201
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Gal S, Tik N, Bernstein-Eliav M, Tavor I. Predicting individual traits from unperformed tasks. Neuroimage 2022; 249:118920. [PMID: 35051583 DOI: 10.1016/j.neuroimage.2022.118920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 01/11/2022] [Accepted: 01/16/2022] [Indexed: 11/16/2022] Open
Abstract
Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.
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Affiliation(s)
- Shachar Gal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Niv Tik
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Michal Bernstein-Eliav
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel.
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202
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Harris VW, Anderson J, Visconti B. Social emotional ability development (SEAD): An integrated model of practical emotion-based competencies. MOTIVATION AND EMOTION 2022; 46:226-253. [PMID: 35034996 PMCID: PMC8742702 DOI: 10.1007/s11031-021-09922-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2021] [Indexed: 12/30/2022]
Abstract
Social emotional abilities (i.e., specific skills), defined as the set of cognitive abilities, emotion-based knowledge, and behavioral competencies (i.e., skill levels) that facilitate adaptively employing prosocial processes and behaviors (i.e., "actions"), such as emotional regulation and sympathetic and empathetic response behaviors, is contemporarily modeled and measured as emotional intelligence. This conceptualization can be problematic, however, as the two concepts are not the same and traditional methods of measuring emotional intelligence can have limited practical utility. The social emotional ability development (SEAD) theoretical model introduced in this treatise represents a pragmatic and simplified approach to the development of social emotional ability and competency as abstracted from constructs of emotional intelligence, social intelligence, and sociocultural learning theory. Further, the SEAD model reaches beyond the individual as the unit of analysis to explore, conceptualize, differentiate, investigate, and define the hierarchal, bi-directional, and contextual nature of the dimensions of social emotional ability within close relationships. Implications for how the SEAD model can be used by researchers, practitioners, educators, individuals, families, and couples across a broad spectrum of domains and interventions are discussed.
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203
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Bao J, Wen Z, Kim M, Saykin AJ, Thompson PM, Zhao Y, Shen L. Identifying imaging genetic associations via regional morphometricity estimation. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:97-108. [PMID: 34890140 PMCID: PMC8730533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer's disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.
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Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mansu Kim
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Department of Radiology and Imaging Sciences Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics University of Southern California School of Medicine, Marina del Rey, CA 90292, USA
| | - Yize Zhao
- Department of Biostatistics Yale University School of Public Health, New Haven, CT 06511, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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204
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Kajimura S, Ito A. The Brain Understands Social Relationships: The Emerging Field of Functional-Connectome-Based Interpersonal Research. Neurosci Insights 2022; 17:26331055221119443. [PMID: 35991809 PMCID: PMC9386479 DOI: 10.1177/26331055221119443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/27/2022] [Indexed: 11/17/2022] Open
Abstract
Human functional brain imaging research over the last 2 decades has shown that data from resting-state brain activity can help predict various psychological and pathological variables and brain function during tasks. However, most variables have been attributed to the individual brain. Recently, several studies have aimed to understand interpersonal relationships based on inter-individual similarity or dissimilarity of functional connectome. In this commentary, we introduce the studies that have opened up a new era of interpersonal research using human brain imaging.
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Affiliation(s)
- Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, Japan
| | - Ayahito Ito
- Research Institute for Future Design, Kochi University of Technology, Kochi, Japan
- Department of Psychology, University of Southampton, Southampton, UK
- Faculty of Health Sciences, Hokkaido University, Hokkaido, Japan
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205
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Chen YC, Arnatkevičiūtė A, McTavish E, Pang JC, Chopra S, Suo C, Fornito A, Aquino KM. The individuality of shape asymmetries of the human cerebral cortex. eLife 2022; 11:75056. [PMID: 36197720 PMCID: PMC9668337 DOI: 10.7554/elife.75056] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 10/04/2022] [Indexed: 01/05/2023] Open
Abstract
Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.
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Affiliation(s)
- Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Monash Data Futures Institute, Monash UniversityMelbourneAustralia
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Eugene McTavish
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Healthy Brain and Mind Research Centre, Faculty of Health Sciences, Australian Catholic UniversityFitzroyAustralia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Department of Psychology, Yale UniversityNew HavenUnited States
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Kevin M Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,School of Physics, University of SydneySydneyAustralia,Center of Excellence for Integrative Brain Function, University of SydneySydneyAustralia,BrainKey IncSan FranciscoUnited States
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206
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Kantarovich K, Mwilambwe-Tshilobo L, Fernández-Cabello S, Setton R, Baracchini G, Lockrow AW, Spreng RN, Turner GR. White matter lesion load is associated with lower within- and greater between- network connectivity across older age. Neurobiol Aging 2022; 112:170-180. [DOI: 10.1016/j.neurobiolaging.2022.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/31/2021] [Accepted: 01/21/2022] [Indexed: 01/01/2023]
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207
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Ward HB, Beermann A, Nawaz U, Halko MA, Janes AC, Moran LV, Brady RO. Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence. Front Psychiatry 2022; 13:804055. [PMID: 35153877 PMCID: PMC8829345 DOI: 10.3389/fpsyt.2022.804055] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/03/2022] [Indexed: 12/30/2022] Open
Abstract
Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = -0.50; 95% CI -0.75 to -0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.
