551
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Eickhoff SB, Constable RT, Yeo BTT. Topographic organization of the cerebral cortex and brain cartography. Neuroimage 2017; 170:332-347. [PMID: 28219775 DOI: 10.1016/j.neuroimage.2017.02.018] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 02/02/2017] [Accepted: 02/07/2017] [Indexed: 01/17/2023] Open
Abstract
One of the most specific but also challenging properties of the brain is its topographic organization into distinct modules or cortical areas. In this paper, we first review the concept of topographic organization and its historical development. Next, we provide a critical discussion of the current definition of what constitutes a cortical area, why the concept has been so central to the field of neuroimaging and the challenges that arise from this view. A key aspect in this discussion is the issue of spatial scale and hierarchy in the brain. Focusing on in-vivo brain parcellation as a rapidly expanding field of research, we highlight potential limitations of the classical concept of cortical areas in the context of multi-modal parcellation and propose a revised interpretation of cortical areas building on the concept of neurobiological atoms that may be aggregated into larger units within and across modalities. We conclude by presenting an outlook on the implication of this revised concept for future mapping studies and raise some open questions in the context of brain parcellation.
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Affiliation(s)
- Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany.
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale University, USA; Department of Neurosurgery, Yale University, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA; Centre for Cognitive Neuroscience, Duke-NUS Graduate Medical School, Singapore
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552
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Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat Neurosci 2017; 20:513-515. [PMID: 28218917 DOI: 10.1038/nn.4511] [Citation(s) in RCA: 137] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 01/25/2017] [Indexed: 12/24/2022]
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553
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Liu W, Wei D, Chen Q, Yang W, Meng J, Wu G, Bi T, Zhang Q, Zuo XN, Qiu J. Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Sci Data 2017; 4:170017. [PMID: 28195583 PMCID: PMC5308199 DOI: 10.1038/sdata.2017.17] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 12/22/2016] [Indexed: 11/10/2022] Open
Abstract
Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed-demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches.
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Affiliation(s)
- Wei Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China.,Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen 6525 EZ, The Netherlands
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Guorong Wu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Taiyong Bi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qinglin Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xi-Nian Zuo
- Faculty of Psychology, Southwest University, Chongqing 400715, China.,Key Laboratory of Behavioral Science, Laboratory for Functional Connectome and Development and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning 530000, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
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554
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Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc 2017; 12:506-518. [PMID: 28182017 DOI: 10.1038/nprot.2016.178] [Citation(s) in RCA: 557] [Impact Index Per Article: 79.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10-100 min for model building, 1-48 h for permutation testing, and 10-20 min for visualization of results.
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555
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Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in Autism Spectrum Disorder. Neuroimage 2017; 170:412-423. [PMID: 28188914 DOI: 10.1016/j.neuroimage.2017.02.019] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/01/2017] [Accepted: 02/07/2017] [Indexed: 11/20/2022] Open
Abstract
Autism Spectrum Disorder (ASD) has been associated with abnormal synaptic development causing a breakdown in functional connectivity. However, when measured at the macro scale using resting state fMRI, these alterations are subtle and often difficult to detect due to the large heterogeneity of the pathology. Recently, we outlined a novel approach for generating robust biomarkers of resting state functional magnetic resonance imaging (RS-fMRI) using connectivity based parcellation of gross morphological structures to improve single-subject reproducibility and generate more robust connectivity fingerprints. Here we apply this novel approach to investigating the organization and connectivity strength of the cortico-striatal system in a large sample of ASD individuals and typically developed (TD) controls (N=130 per group). Our results showed differences in the parcellation of the striatum in ASD. Specifically, the putamen was found to be one single structure in ASD, whereas this was split into anterior and posterior segments in an age, IQ, and head movement matched TD group. An analysis of the connectivity fingerprints revealed that the group differences in clustering were driven by differential connectivity between striatum and the supplementary motor area, posterior cingulate cortex, and posterior insula. Our approach for analysing RS-fMRI in clinical populations has provided clear evidence that cortico-striatal circuits are organized differently in ASD. Based on previous task-based segmentations of the striatum, we believe that the anterior putamen cluster present in TD, but not in ASD, likely contributes to social and language processes.
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556
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Amico E, Marinazzo D, Di Perri C, Heine L, Annen J, Martial C, Dzemidzic M, Kirsch M, Bonhomme V, Laureys S, Goñi J. Mapping the functional connectome traits of levels of consciousness. Neuroimage 2017; 148:201-211. [PMID: 28093358 DOI: 10.1016/j.neuroimage.2017.01.020] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 12/12/2016] [Accepted: 01/10/2017] [Indexed: 12/28/2022] Open
Abstract
Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a "resting" human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.
