization was exacerbated during directed tasks. Finally, the default mode network, often implicated in neurocognitive aging, contributed strongly to this pattern. These findings establish that age differences in functional connectivity are state-dependent, providing greater insight into the mechanisms by which aging may lead to cognitive declines.
Collapse
Affiliation(s)
- Colleen Hughes
- Psychological and Brain Sciences Department, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
| | - Joshua Faskowitz
- Psychological and Brain Sciences Department, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Brittany S Cassidy
- Department of Psychology, The University of North Carolina at Greensboro, 296 Eberhart Building, Greensboro, NC, 27412, USA
| | - Olaf Sporns
- Psychological and Brain Sciences Department, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| | - Anne C Krendl
- Psychological and Brain Sciences Department, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
| |
Collapse
|
30
|
Uddin LQ, Yeo BTT, Spreng RN. Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topogr 2019; 32:926-42. [PMID: 31707621 DOI: 10.1007/s10548-019-00744-6] [Citation(s) in RCA: 289] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/02/2019] [Indexed: 12/25/2022]
Abstract
The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.
Collapse
|
31
|
Abstract
Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological concept of g. Few attempts have been made to explain intelligence exclusively based on brain characteristics - the brain perspective. From a methodological viewpoint the brain intelligence relation has been studied by means of correlational and interventional studies. The later providing a causal elucidation of the brain - intelligence relation. The best neuro-anatomical predictor of intelligence is brain volume showing a modest positive correlation with g, explaining between 9 to 16% of variance. The most likely explanation was that larger brains, containing more neurons, have a greater computational power and in that way allow more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape showed less consistent patterns. The development of diffusion tensor imaging has allowed researchers to look also into the microstructure of brain tissue. Consistently observed was a positively correlation between white matter integrity and intelligence, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain intelligence relationship three theories have been put forward: the neural efficiency, the P-FIT and the multi demand (MD) system theory. On the other hand, The Network Neuroscience Theory of g, based on methods from mathematics, physics, and computer science, is an example for the brain perspective on neurobiological underpinning of intelligence. In this framework network flexibility and dynamics provide the foundation for general intelligence. With respect to intervention studies the most promising results have been achieved with noninvasive brain stimulation and behavioral training providing tentative support for findings put forward by the correlational approach. To date the best consensus based on the diversity of results reported would be that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas. The capacity of flexible transitions between these network states represents the essence of intelligence - g.
Collapse
|
32
|
Toro-Serey C, Tobyne SM, McGuire JT. Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex. Neuroimage 2019; 205:116305. [PMID: 31654759 DOI: 10.1016/j.neuroimage.2019.116305] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 09/17/2019] [Accepted: 10/19/2019] [Indexed: 12/18/2022] Open
Abstract
Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into "DN" and "non-DN" subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.
Collapse
Affiliation(s)
- Claudio Toro-Serey
- Department of Psychological and Brain Sciences, Boston University, Boston, USA; Center for Systems Neuroscience, Boston University, Boston, USA.
| | - Sean M Tobyne
- Department of Psychological and Brain Sciences, Boston University, Boston, USA; Graduate Program for Neuroscience, Boston University, Boston, USA
| | - Joseph T McGuire
- Department of Psychological and Brain Sciences, Boston University, Boston, USA; Center for Systems Neuroscience, Boston University, Boston, USA.
| |
Collapse
|
33
|
Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
Collapse
Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| |
Collapse
|
34
|
Stillman PE, Wilson JD, Denny MJ, Desmarais BA, Cranmer SJ, Lu ZL. A consistent organizational structure across multiple functional subnetworks of the human brain. Neuroimage 2019; 197:24-36. [PMID: 30928689 DOI: 10.1016/j.neuroimage.2019.03.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 02/15/2019] [Accepted: 03/17/2019] [Indexed: 10/27/2022] Open
Abstract
A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique - the correlation generalized exponential random graph model (cGERGM) - to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs - consistent patterns of coactivation (i.e., connectivity) between collections of nodes that describe how the regions within a network are organized (e.g., clustering) - and anatomical properties - relationships between the regions that are dictated by anatomy (e.g., Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i.e., hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.
Collapse
Affiliation(s)
| | - James D Wilson
- University of San Francisco, San Francisco, CA, 94117, USA
| | | | | | | | - Zhong-Lin Lu
- The Ohio State University, Columbus, OH, 43210, USA
| |
Collapse
|
35
|
Abstract
Psychiatric disorders are disturbances of cognitive and behavioral processes mediated by the brain. Emerging evidence suggests that accurate biomarkers for psychiatric disorders might benefit from incorporating information regarding multiple brain regions and their interactions with one another, rather than considering local perturbations in brain structure and function alone. Recent advances in the field of applied mathematics generally - and network science specifically - provide a language to capture the complexity of interacting brain regions, and the application of this language to fundamental questions in neuroscience forms the emerging field of network neuroscience. This chapter provides an overview of the use and utility of network neuroscience for building biomarkers in psychiatry. The chapter begins with an overview of the theoretical frameworks and tools that encompass network neuroscience before describing applications of network neuroscience to the study of schizophrenia and major depressive disorder. With reference to work on genetic, molecular, and environmental correlates of network neuroscience features, the promises and challenges of network neuroscience for providing tools that aid in the diagnosis and the evaluation of treatment response in psychiatric disorders are discussed.
Collapse
|
36
|
Girn M, Mills C, Christoff K. Linking brain network reconfiguration and intelligence: Are we there yet? Trends Neurosci Educ 2019; 15:62-70. [PMID: 31176472 DOI: 10.1016/j.tine.2019.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/22/2019] [Accepted: 04/04/2019] [Indexed: 01/08/2023]
Abstract
Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.