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Affiliation(s)
- Heather Burrell Ward
- Beth Israel Deaconess Medical Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Adam Beermann
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Uzma Nawaz
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Mark A Halko
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Amy C Janes
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Lauren V Moran
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Roscoe O Brady
- Beth Israel Deaconess Medical Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
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208
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Li J, Wu GR, Li B, Fan F, Zhao X, Meng Y, Zhong P, Yang S, Biswal BB, Chen H, Liao W. Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter. Commun Biol 2021; 4:1417. [PMID: 34931033 PMCID: PMC8688465 DOI: 10.1038/s42003-021-02952-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/30/2021] [Indexed: 12/18/2022] Open
Abstract
Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, 400715, P.R. China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Xiaopeng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Peng Zhong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07103, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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209
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Luo W, Constable RT. Inside information: Systematic within-node functional connectivity changes observed across tasks or groups. Neuroimage 2021; 247:118792. [PMID: 34896289 PMCID: PMC8840325 DOI: 10.1016/j.neuroimage.2021.118792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/16/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
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Affiliation(s)
- Wenjing Luo
- Department of Biomedical Engineering, Yale University School of Medicine USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
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210
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Finn ES. Is it time to put rest to rest? Trends Cogn Sci 2021; 25:1021-1032. [PMID: 34625348 PMCID: PMC8585722 DOI: 10.1016/j.tics.2021.09.005] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 12/29/2022]
Abstract
The so-called resting state, in which participants lie quietly with no particular inputs or outputs, represented a paradigm shift from conventional task-based studies in human neuroimaging. Our foray into rest was fruitful from both a scientific and methodological perspective, but at this point, how much more can we learn from rest on its own? While rest still dominates in many subfields, data from tasks have empirically demonstrated benefits, as well as the potential to provide insights about the mind in addition to the brain. I argue that we can accelerate progress in human neuroscience by de-emphasizing rest in favor of more grounded experiments, including promising integrated designs that respect the prominence of self-generated activity while offering enhanced control and interpretability.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
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211
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Burger B, Nenning KH, Schwartz E, Margulies DS, Goulas A, Liu H, Neubauer S, Dauwels J, Prayer D, Langs G. Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. Neuroimage 2021; 247:118770. [PMID: 34861392 DOI: 10.1016/j.neuroimage.2021.118770] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/10/2021] [Accepted: 11/29/2021] [Indexed: 10/19/2022] Open
Abstract
The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.
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Affiliation(s)
- Bianca Burger
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States
| | - Ernst Schwartz
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Daniel S Margulies
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, 75006 Paris, France; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Alexandros Goulas
- Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinstr. 52, 20246 Hamburg, Germany
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29466, USAs
| | - Simon Neubauer
- Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Justin Dauwels
- TU Delft Fac. EEMCS Mekelweg 4 2628 CD Delft; Nayang Technological University, 639798, Singapore
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculo-skeletal Radiology, Medical University of Vienna, Vienna 1090, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
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212
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Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 2021; 10:e72129. [PMID: 34783653 PMCID: PMC8660024 DOI: 10.7554/elife.72129] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.
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Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Aurina Arnatkeviciute
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Ben D Fulcher
- School of Physics, University of SydneySydneyAustralia
| | - Alex Fornito
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
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213
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Sripada C, Angstadt M, Taxali A, Clark DA, Greathouse T, Rutherford S, Dickens JR, Shedden K, Gard AM, Hyde LW, Weigard A, Heitzeg M. Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth. Transl Psychiatry 2021; 11:571. [PMID: 34750359 PMCID: PMC8575890 DOI: 10.1038/s41398-021-01704-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10-16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.
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Affiliation(s)
- Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Mike Angstadt
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Aman Taxali
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - D. Angus Clark
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Tristan Greathouse
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Saige Rutherford
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Joseph R. Dickens
- grid.214458.e0000000086837370Department of Statistics, University of Michigan, Ann Arbor, MI USA
| | - Kerby Shedden
- grid.214458.e0000000086837370Department of Statistics, University of Michigan, Ann Arbor, MI USA
| | - Arianna M. Gard
- grid.164295.d0000 0001 0941 7177Department of Psychology and Neuroscience and Cognitive Neuroscience Program, University of Maryland, College Park, MD USA
| | - Luke W. Hyde
- grid.214458.e0000000086837370Department of Psychology and Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, MI USA
| | - Alexander Weigard
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
| | - Mary Heitzeg
- grid.214458.e0000000086837370Department of Psychiatry, University of Michigan, Ann Arbor, MI USA
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214
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Kong X, Kong R, Orban C, Wang P, Zhang S, Anderson K, Holmes A, Murray JD, Deco G, van den Heuvel M, Yeo BTT. Sensory-motor cortices shape functional connectivity dynamics in the human brain. Nat Commun 2021; 12:6373. [PMID: 34737302 PMCID: PMC8568904 DOI: 10.1038/s41467-021-26704-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 10/07/2021] [Indexed: 12/04/2022] Open
Abstract
Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.