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Affiliation(s)
- Enrico Amico
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium
| | - Daniele Marinazzo
- Department of Data-analysis, University of Ghent, B9000 Ghent, Belgium
| | - Carol Di Perri
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; University Hospital of Liège, Liège, Belgium
| | - Lizette Heine
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; University Hospital of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; University Hospital of Liège, Liège, Belgium
| | - Mario Dzemidzic
- Department of Neurology and Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | - Steven Laureys
- Coma Science Group, GIGA Research Center, University of Liège, Liège, Belgium; University Hospital of Liège, Liège, Belgium.
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA; Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA.
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557
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Wang J, Xie S, Guo X, Becker B, Fox PT, Eickhoff SB, Jiang T. Correspondent Functional Topography of the Human Left Inferior Parietal Lobule at Rest and Under Task Revealed Using Resting-State fMRI and Coactivation Based Parcellation. Hum Brain Mapp 2017; 38:1659-1675. [PMID: 28045222 DOI: 10.1002/hbm.23488] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 11/27/2016] [Accepted: 11/30/2016] [Indexed: 11/09/2022] Open
Abstract
The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven subregions. Four subregions were located in the supramarginal gyrus (SMG) and three subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing. Hum Brain Mapp 38:1659-1675, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China
| | - Sangma Xie
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Xin Guo
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China
| | - Benjamin Becker
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Dusseldorf, Germany
| | - Tianzi Jiang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 625014, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,The Queensland Brain Institute, University of Queensland, Brisbane, Queensland, 4072, Australia
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558
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Gkigkitzis I, Haranas I, Kotsireas I. Biological Relevance of Network Architecture. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:1-29. [PMID: 28971385 DOI: 10.1007/978-3-319-56246-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
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Affiliation(s)
- Ioannis Gkigkitzis
- Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
| | - Ilias Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
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559
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Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology. Harv Rev Psychiatry 2017; 25:209-217. [PMID: 28816791 PMCID: PMC5644502 DOI: 10.1097/hrp.0000000000000166] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit-level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior-providing, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. This review focuses on the use of resting-state functional connectivity MRI to assess brain circuits, on the advances generated by the Human Connectome Project, and on how these advances potentially contribute to understanding neural circuit dysfunction in psychopathology. The review gives particular attention to the methods developed by the Human Connectome Project that may be especially relevant to studies of psychopathology; it outlines some of the key findings about what constitutes a brain region; and it highlights new information about the nature and stability of brain circuits. Some of the Human Connectome Project's new findings particularly relevant to psychopathology-about neural circuits and their relationships to behavior-are also presented. The review ends by discussing the extension of Human Connectome Project methods across the lifespan and into manifest illness. Potential treatment implications are also considered.
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560
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Wang J, Wang H. A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data. Front Hum Neurosci 2016; 10:659. [PMID: 28082885 PMCID: PMC5187473 DOI: 10.3389/fnhum.2016.00659] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 12/12/2016] [Indexed: 01/09/2023] Open
Abstract
Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two-level SLIC were proposed. The cluster number varied in a wide range in order to generate parcellations with multiple granularities. The two SLIC approaches were compared with three state-of-the-art approaches under different evaluation metrics, which include spatial contiguity, functional homogeneity, and reproducibility. Both the group-to-group reproducibility and the group-to-subject reproducibility were evaluated in our study. The experimental results showed that the proposed approaches obtained relatively good overall clustering performances in different conditions that included different weighting functions, different sparsifying schemes, and several confounding factors. Therefore, the generated atlases are appropriate to be utilized as nodes for network analysis. The generated atlases and major source codes of this study have been made publicly available at http://www.nitrc.org/projects/slic/.
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Affiliation(s)
- Jing Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University Nanjing, China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University Nanjing, China
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561
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Contreras JA, Goñi J, Risacher SL, Amico E, Yoder K, Dzemidzic M, West JD, McDonald BC, Farlow MR, Sporns O, Saykin AJ. Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2016; 6:40-49. [PMID: 28149942 PMCID: PMC5266473 DOI: 10.1016/j.dadm.2016.12.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. METHODS Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). RESULTS Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. DISCUSSION Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.
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Affiliation(s)
- Joey A. Contreras
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Joaquín Goñi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- School of Industrial Engineering, Purdue University, West-Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West-Lafayette, IN, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enrico Amico
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Karmen Yoder
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mario Dzemidzic
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John D. West
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brenna C. McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin R. Farlow
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Corresponding author.
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562
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Janssen RJ, Jylänki P, van Gerven MAJ. Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data. PLoS One 2016; 11:e0164703. [PMID: 27935937 PMCID: PMC5147788 DOI: 10.1371/journal.pone.0164703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 09/29/2016] [Indexed: 11/18/2022] Open
Abstract
We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.