Collapse
Affiliation(s)
- Manesh Girn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychology, University of British Columbia, Vancouver, British Columbia.
| | - Caitlin Mills
- Department of Psychology, University of New Hampshire, Durham, New Hampshire
| | - Kalina Christoff
- Department of Psychology, University of British Columbia, Vancouver, British Columbia; Centre for Brain Health, University of British Columbia, Vancouver, British Columbia
| |
Collapse
|
37
|
Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Netw Neurosci 2018; 3:1-26. [PMID: 30793071 PMCID: PMC6326733 DOI: 10.1162/netn_a_00054] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 04/03/2018] [Indexed: 12/15/2022] Open
Abstract
Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
Collapse
Affiliation(s)
- Michael N. Hallquist
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
| | - Frank G. Hillary
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
- Department of Neurology, Hershey Medical Center, Hershey, PA, USA
| |
Collapse
|
38
|
Raj A, Powell F. Models of Network Spread and Network Degeneration in Brain Disorders. Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3:788-797. [PMID: 30170711 DOI: 10.1016/j.bpsc.2018.07.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/11/2018] [Accepted: 07/11/2018] [Indexed: 01/01/2023]
Abstract
Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.
Collapse
Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
| | - Fon Powell
- Department of Radiology, Weill Cornell Medicine, New York, New York
| |
Collapse
|
39
|
Morgan SE, White SR, Bullmore ET, Vértes PE. A Network Neuroscience Approach to Typical and Atypical Brain Development. Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3:754-766. [PMID: 29703679 DOI: 10.1016/j.bpsc.2018.03.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 02/21/2018] [Accepted: 03/01/2018] [Indexed: 12/15/2022]
Abstract
Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.
Collapse
Affiliation(s)
- Sarah E Morgan
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
| | - Simon R White
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Edward T Bullmore
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, United Kingdom; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, United Kingdom
| | - Petra E Vértes
- Behavioural and Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
40
|
Sizemore AE, Giusti C, Kahn A, Vettel JM, Betzel RF, Bassett DS. Cliques and cavities in the human connectome. J Comput Neurosci 2018; 44:115-145. [PMID: 29143250 PMCID: PMC5769855 DOI: 10.1007/s10827-017-0672-6] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 09/30/2017] [Accepted: 10/27/2017] [Indexed: 12/26/2022]
Abstract
Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large, distributed networks of brain areas, principled examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates that we move from considering exclusively pairwise interactions to capturing higher order relations, concepts naturally expressed in the language of algebraic topology. These tools can be used to study mesoscale network structures that arise from the arrangement of densely connected substructures called cliques in otherwise sparsely connected brain networks. We detect cliques (all-to-all connected sets of brain regions) in the average structural connectomes of 8 healthy adults scanned in triplicate and discover the presence of more large cliques than expected in null networks constructed via wiring minimization, providing architecture through which brain network can perform rapid, local processing. We then locate topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cavities exist consistently across subjects, differ from those observed in null model networks, and - importantly - link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain's structural architecture.
Collapse
Affiliation(s)
- Ann E. Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Broad Institute, Harvard University and the Massachusetts Institute of Technology, Cambridge, MA USA
| | - Chad Giusti
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Ari Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Human Research & Engineering Directorate, U.S. Army Research Laboratory, Aberdeen, MD USA
| | - Jean M. Vettel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Human Research & Engineering Directorate, U.S. Army Research Laboratory, Aberdeen, MD USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA USA
| | - Richard F. Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| |
Collapse
|
41
|
Khambhati AN, Mattar MG, Wymbs NF, Grafton ST, Bassett DS. Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. Neuroimage 2017; 166:385-399. [PMID: 29138087 DOI: 10.1016/j.neuroimage.2017.11.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 10/18/2017] [Accepted: 11/07/2017] [Indexed: 11/15/2022] Open
Abstract
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non-negative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.
Collapse
Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo G Mattar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas F Wymbs
- Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD 21205, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
| |
Collapse
|
42
|
Abstract
The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution.
Collapse
Affiliation(s)
- Richard F Betzel
- School of Engineering and Applied Science, Department of Bioengineering, USA
| | - Danielle S Bassett
- School of Engineering and Applied Science, Department of Bioengineering, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
43
|
Gu S, Betzel RF, Mattar MG, Cieslak M, Delio PR, Grafton ST, Pasqualetti F, Bassett DS. Optimal trajectories of brain state transitions. Neuroimage 2017; 148:305-317. [PMID: 28088484 PMCID: PMC5489344 DOI: 10.1016/j.neuroimage.2017.01.003] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 12/27/2016] [Accepted: 01/02/2017] [Indexed: 12/05/2022] Open
Abstract
The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.
Collapse
Affiliation(s)
- Shi Gu
- Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo G Mattar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Philip R Delio
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA; Neurology Associates of Santa Barbara, Santa Barbara, CA 93105, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
44
|
Huber L. Brains are not just neurons. Comment on "Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition" by Fitch. Phys Life Rev 2014; 11:373-4. [PMID: 24908289 DOI: 10.1016/j.plrev.2014.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Accepted: 05/27/2014] [Indexed: 11/17/2022]
Affiliation(s)
- Ludwig Huber
- Comparative Cognition, Messerli Research Institute, University of Veterinary Medicine Vienna, Medical University of Vienna, University of Vienna, Veterinaerplatz 1, 1210 Vienna, Austria
| |
Collapse
|