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Affiliation(s)
- Xiaolu Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Peng Wang
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Shaoshi Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Kevin Anderson
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - John D Murray
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats, Universitat Barcelona, Barcelona, Spain
| | - Martijn van den Heuvel
- Department of Complex Trait Genetics, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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215
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Du J, Buckner RL. Precision Estimates of Macroscale Network Organization in the Human and Their Relation to Anatomical Connectivity in the Marmoset Monkey. Curr Opin Behav Sci 2021; 40:144-152. [PMID: 34722833 DOI: 10.1016/j.cobeha.2021.04.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Precision estimates of network organization from functional connectivity MRI in the human and tract-tracing data in the marmoset monkey converge to reveal an orderly macroscale gradient of sequential networks across the cerebral cortex. Parallel networks begin with a sequence of multiple nested sensory-motor networks in both species progressing to more distributed association networks in rostral prefrontal and temporal association zones, which are expanded and differentiated in the human. From this perspective, the spatially-distributed motif encountered in association networks appears to be on a continuum with primary sensory-motor networks. Network motifs supporting sophisticated forms of human cognition may arise from specializations of distributed anatomical networks formed in an ancestor at least 45 million years ago.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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216
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Bai L, JI G, Song Y, Sun J, Wei J, Xue F, Zhu L, Li R, Han Y, Zhang L, Yang J, Qiu B, Wu G, Zhang J, Hong J, Wang K, Zhu C. Dynamic brain connectome and high risk of mental problem in clinical nurses. Hum Brain Mapp 2021; 42:5300-5308. [PMID: 34331489 PMCID: PMC8519872 DOI: 10.1002/hbm.25617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022] Open
Abstract
With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.
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Affiliation(s)
- Ling Bai
- School of Nursing, Anhui Medical UniversityHefeiChina
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Gong‐Jun JI
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yongxia Song
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinmei Sun
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Junjie Wei
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Fang Xue
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Lu Zhu
- Institute of Literature in Chinese Medicine, Nanjing University of Chinese MedicineNanjingChina
| | - Rui Li
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Yanfang Han
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Liu Zhang
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinying Yang
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Bensheng Qiu
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Guo‐Rong Wu
- Key Laboratory of Cognition and Personality, Southwest UniversityChongqingChina
| | - Jing Zhang
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Jingfang Hong
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
| | - Chunyan Zhu
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
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217
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Bijsterbosch JD, Valk SL, Wang D, Glasser MF. Recent developments in representations of the connectome. Neuroimage 2021; 243:118533. [PMID: 34469814 PMCID: PMC8842504 DOI: 10.1016/j.neuroimage.2021.118533] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/16/2021] [Accepted: 08/28/2021] [Indexed: 02/03/2023] Open
Abstract
Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights.
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Affiliation(s)
- Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; INM-7, Forschungszentrum Jülich, Jülich, Germany
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri, 63110, USA
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218
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Tian Y, Zalesky A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? Neuroimage 2021; 245:118648. [PMID: 34673248 DOI: 10.1016/j.neuroimage.2021.118648] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/03/2021] [Accepted: 10/10/2021] [Indexed: 10/20/2022] Open
Abstract
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
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Affiliation(s)
- Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia; Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.
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219
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Kajimura S, Ito A, Izuma K. Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female-Male Relationship. Cereb Cortex 2021; 31:5077-5089. [PMID: 34145453 PMCID: PMC8491675 DOI: 10.1093/cercor/bhab143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/15/2021] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.
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Affiliation(s)
- Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - Ayahito Ito
- Research Institute for Future Design, Kochi University of Technology, Kochi 780-8515, Japan
- Department of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Faculty of Health Sciences, Hokkaido University, Hokkaido 060-0812, Japan
| | - Keise Izuma
- Research Institute for Future Design, Kochi University of Technology, Kochi 780-8515, Japan
- Department of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
- School of Economics & Management, Kochi University of Technology, Kochi 780-8515, Japan
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220
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Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 2021; 109:2820-2846. [PMID: 34270921 PMCID: PMC8448958 DOI: 10.1016/j.neuron.2021.06.016] [Citation(s) in RCA: 251] [Impact Index Per Article: 83.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/24/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, NIMH Intramural Research Program, NIH, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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221
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Elliott ML, Knodt AR, Hariri AR. Striving toward translation: strategies for reliable fMRI measurement. Trends Cogn Sci 2021; 25:776-787. [PMID: 34134933 PMCID: PMC8363569 DOI: 10.1016/j.tics.2021.05.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/27/2022]
Abstract
fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
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222
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Ma ZH, Lu B, Li X, Mei T, Guo YQ, Yang L, Wang H, Tang XZ, Ji ZZ, Liu JR, Xu LZ, Yang YL, Cao QJ, Yan CG, Liu J. Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2021; 26:1108-1122. [PMID: 34465247 DOI: 10.1177/13623613211041904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
LAY ABSTRACT Autism spectrum disorder has long been conceptualized as a disorder of "atypical development of functional brain connectivity (which refers to correlations in activity levels of distant brain regions)." However, most of the research has focused on the connectivity between cortical regions, and much remains unknown about the developmental changes of functional connectivity between subcortical and cortical areas in autism spectrum disorder. We used the technique of resting-state functional magnetic resonance imaging to explore the developmental characteristics of intrinsic functional connectivity (functional brain connectivity when people are asked not to do anything) between subcortical and cortical regions in individuals with and without autism spectrum disorder aged 6-30 years. We focused on one important subcortical structure called striatum, which has roles in motor, cognitive, and affective processes. We found that cortico-striatal intrinsic functional connectivities showed opposite developmental trajectories in autism spectrum disorder and typically developing individuals, with connectivity increasing with age in autism spectrum disorder and decreasing or constant in typically developing individuals. We also found significant negative behavioral correlations between those atypical cortico-striatal intrinsic functional connectivities and autistic symptoms, such as social-communication deficits, and restricted/repetitive behaviors and interests. Taken together, this work highlights that the atypical development of cortico-subcortical functional connectivity might be largely involved in the neuropathological mechanisms of autism spectrum disorder.