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Affiliation(s)
- Ronald J. Janssen
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
- * E-mail:
| | - Pasi Jylänki
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
| | - Marcel A. J. van Gerven
- Radboud University, Donders Centre for Brain Cognition and Behaviour, Nijmegen, the Netherlands
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563
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Huang MX, Harrington DL, Robb Swan A, Angeles Quinto A, Nichols S, Drake A, Song T, Diwakar M, Huang CW, Risbrough VB, Dale A, Bartsch H, Matthews S, Huang JW, Lee RR, Baker DG. Resting-State Magnetoencephalography Reveals Different Patterns of Aberrant Functional Connectivity in Combat-Related Mild Traumatic Brain Injury. J Neurotrauma 2016; 34:1412-1426. [PMID: 27762653 DOI: 10.1089/neu.2016.4581] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Blast mild traumatic brain injury (mTBI) is a leading cause of sustained impairment in military service members and veterans. However, the mechanism of persistent disability is not fully understood. The present study investigated disturbances in brain functioning in mTBI participants using a source-imaging-based approach to analyze functional connectivity (FC) from resting-state magnetoencephalography (rs-MEG). Study participants included 26 active-duty service members or veterans who had blast mTBI with persistent post-concussive symptoms, and 22 healthy control active-duty service members or veterans. The source time courses from regions of interest (ROIs) were used to compute ROI to whole-brain (ROI-global) FC for different frequency bands using two different measures: 1) time-lagged cross-correlation and 2) phase-lock synchrony. Compared with the controls, blast mTBI participants showed increased ROI-global FC in beta, gamma, and low-frequency bands, but not in the alpha band. Sources of abnormally increased FC included the: 1) prefrontal cortex (right ventromedial prefrontal cortex [vmPFC], right rostral anterior cingulate cortex [rACC]), and left ventrolateral and dorsolateral prefrontal cortex; 2) medial temporal lobe (bilateral parahippocampus, hippocampus, and amygdala); and 3) right putamen and cerebellum. In contrast, the blast mTBI group also showed decreased FC of the right frontal pole. Group differences were highly consistent across the two different FC measures. FC of the left ventrolateral prefrontal cortex correlated with executive functioning and processing speed in mTBI participants. Altogether, our findings of increased and decreased regionalpatterns of FC suggest that disturbances in intrinsic brain connectivity may be the result of multiple mechanisms, and are associated with cognitive sequelae of the injury.
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Affiliation(s)
- Ming-Xiong Huang
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Deborah L Harrington
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Ashley Robb Swan
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Annemarie Angeles Quinto
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Sharon Nichols
- 3 Department of Neuroscience, University of California , San Diego, California
| | | | - Tao Song
- 2 Department of Radiology, University of California , San Diego, California
| | - Mithun Diwakar
- 2 Department of Radiology, University of California , San Diego, California
| | - Charles W Huang
- 5 Department of Bioengineering, University of California , San Diego, California
| | - Victoria B Risbrough
- 6 Department of Psychiatry, University of California , San Diego, California.,7 VA Center of Excellence for Stress and Mental Health , San Diego, California
| | - Anders Dale
- 2 Department of Radiology, University of California , San Diego, California
| | - Hauke Bartsch
- 2 Department of Radiology, University of California , San Diego, California
| | - Scott Matthews
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,6 Department of Psychiatry, University of California , San Diego, California.,8 Aspire Center , VASDHS Residential Rehabilitation Treatment Program, San Diego, California
| | | | - Roland R Lee
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Dewleen G Baker
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,6 Department of Psychiatry, University of California , San Diego, California.,7 VA Center of Excellence for Stress and Mental Health , San Diego, California
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564
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Honnorat N, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C. sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain. J Neurosci Methods 2016; 277:1-20. [PMID: 27913211 DOI: 10.1016/j.jneumeth.2016.11.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 10/27/2016] [Accepted: 11/24/2016] [Indexed: 10/20/2022]
Abstract
BACKGROUND Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for. NEW METHOD In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually. RESULTS Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population. COMPARISON WITH EXISTING METHODS We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data. CONCLUSIONS Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.
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Affiliation(s)
- N Honnorat
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - T D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - R E Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - R C Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - C Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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565
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Nie L, Matthews PM, Guo Y. Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex. IEEE Trans Biomed Eng 2016; 63:2505-2517. [PMID: 27875122 DOI: 10.1109/tbme.2016.2571221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual-or state-dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. METHODS We propose a method called "joint K-means" to robustly parcellate the cerebral cortex using functional magnetic resonance imaging (fMRI) data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. RESULTS Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are nonuniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal, and occipito-parietal cortices. This pattern is reproducible across sessions, between groups, and using different numbers of parcels. CONCLUSION The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. SIGNIFICANCE The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.