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Affiliation(s)
- Zeng-Hui Ma
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, China.,Department of Psychology, University of Chinese Academy of Sciences, China
| | - Xue Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ting Mei
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yan-Qing Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Liu Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hui Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xin-Zhou Tang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Zhao-Zheng Ji
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jing-Ran Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ling-Zi Xu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yu-Lu Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Qing-Jiu Cao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, China.,Department of Psychology, University of Chinese Academy of Sciences, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, China.,International Big-Data Research Center for Depression (IBRCD), Institute of Psychology, Chinese Academy of Sciences, China
| | - Jing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
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223
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Zhao K, Zheng Q, Che T, Dyrba M, Li Q, Ding Y, Zheng Y, Liu Y, Li S. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis. Netw Neurosci 2021; 5:783-797. [PMID: 34746627 PMCID: PMC8567836 DOI: 10.1162/netn_a_00200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/08/2021] [Indexed: 12/13/2022] Open
Abstract
A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.
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Affiliation(s)
- Kun Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, China
| | - Tongtong Che
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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224
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Farahibozorg SR, Bijsterbosch JD, Gong W, Jbabdi S, Smith SM, Harrison SJ, Woolrich MW. Hierarchical modelling of functional brain networks in population and individuals from big fMRI data. Neuroimage 2021; 243:118513. [PMID: 34450262 PMCID: PMC8526871 DOI: 10.1016/j.neuroimage.2021.118513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 06/30/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data. sPROFUMO hierarchically estimates fMRI networks for the population and every individual. We characterised high dimensional resting state fMRI networks from UK Biobank. Model outperforms ICA and dual regression for estimation of individual-specific network topography. We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.
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Affiliation(s)
- Seyedeh-Rezvan Farahibozorg
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom.
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, United States
| | - Weikang Gong
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; New Zealand Brain Research Institute, University of Otago, Christchurch, New Zealand
| | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; OHBA, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United Kingdom
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225
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Kong R, Yang Q, Gordon E, Xue A, Yan X, Orban C, Zuo XN, Spreng N, Ge T, Holmes A, Eickhoff S, Yeo BTT. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cereb Cortex 2021; 31:4477-4500. [PMID: 33942058 PMCID: PMC8757323 DOI: 10.1093/cercor/bhab101] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/03/2021] [Accepted: 03/12/2021] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).
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Affiliation(s)
- Ru Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Qing Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Evan Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Aihuiping Xue
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Xiaoxuan Yan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- National Basic Public Science Data Center, Chinese Academy of Sciences, Beijing 100101, China
| | - Nathan Spreng
- Laboratory of Brain and Cognition, Department of Neurology and Neurosurgery, McGill University, Montreal QC H3A 2B4, Canada
- Departments of Psychiatry and Psychology, Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal QC H3A 2B4, Canada
| | - Tian Ge
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Simon Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich 52425, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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226
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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227
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Yang S, Wagstyl K, Meng Y, Zhao X, Li J, Zhong P, Li B, Fan YS, Chen H, Liao W. Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices. Cell Rep 2021; 36:109582. [PMID: 34433023 DOI: 10.1016/j.celrep.2021.109582] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/28/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022] Open
Abstract
The topological organization of the cerebral cortex provides hierarchical axes, namely gradients, which reveal systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axes remains unclear. We map the gradient of cortical morphometric similarity (MS) connectome, combining multiple features conceptualized as a "fingerprint" of an individual's brain. The principal MS gradient is anchored by motor and sensory cortices at two extreme ends, which are reliable and reproducible. Notably, divergences between motor and sensory hierarchies are consistent with the laminar histological thickness gradient but contrary to the canonical functional connectivity (FC) gradient. Moreover, the MS dissociates with FC gradients in the higher-order association cortices. The MS gradient recapitulates fundamental properties of cortical organization, from gene expression and cyto- and myeloarchitecture to evolutionary expansion. Collectively, our findings provide a heuristic hierarchical organization of cortical morphological neuromarkers.
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Affiliation(s)
- Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Xiaopeng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Peng Zhong
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China.
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228
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Bryce NV, Flournoy JC, Guassi Moreira JF, Rosen ML, Sambook KA, Mair P, McLaughlin KA. Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity. Neuroimage 2021; 243:118487. [PMID: 34419594 PMCID: PMC8629133 DOI: 10.1016/j.neuroimage.2021.118487] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 12/16/2022] Open
Abstract
Over the past decade extensive research has examined the segregation of the human brain into large-scale functional networks. The resulting network maps, i.e. parcellations, are now commonly used for the a priori identification of functional networks. However, the use of these parcellations, particularly in developmental and clinical samples, hinges on four fundamental assumptions: (1) the various parcellations are equally able to recover the networks of interest; (2) adult-derived parcellations well represent the networks in children’s brains; (3) network properties, such as within-network connectivity, are reliably measured across parcellations; and (4) parcellation selection does not impact the results with regard to individual differences in given network properties. In the present study we examined these assumptions using eight common parcellation schemes in two independent developmental samples. We found that the parcellations are equally able to capture networks of interest in both children and adults. However, networks bearing the same name across parcellations (e.g., default network) do not produce reliable within-network measures of functional connectivity. Critically, parcellation selection significantly impacted the magnitude of associations of functional connectivity with age, poverty, and cognitive ability, producing meaningful differences in interpretation of individual differences in functional connectivity based on parcellation choice. Our findings suggest that work employing parcellations may benefit from the use of multiple schemes to confirm the robustness and generalizability of results. Furthermore, researchers looking to gain insight into functional networks may benefit from employing more nuanced network identification approaches such as using densely-sampled data to produce individual-derived network parcellations. A transition towards precision neuroscience will provide new avenues in the characterization of functional brain organization across development and within clinical populations.
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Affiliation(s)
- Nessa V Bryce
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States.
| | - John C Flournoy
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - João F Guassi Moreira
- Department of Psychology, University of California, Los Angeles, CA 90095, United States
| | - Maya L Rosen
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Kelly A Sambook
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Patrick Mair
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
| | - Katie A McLaughlin
- Department of Psychology, Harvard University, Cambridge, MA 02139, United States
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229
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Zhu H, Jin W, Zhou J, Tong S, Xu X, Sun J. Nodal Memberships to Communities of Functional Brain Networks Reveal Functional Flexibility and Individualized Connectome. Cereb Cortex 2021; 31:5090-5106. [PMID: 34387312 DOI: 10.1093/cercor/bhab144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/12/2022] Open
Abstract
Human brain network is organized as interconnected communities for supporting cognition and behavior. Despite studies on the nonoverlapping communities of brain network, overlapping community structure and its relationship to brain function remain largely unknown. With this consideration, we employed the Bayesian nonnegative matrix factorization to decompose the functional brain networks constructed from resting-state fMRI data into overlapping communities with interdigitated mapping to functional subnetworks. By examining the heterogeneous nodal membership to communities, we classified nodes into three classes: Most nodes in somatomotor and limbic subnetworks were affiliated with one dominant community and classified as unimodule nodes; most nodes in attention and frontoparietal subnetworks were affiliated with more than two communities and classified as multimodule nodes; and the remaining nodes affiliated with two communities were classified as bimodule nodes. This three-class paradigm was highly reproducible across sessions and subjects. Furthermore, the more likely a node was classified as multimodule node, the more flexible it will be engaged in multiple tasks. Finally, the FC feature vector associated with multimodule nodes could serve as connectome "fingerprinting" to gain high subject discriminability. Together, our findings offer new insights on the flexible spatial overlapping communities that related to task-based functional flexibility and individual connectome "fingerprinting."
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Affiliation(s)
- Hong Zhu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wen Jin
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Zhou
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shanbao Tong
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiaoke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
| | - Junfeng Sun
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
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Three types of individual variation in brain networks revealed by single-subject functional connectivity analyses. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.02.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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231
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Miller JA, D'Esposito M, Weiner KS. Using Tertiary Sulci to Map the "Cognitive Globe" of Prefrontal Cortex. J Cogn Neurosci 2021; 33:1698-1715. [PMID: 34375416 DOI: 10.1162/jocn_a_01696] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Stuss considered the human PFC as a "cognitive globe" [Stuss, D. T., & Benson, D. F. Neuropsychological studies of the frontal lobes. Psychological Bulletin, 95, 3-28, 1984] on which functions of the frontal lobe could be mapped. Here, we discuss classic and recent findings regarding the evolution, development, function, and cognitive role of shallow indentations or tertiary sulci in PFC, with the goal of using tertiary sulci to map the "cognitive globe" of PFC. First, we discuss lateral PFC (LPFC) tertiary sulci in classical anatomy and modern neuroimaging, as well as their development, with a focus on those within the middle frontal gyrus. Second, we discuss tertiary sulci in comparative neuroanatomy, focusing on primates. Third, we summarize recent findings showing the utility of tertiary sulci for understanding structural-functional relationships with functional network insights in ventromedial PFC and LPFC. Fourth, we revisit and update unresolved theoretical perspectives considered by C. Vogt and O. Vogt (Allgemeinere ergebnisse unserer hirnforschung. Journal für Psychologie und Neurologie, 25, 279-462, 1919) and F. Sanides (Structure and function of the human frontal lobe. Neuropsychologia, 2, 209-219, 1964) that tertiary sulci serve as landmarks for cortical gradients. Together, the consideration of these classic and recent findings indicate that tertiary sulci are situated in a unique position within the complexity of the "cognitive globe" of PFC: They are the smallest and shallowest of sulci in PFC, yet can offer insights that bridge spatial scales (microns to networks), modalities (functional connectivity to behavior), and species. As such, the map of tertiary sulci within each individual participant serves as a coordinate system specific to that individual on which functions may be further mapped. We conclude with new theoretical and methodological questions that, if answered in future research, will likely lead to mechanistic insight regarding the structure and function of human LPFC.