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566
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Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Cannon TD, Constable RT. Multisite reliability of MR-based functional connectivity. Neuroimage 2016; 146:959-970. [PMID: 27746386 DOI: 10.1016/j.neuroimage.2016.10.020] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/26/2022] Open
Abstract
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.
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Affiliation(s)
- Stephanie Noble
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA.
| | - Dustin Scheinost
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Emily S Finn
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA
| | - Xilin Shen
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Xenophon Papademetris
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Yale University, Department of Biomedical Engineering, New Haven, CT, USA
| | - Sarah C McEwen
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Carrie E Bearden
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Jean Addington
- University of Calgary, Department of Psychiatry, Calgary, Alberta, Canada
| | - Bradley Goodyear
- University of Calgary, Departments of Radiology, Clinical Neurosciences and Psychiatry, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Heline Mirzakhanian
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Barbara A Cornblatt
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Doreen M Olvet
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry, San Francisco, CA, USA
| | | | - Diana O Perkins
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Aysenil Belger
- University of North Carolina, Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA
| | - Larry J Seidman
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Theo G M van Erp
- University of California, Irvine, Department of Psychiatry and Human Behavior, Irvine, CA, USA
| | - Elaine F Walker
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Stephan Hamann
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Scott W Woods
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Tyrone D Cannon
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - R Todd Constable
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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567
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Finn ES. Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease. DIALOGUES IN CLINICAL NEUROSCIENCE 2016; 18:277-287. [PMID: 27757062 PMCID: PMC5067145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Functional brain connectivity measured with functional magnetic resonance imaging (fMRI) is a popular technique for investigating neural organization in both healthy subjects and patients with mental illness. Despite a rapidly growing body of literature, however, functional connectivity research has yet to deliver biomarkers that can aid psychiatric diagnosis or prognosis at the single-subject level. One impediment to developing such practical tools has been uncertainty regarding the ratio of intra- to interindividual variability in functional connectivity; in other words, how much variance is state- versus trait-related. Here, we review recent evidence that functional connectivity profiles are both reliable within subjects and unique across subjects, and that features of these profiles relate to behavioral phenotypes. Together, these results suggest the potential to discover reliable correlates of present and future illness and/or response to treatment in the strength of an individual's functional brain connections. Ultimately, this work could help develop personalized approaches to psychiatric illness.
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Affiliation(s)
- Emily S. Finn
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA
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568
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Scheinost D, Tokoglu F, Shen X, Finn ES, Noble S, Papademetris X, Constable RT. Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks. IEEE Trans Biomed Eng 2016; 63:2540-2549. [PMID: 27541328 DOI: 10.1109/tbme.2016.2600248] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
OBJECTIVE The aim of this study was to explore the relationship between global brain activity, changes in whole-brain connectivity, and changes in brain states across subjects using resting-state functional magnetic resonance imaging. METHODS We extended current methods that use a sparse set of coactivation patterns to extract critical time points in global brain activity. Critical activity time points were defined as points where the global signal is greater than one standard deviation above or below the average global signal. Four categories of critical points were defined along dimensions of global signal intensity and trajectory. Voxel-based methods were used to interrogate differences in connectivity between these critical points. RESULTS Several differences in connectivity were found in functional resting-state networks (RSNs) as a function of global activity. RSNs associated with cognitive functions in frontal, parietal, and subcortical regions exhibited greater whole-brain connectivity during lower global activity states. Meanwhile, RSNs associated with sensory functions exhibited greater whole-brain connectivity during the higher global activity states. Moreover, we present evidence that these results depend in part upon the standard deviation threshold used to define the critical points, suggesting critical points at different thresholds represent unique brain states. CONCLUSION Overall, the findings support the hypothesis that the brain oscillates through different states over the course of a resting-state study reflecting differences in RSN connectivity associated with global brain activity. SIGNIFICANCE Increased understanding of brain dynamics may help to elucidate individual differences in behavior and dysfunction.
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569
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Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M, Smith SM, Van Essen DC. A multi-modal parcellation of human cerebral cortex. Nature 2016; 536:171-178. [PMID: 27437579 PMCID: PMC4990127 DOI: 10.1038/nature18933] [Citation(s) in RCA: 2512] [Impact Index Per Article: 314.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 06/15/2016] [Indexed: 01/27/2023]
Abstract
Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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Affiliation(s)
- Matthew F Glasser
- Department of Neuroscience, Washington University Medical School, Saint Louis, Missouri 63110, USA
| | - Timothy S Coalson
- Department of Neuroscience, Washington University Medical School, Saint Louis, Missouri 63110, USA
| | - Emma C Robinson
- FMRIB centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
- Department of Computing, Imperial College, London SW7 2AZ, UK
| | - Carl D Hacker
- Department of Biomedical Engineering, Washington University, Saint Louis, Missouri 63110, USA
| | - John Harwell
- Department of Neuroscience, Washington University Medical School, Saint Louis, Missouri 63110, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Jesper Andersson
- FMRIB centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre Nijmegen, Postbus 9101, Nijmegen 6500 HB, The Netherlands
| | - Mark Jenkinson
- FMRIB centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Stephen M Smith
- FMRIB centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - David C Van Essen
- Department of Neuroscience, Washington University Medical School, Saint Louis, Missouri 63110, USA
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570
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Ma Z, Perez P, Ma Z, Liu Y, Hamilton C, Liang Z, Zhang N. Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration. Neuroimage 2016; 170:95-112. [PMID: 27393420 DOI: 10.1016/j.neuroimage.2016.07.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 06/06/2016] [Accepted: 07/04/2016] [Indexed: 12/23/2022] Open
Abstract
Connectivity-based parcellation approaches present an innovative method to segregate the brain into functionally specialized regions. These approaches have significantly advanced our understanding of the human brain organization. However, parallel progress in animal research is sparse. Using resting-state fMRI data and a novel, data-driven parcellation method, we have obtained robust functional parcellations of the rat brain. These functional parcellations reveal the regional specialization of the rat brain, which exhibited high within-parcel homogeneity and high reproducibility across animals. Graph analysis of the whole-brain network constructed based on these functional parcels indicates that the rat brain has a topological organization similar to humans, characterized by both segregation and integration. Our study also provides compelling evidence that the cingulate cortex is a functional hub region conserved from rodents to humans. Together, this study has characterized the rat brain specialization and integration, and has significantly advanced our understanding of the rat brain organization. In addition, it is valuable for studies of comparative functional neuroanatomy in mammalian brains.
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Affiliation(s)
- Zhiwei Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Pablo Perez
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Zilu Ma
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Yikang Liu
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Christina Hamilton
- The Neuroscience Program, The Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Zhifeng Liang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park 16802, USA; The Neuroscience Program, The Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
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571
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Intrinsic functional connectivity predicts individual differences in distractibility. Neuropsychologia 2016; 86:176-82. [DOI: 10.1016/j.neuropsychologia.2016.04.023] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 03/17/2016] [Accepted: 04/25/2016] [Indexed: 12/25/2022]
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572
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Hirose S, Osada T, Ogawa A, Tanaka M, Wada H, Yoshizawa Y, Imai Y, Machida T, Akahane M, Shirouzu I, Konishi S. Lateral-Medial Dissociation in Orbitofrontal Cortex-Hypothalamus Connectivity. Front Hum Neurosci 2016; 10:244. [PMID: 27303281 PMCID: PMC4880561 DOI: 10.3389/fnhum.2016.00244] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 05/09/2016] [Indexed: 12/24/2022] Open
Abstract
The orbitofrontal cortex (OFC) is involved in cognitive functions, and is also closely related to autonomic functions. The OFC is densely connected with the hypothalamus, a heterogeneous structure controlling autonomic functions that can be divided into two major parts: the lateral and the medial. Resting-state functional connectivity has allowed us to parcellate the cerebral cortex into putative functional areas based on the changes in the spatial pattern of connectivity in the cerebral cortex when a seed point is moved from one voxel to another. In the present high spatial-resolution fMRI study, we investigate the connectivity-based organization of the OFC with reference to the hypothalamus. The OFC was parcellated using resting-state functional connectivity in an individual subject approach, and then the functional connectivity was examined between the parcellated areas in the OFC and the lateral/medial hypothalamus. We found a functional double dissociation in the OFC: the lateral OFC (the lateral orbital gyrus) was more likely connected with the lateral hypothalamus, whereas the medial OFC (the medial orbital and rectal gyri) was more likely connected with the medial hypothalamus. These results demonstrate the fundamental heterogeneity of the OFC, and suggest a potential neural basis of the OFC–hypothalamic functional interaction.