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Smith DM, Perez DC, Porter A, Dworetsky A, Gratton C. Light Through the Fog: Using Precision fMRI Data to Disentangle the Neural Substrates of Cognitive Control. Curr Opin Behav Sci 2021; 40:19-26. [PMID: 33553511 PMCID: PMC7861476 DOI: 10.1016/j.cobeha.2020.12.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cognitive control, the ability to engage in goal-related behavior, is linked to frontal, parietal, and cingulate brain regions. However, the underlying function(s) of these regions is still in question, with ongoing discussions about their specificity and/or multifunctionality. These brain regions are also among the most variable across individuals, which may confound multi-functionality with inter-individual heterogeneity. Precision fMRI-extended data acquisition from single individuals-allows for reliable individualized mapping of brain organization. We review examples of recent studies that use precision fMRI to surmount inter-individual variability in functional neuroanatomy. These studies provide evidence of interleaved specialized and multifunctional regions in the frontal cortex. We discuss the potential for these techniques to address outstanding controversies on the neural underpinnings of cognitive control.
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Affiliation(s)
- Derek M. Smith
- Department of Psychology, Northwestern University, Evanston; Illinois
| | - Diana C. Perez
- Department of Psychology, Northwestern University, Evanston; Illinois
| | - Alexis Porter
- Department of Psychology, Northwestern University, Evanston; Illinois
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, Evanston; Illinois
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston; Illinois
- Department of Neurology, Northwestern University, Evanston, Illinois
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Salvo JJ, Holubecki AM, Braga RM. Correspondence between functional connectivity and task-related activity patterns within the individual. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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235
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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] [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.
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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.
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236
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Ribeiro FL, Dos Santos FRC, Sato JR, Pinaya WHL, Biazoli CE. Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach. Netw Neurosci 2021; 5:527-548. [PMID: 34189376 PMCID: PMC8233119 DOI: 10.1162/netn_a_00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/10/2021] [Indexed: 11/06/2022] Open
Abstract
Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks. The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.
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Affiliation(s)
- Fernanda L Ribeiro
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | | | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Claudinei E Biazoli
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
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237
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Raut RV, Snyder AZ, Mitra A, Yellin D, Fujii N, Malach R, Raichle ME. Global waves synchronize the brain's functional systems with fluctuating arousal. SCIENCE ADVANCES 2021; 7:7/30/eabf2709. [PMID: 34290088 PMCID: PMC8294763 DOI: 10.1126/sciadv.abf2709] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/04/2021] [Indexed: 05/04/2023]
Abstract
We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure ("functional connectivity") of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.
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Affiliation(s)
- Ryan V Raut
- Department of Radiology, Washington University, St. Louis, MO 63110, USA.
| | - Abraham Z Snyder
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Anish Mitra
- Department of Psychiatry, Stanford University, Stanford, CA 94305, USA
| | - Dov Yellin
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Rafael Malach
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Marcus E Raichle
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
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238
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Oathes DJ, Balderston NL, Kording KP, DeLuisi JA, Perez GM, Medaglia JD, Fan Y, Duprat RJ, Satterthwaite TD, Sheline YI, Linn KA. Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1553. [PMID: 33470055 DOI: 10.1002/wcs.1553] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/02/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph A DeLuisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Gianna M Perez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Drexel University, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kristin A Linn
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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239
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Fan YS, Li H, Guo J, Pang Y, Li L, Hu M, Li M, Wang C, Sheng W, Liu H, Gao Q, Chen X, Zong X, Chen H. Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity. Brain Connect 2021; 12:454-464. [PMID: 34210149 DOI: 10.1089/brain.2021.0061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC). METHODS A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES. RESULTS Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom. CONCLUSION This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.
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Affiliation(s)
- Yun-Shuang Fan
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Haoru Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Jing Guo
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China;
| | - Liang Li
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chengdu, Sichuan, China;
| | - Maolin Hu
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Meiling Li
- University of Electronic Science and Technology of China, 610054, China, School of Life Science & Technology,, Chengdu, Sichuan, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, United States;
| | - Chong Wang
- University of Electronic Science and Technology of China, 12599, The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, Chengdu, China.,University of Electronic Science and Technology of China, 12599, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Chengdu, China;
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China, chengdu, China;
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, Charlestown, MA, United States;
| | - Qing Gao
- University of Electronic Science and Technology of China, 12599, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, China, 610054;
| | - Xiaogang Chen
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Xiaofen Zong
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, PR China, Changsha, China;
| | - Huafu Chen
- University of Electronic Science and Technology of China,, School of Life Science and Technology, University of Electronic Science and Technology of China, Sichuan,Chengdu 610054, China, chengdu, China, 610054;
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Moutoussis M, Garzón B, Neufeld S, Bach DR, Rigoli F, Goodyer I, Bullmore E, Guitart-Masip M, Dolan RJ. Decision-making ability, psychopathology, and brain connectivity. Neuron 2021; 109:2025-2040.e7. [PMID: 34019810 PMCID: PMC8221811 DOI: 10.1016/j.neuron.2021.04.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/16/2021] [Accepted: 04/19/2021] [Indexed: 12/11/2022]
Abstract
Decision-making is a cognitive process of central importance for the quality of our lives. Here, we ask whether a common factor underpins our diverse decision-making abilities. We obtained 32 decision-making measures from 830 young people and identified a common factor that we call "decision acuity," which was distinct from IQ and reflected a generic decision-making ability. Decision acuity was decreased in those with aberrant thinking and low general social functioning. Crucially, decision acuity and IQ had dissociable brain signatures, in terms of their associated neural networks of resting-state functional connectivity. Decision acuity was reliably measured, and its relationship with functional connectivity was also stable when measured in the same individuals 18 months later. Thus, our behavioral and brain data identify a new cognitive construct that underpins decision-making ability across multiple domains. This construct may be important for understanding mental health, particularly regarding poor social function and aberrant thought patterns.
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Affiliation(s)
- Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK.
| | - Benjamín Garzón
- Aging Research Centre, Karolinska Institute, Stockholm, Sweden
| | - Sharon Neufeld
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Computational Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich, 8032 Zurich, Switzerland
| | | | - Ian Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Edward Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Marc Guitart-Masip
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Aging Research Centre, Karolinska Institute, Stockholm, Sweden
| | - Raymond J Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
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241
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Yang M, Cao M, Chen Y, Chen Y, Fan G, Li C, Wang J, Liu T. Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model. Front Hum Neurosci 2021; 15:687288. [PMID: 34149385 PMCID: PMC8206477 DOI: 10.3389/fnhum.2021.687288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
GOAL Brain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI. METHODS A deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features. RESULTS We collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs. CONCLUSION The proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD. SIGNIFICANCE These findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.
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Affiliation(s)
- Ming Yang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Menglin Cao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Yuhao Chen
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | | | - Geng Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Chenxi Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, China
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242
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Popovych OV, Jung K, Manos T, Diaz-Pier S, Hoffstaedter F, Schreiber J, Yeo BTT, Eickhoff SB. Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling. Neuroimage 2021; 236:118201. [PMID: 34033913 PMCID: PMC8271096 DOI: 10.1016/j.neuroimage.2021.118201] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/21/2021] [Accepted: 05/22/2021] [Indexed: 11/05/2022] Open
Abstract
Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.
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Affiliation(s)
- Oleksandr V Popovych
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
| | - Kyesam Jung
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Thanos Manos
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany; Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS, UMR 8089, Cergy-Pontoise cedex 95302, France
| | - Sandra Diaz-Pier
- Institute for Advanced Simulation, Juelich Supercomputing Centre (JSC), Research Centre Juelich, Juelich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition, Centre for Translational MR Research & N.1 Institute for Health, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Integrative Sciences and Engineering Programme (ISEP), Singapore
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
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243
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Probabilistic mapping of human functional brain networks identifies regions of high group consensus. Neuroimage 2021; 237:118164. [PMID: 34000397 PMCID: PMC8296467 DOI: 10.1016/j.neuroimage.2021.118164] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 05/11/2021] [Indexed: 11/21/2022] Open
Abstract
Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
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244
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Taxali A, Angstadt M, Rutherford S, Sripada C. Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling. Cereb Cortex 2021; 31:2822-2833. [PMID: 33447841 PMCID: PMC8599720 DOI: 10.1093/cercor/bhaa390] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 08/17/2023] Open
Abstract
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
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Affiliation(s)
- Aman Taxali
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
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245
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DiNicola LM, Buckner RL. Precision Estimates of Parallel Distributed Association Networks: Evidence for Domain Specialization and Implications for Evolution and Development. Curr Opin Behav Sci 2021; 40:120-129. [PMID: 34263017 DOI: 10.1016/j.cobeha.2021.03.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Humans can reason about other minds, comprehend language and imagine. These abilities depend on association regions that exhibit evolutionary expansion and prolonged postnatal development. Precision maps within individuals reveal these expanded zones are populated by multiple specialized networks that each possess a spatially distributed motif but remain anatomically separated throughout the cortex for language, social and mnemonic / spatial functions. Rather than converge on multi-domain regions or hubs, these networks include distinct regions within rostral prefrontal and temporal association zones. To account for these observations, we propose the expansion-fractionation-specialization (EFS) hypothesis: evolutionary expansion of human association cortex may have allowed for an archetype distributed network to fractionate into multiple specialized networks. Human development may recapitulate fractionation and specialization when these abilities emerge.