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Affiliation(s)
- Satoshi Hirose
- Department of Neurophysiology, Juntendo University School of MedicineTokyo, Japan; Department of Physiology, The University of Tokyo School of MedicineTokyo, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of MedicineTokyo, Japan; Department of Physiology, The University of Tokyo School of MedicineTokyo, Japan
| | - Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine Tokyo, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine Tokyo, Japan
| | - Hiroyuki Wada
- Department of Radiology, NTT Medical Center Tokyo Tokyo, Japan
| | | | - Yoshio Imai
- Department of Radiology, NTT Medical Center Tokyo Tokyo, Japan
| | - Toru Machida
- Department of Radiology, NTT Medical Center TokyoTokyo, Japan; International University of Health and WelfareTokyo, Japan
| | - Masaaki Akahane
- Department of Radiology, NTT Medical Center Tokyo Tokyo, Japan
| | - Ichiro Shirouzu
- Department of Radiology, NTT Medical Center Tokyo Tokyo, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of MedicineTokyo, Japan; Department of Physiology, The University of Tokyo School of MedicineTokyo, Japan
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573
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Group-wise parcellation of the cortex through multi-scale spectral clustering. Neuroimage 2016; 136:68-83. [PMID: 27192437 DOI: 10.1016/j.neuroimage.2016.05.035] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Revised: 04/21/2016] [Accepted: 05/10/2016] [Indexed: 11/21/2022] Open
Abstract
The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
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574
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Wang C, Kipping J, Bao C, Ji H, Qiu A. Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering. Front Neurosci 2016; 10:188. [PMID: 27199650 PMCID: PMC4852537 DOI: 10.3389/fnins.2016.00188] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 04/14/2016] [Indexed: 11/13/2022] Open
Abstract
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children.
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Affiliation(s)
- Changqing Wang
- Graduate School for Integrative Sciences and Engineering, National University of Singapore Singapore, Singapore
| | - Judy Kipping
- Department of Biomedical Engineering, National University of Singapore Singapore, Singapore
| | - Chenglong Bao
- Department of Mathematics, National University of Singapore Singapore, Singapore
| | - Hui Ji
- Department of Mathematics, National University of Singapore Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of SingaporeSingapore, Singapore; Clinical Imaging Research Centre, National University of SingaporeSingapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and ResearchSingapore, Singapore
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575
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Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory. NPJ SCHIZOPHRENIA 2016; 2:16014. [PMID: 27336056 PMCID: PMC4898893 DOI: 10.1038/npjschz.2016.14] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 01/21/2016] [Accepted: 01/22/2016] [Indexed: 01/17/2023]
Abstract
A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response.
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576
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Zhang T, Zhu D, Jiang X, Zhang S, Kou Z, Guo L, Liu T. Group-wise consistent cortical parcellation based on connectional profiles. Med Image Anal 2016; 32:32-45. [PMID: 27054276 DOI: 10.1016/j.media.2016.02.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 02/29/2016] [Accepted: 02/29/2016] [Indexed: 10/22/2022]
Abstract
For decades, seeking common, consistent and corresponding anatomical/functional regions across individual brains via cortical parcellation has been a longstanding challenging problem. In our opinion, two major barriers to solve this problem are determining meaningful cortical boundaries that segregate homogeneous regions and establishing correspondences among parcellated regions of multiple brains. To establish a corresponding system across subjects, we recently developed the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system which possesses group-wise consistent white matter fiber connection patterns across individuals and thus provides a dense map of corresponding cortical landmarks. Despite this useful property, however, the DICCCOL landmarks are still far from covering the whole cerebral cortex and do not provide clear structural/functional cortical boundaries. To address the above limitation while leveraging the advantage of DICCCOL, in this paper, we present a novel approach for group-wise consistent parcellation of the cerebral cortex via a hierarchical scheme. In each hierarchical level, DICCCOLs are used as corresponding samples to automatically determine the cluster number so that other cortical surface vertices are iteratively classified into corresponding clusters across subjects within a group-wise classification framework. Experimental results showed that this approach can achieve consistent fine-granularity cortical parcellation with intrinsically-established structural correspondences across individual brains. Besides, comparisons with resting-state and task-based fMRI datasets demonstrated that the group-wise parcellation boundaries segregate functionally homogeneous areas.
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Affiliation(s)
- Tuo Zhang
- School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an 710072, China; Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA
| | - Dajiang Zhu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA
| | - Xi Jiang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA
| | - Shu Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA
| | - Zhifeng Kou
- Departments of Biomedical Engineering and Radiology, Wayne State University, Detroit, MI 48201, USA
| | - Lei Guo
- School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.
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577
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Khatamian YB, Golestani AM, Ragot DM, Chen JJ. Spin-Echo Resting-State Functional Connectivity in High-Susceptibility Regions: Accuracy, Reliability, and the Impact of Physiological Noise. Brain Connect 2016; 6:283-97. [PMID: 26842962 DOI: 10.1089/brain.2015.0365] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Gradient-echo (GE) echo-planar imaging (EPI) is the method of choice in blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) studies, as it demonstrates substantially higher BOLD sensitivity than its spin-echo (SE) counterpart. However, it is also well known that the GE-EPI signal is prone to signal dropouts and shifts due to susceptibility effects near air-tissue interfaces. SE-EPI, in contrast, is minimally affected by these artifacts. In this study, we quantify, for the first time, the sensitivity and specificity of SE and GE EPI for resting-state fMRI functional connectivity (fcMRI) mapping, using the 1000-brain fcMRI atlas (Yeo et al., 2011 ) as the pseudoground truth. Moreover, we assess the influence of physiological processes on resting-state BOLD measured using both regular and ultrafast GE and SE acquisitions. Our work demonstrates that SE-EPI and GE-EPI are associated with similar sensitivities, specificities, and intersubject reproducibility in fcMRI for most brain networks, generated using both seed-based analysis and independent component analysis. More importantly, SE-based fcMRI measurements demonstrated significantly higher sensitivity, specificity, and intersubject reproducibility in high-susceptibility regions, spanning the limbic and frontal networks in the 1000-brain atlas. In addition, SE-EPI is significantly less sensitive to prominent sources of physiological noise, including low-frequency respiratory volume and heart rate variations. Our work suggests that SE-EPI should be increasingly adopted in the study of networks spanning susceptibility-affected brain regions, including those that are important to memory, language, and emotion.
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Affiliation(s)
| | | | - Don M Ragot
- 1 Rotman Research Institute , Baycrest, Toronto, Canada .,2 Department of Medical Biophysics, University of Toronto , Toronto, Canada
| | - J Jean Chen
- 1 Rotman Research Institute , Baycrest, Toronto, Canada .,2 Department of Medical Biophysics, University of Toronto , Toronto, Canada
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578
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Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction. Med Image Anal 2016; 30:11-29. [PMID: 26849421 DOI: 10.1016/j.media.2016.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 01/05/2016] [Accepted: 01/07/2016] [Indexed: 12/14/2022]
Abstract
Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parcellate the whole human cortex. The main originality of the method is to compress for each gyrus the connectivity profiles used for the clustering without any anatomical prior information. This step takes into account the interindividual cortical and connectivity variability. To this end, we consider intersubject high density connectivity areas extracted using a surface-based watershed algorithm. A wide validation study has led to a fully automatic pipeline which is robust to variations in data preprocessing (tracking type, cortical mesh characteristics and boundaries of initial gyri), data characteristics (including number of subjects), and the main algorithmic parameters. A remarkable reproducibility is achieved in parcellation results for the whole cortex, leading to clear and stable cortical patterns. This reproducibility has been tested across non-overlapping subgroups and the validation is presented mainly on the pre- and postcentral gyri.
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579
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Bertolero MA, Yeo BTT, D'Esposito M. The modular and integrative functional architecture of the human brain. Proc Natl Acad Sci U S A 2015; 112:E6798-807. [PMID: 26598686 PMCID: PMC4679040 DOI: 10.1073/pnas.1510619112] [Citation(s) in RCA: 313] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author-topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions.
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Affiliation(s)
- Maxwell A Bertolero
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720; Department of Psychology, University of California, Berkeley, CA 94720;
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077; Clinical Imaging Research Centre, National University of Singapore, Singapore 117599; Singapore Institute for Neurotechnology, National University of Singapore, Singapore 117456; Memory Networks Programme, National University of Singapore, Singapore 119077
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720; Department of Psychology, University of California, Berkeley, CA 94720
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580
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Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT, Stufflebeam SM, Wang K, Wang X, Hong B, Liu H. Parcellating cortical functional networks in individuals. Nat Neurosci 2015; 18:1853-60. [PMID: 26551545 PMCID: PMC4661084 DOI: 10.1038/nn.4164] [Citation(s) in RCA: 315] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 10/14/2015] [Indexed: 12/19/2022]
Abstract
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.
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Affiliation(s)
- Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L. Buckner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Michael D. Fox
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daphne J. Holt
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avram J. Holmes
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Sophia Stoecklein
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Ludwig Maximilians University Munich, Institute of Clinical Radiology, Munich, Germany
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ruiqi Pan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Tianyi Qian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- Siemens Healthcare, MR Collaboration NE Asia, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Justin T. Baker
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Cambridge, MA, USA
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaomin Wang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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581
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Fan Y, Nickerson LD, Li H, Ma Y, Lyu B, Miao X, Zhuo Y, Ge J, Zou Q, Gao JH. Functional Connectivity-Based Parcellation of the Thalamus: An Unsupervised Clustering Method and Its Validity Investigation. Brain Connect 2015; 5:620-30. [DOI: 10.1089/brain.2015.0338] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Yang Fan
- Center for MRI Research, Peking University, Beijing, China
- Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
| | - Lisa D. Nickerson
- McLean Imaging Center, McLean Hospital, Belmont, Massachusetts
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Huanjie Li
- Department of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Yajun Ma
- Center for MRI Research, Peking University, Beijing, China
- Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
| | - Bingjiang Lyu
- Center for MRI Research, Peking University, Beijing, China
- Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
| | - Xinyuan Miao
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yan Zhuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jianqiao Ge
- Center for MRI Research, Peking University, Beijing, China
| | - Qihong Zou
- Center for MRI Research, Peking University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Peking University, Beijing, China
- Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
- McGovern Institute for Brain Research, Peking University, Beijing, China
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582
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Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 2015; 19:165-71. [PMID: 26595653 PMCID: PMC4696892 DOI: 10.1038/nn.4179] [Citation(s) in RCA: 600] [Impact Index Per Article: 66.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 10/27/2015] [Indexed: 12/17/2022]
Abstract
Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.