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Affiliation(s)
- Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138 USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138 USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129 USA
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246
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Lynch CJ, Elbau I, Liston C. Improving precision functional mapping routines with multi-echo fMRI. Curr Opin Behav Sci 2021; 40:113-119. [PMID: 34095359 DOI: 10.1016/j.cobeha.2021.03.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Rapidly developing approaches to acquiring and analyzing densely-sampled, single-subject fMRI data have opened new avenues for understanding the neurobiological basis of individual differences in behavior and could allow fMRI to become a more clinically useful tool. Here, we review briefly key insights from these precision functional mapping studies and a highlight significant barrier to their clinical translation. Specifically, that reliable delineation of functional brain networks in individual humans can require hours of resting-state fMRI data per-subject. We found recently that multi-echo fMRI improves the test-retest reliability of resting-state functional connectivity measurements, mitigating the need for acquiring large quantities of per -subject data. Because the benefits of multi-echo acquisitions are most pronounced in clinically important but artifact-prone brain regions, such as the subgenual cingulate and structures deep in the subcortex, this approach has the potential to increase the impact of precision functional mapping routines in both healthy and clinical populations.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Immanuel Elbau
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
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247
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Wu J, Eickhoff SB, Hoffstaedter F, Patil KR, Schwender H, Yeo BTT, Genon S. A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies. Cereb Cortex 2021; 31:3732-3751. [PMID: 33884421 DOI: 10.1093/cercor/bhab044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/01/2023] Open
Abstract
The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.
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Affiliation(s)
- Jianxiao Wu
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Simon B Eickhoff
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Kaustubh R Patil
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City 117575, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City 117597, Singapore.,N. 1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City 117597, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City 117575, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sarah Genon
- Medical Faculty, Institute for Systems Neuroscience, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, 52428 Jülich, Germany
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248
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Markello RD, Misic B. Comparing spatial null models for brain maps. Neuroimage 2021; 236:118052. [PMID: 33857618 DOI: 10.1016/j.neuroimage.2021.118052] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 04/02/2021] [Accepted: 04/05/2021] [Indexed: 01/31/2023] Open
Abstract
Technological and data sharing advances have led to a proliferation of high-resolution structural and functional maps of the brain. Modern neuroimaging research increasingly depends on identifying correspondences between the topographies of these maps; however, most standard methods for statistical inference fail to account for their spatial properties. Recently, multiple methods have been developed to generate null distributions that preserve the spatial autocorrelation of brain maps and yield more accurate statistical estimates. Here, we comprehensively assess the performance of ten published null frameworks in statistical analyses of neuroimaging data. To test the efficacy of these frameworks in situations with a known ground truth, we first apply them to a series of controlled simulations and examine the impact of data resolution and spatial autocorrelation on their family-wise error rates. Next, we use each framework with two empirical neuroimaging datasets, investigating their performance when testing (1) the correspondence between brain maps (e.g., correlating two activation maps) and (2) the spatial distribution of a feature within a partition (e.g., quantifying the specificity of an activation map within an intrinsic functional network). Finally, we investigate how differences in the implementation of these null models may impact their performance. In agreement with previous reports, we find that naive null models that do not preserve spatial autocorrelation consistently yield elevated false positive rates and unrealistically liberal statistical estimates. While spatially-constrained null models yielded more realistic, conservative estimates, even these frameworks suffer from inflated false positive rates and variable performance across analyses. Throughout our results, we observe minimal impact of parcellation and resolution on null model performance. Altogether, our findings highlight the need for continued development of statistically-rigorous methods for comparing brain maps. The present report provides a harmonised framework for benchmarking and comparing future advancements.
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Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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249
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Dhamala E, Jamison KW, Jaywant A, Dennis S, Kuceyeski A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Hum Brain Mapp 2021; 42:3102-3118. [PMID: 33830577 PMCID: PMC8193532 DOI: 10.1002/hbm.25420] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/03/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022] Open
Abstract
White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.
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Affiliation(s)
- Elvisha Dhamala
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Abhishek Jaywant
- Department of Psychiatry, Weill Cornell Medicine, New York, New York, USA.,Department of Rehabilitation Medicine, Weill Cornell Medicine, New York, New York, USA.,NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Sarah Dennis
- Sarah Lawrence College, Bronxville, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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250
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Finn ES, Bandettini PA. Movie-watching outperforms rest for functional connectivity-based prediction of behavior. Neuroimage 2021; 235:117963. [PMID: 33813007 PMCID: PMC8204673 DOI: 10.1016/j.neuroimage.2021.117963] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/23/2021] [Accepted: 03/08/2021] [Indexed: 01/31/2023] Open
Abstract
A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.
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Affiliation(s)
- Emily S Finn
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
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