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Affiliation(s)
| | - Emily S Finn
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, Connecticut, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, USA.,Department of Neurobiology, Yale University, New Haven, Connecticut, USA
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583
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584
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Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 2015; 18:1664-71. [PMID: 26457551 PMCID: PMC5008686 DOI: 10.1038/nn.4135] [Citation(s) in RCA: 1529] [Impact Index Per Article: 169.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 09/11/2015] [Indexed: 12/17/2022]
Abstract
Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.
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Affiliation(s)
- Emily S. Finn
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT USA
| | - Xilin Shen
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT USA
| | - Dustin Scheinost
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT USA
| | | | - Jessica Huang
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT USA
| | - Marvin M. Chun
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT USA
- Department of Psychology, Yale University, New Haven, CT USA
- Department of Neurobiology, Yale University, New Haven, CT USA
| | - Xenophon Papademetris
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT USA
- Department of Biomedical Engineering, Yale University, New Haven, CT USA
| | - R. Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT USA
- Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT USA
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585
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A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magn Reson Imaging 2015; 34:209-18. [PMID: 26523655 DOI: 10.1016/j.mri.2015.10.036] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 10/25/2015] [Indexed: 11/22/2022]
Abstract
The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI's translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants' functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group's mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI=0.72-0.85) than with the Random atlases (JI=0.59-0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during rest and performing the n-back working memory task (r=0.75-0.80) than the Random atlases (r=0.64-0.72), further validating their utility. We expected regions governing higher-order cognition (such as frontal and anterior temporal lobes) to show greatest difference between Task and Rest atlases; contrary to expectations, these areas had greatest similarity between atlases. Our findings indicate that atlases derived from parcellation of task-based and resting-state fMRI data are highly comparable, and existing resting-state atlases are suitable for task-based analyses. We introduce an anatomically labeled fMRI-derived whole-brain human atlas for future Cognitive Connectome analyses.
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586
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Garcés P, Pereda E, Hernández-Tamames JA, Del-Pozo F, Maestú F, Pineda-Pardo JÁ. Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI. Hum Brain Mapp 2015; 37:20-34. [PMID: 26503502 PMCID: PMC5132061 DOI: 10.1002/hbm.22995] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Revised: 08/26/2015] [Accepted: 08/27/2015] [Indexed: 12/28/2022] Open
Abstract
Structural and functional connectivity (SC and FC) have received much attention over the last decade, as they offer unique insight into the coordination of brain functioning. They are often assessed independently with three imaging modalities: SC using diffusion‐weighted imaging (DWI), FC using functional magnetic resonance imaging (fMRI), and magnetoencephalography/electroencephalography (MEG/EEG). DWI provides information about white matter organization, allowing the reconstruction of fiber bundles. fMRI uses blood‐oxygenation level‐dependent (BOLD) contrast to indirectly map neuronal activation. MEG and EEG are direct measures of neuronal activity, as they are sensitive to the synchronous inputs in pyramidal neurons. Seminal studies have targeted either the electrophysiological substrate of BOLD or the anatomical basis of FC. However, multimodal comparisons have been scarcely performed, and the relation between SC, fMRI‐FC, and MEG‐FC is still unclear. Here we present a systematic comparison of SC, resting state fMRI‐FC, and MEG‐FC between cortical regions, by evaluating their similarities at three different scales: global network, node, and hub distribution. We obtained strong similarities between the three modalities, especially for the following pairwise combinations: SC and fMRI‐FC; SC and MEG‐FC at theta, alpha, beta and gamma bands; and fMRI‐FC and MEG‐FC in alpha and beta. Furthermore, highest node similarity was found for regions of the default mode network and primary motor cortex, which also presented the highest hubness score. Distance was partially responsible for these similarities since it biased all three connectivity estimates, but not the unique contributor, since similarities remained after controlling for distance. Hum Brain Mapp 37:20–34, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain
| | - Ernesto Pereda
- Department of Industrial Engineering, Institute of Biomedical Technology (ITB-CIBINCAN), Universidad De La Laguna, Avda. Astrofísico Fco. Sánchez S/N, La Laguna, Tenerife, 38205, Spain
| | - Juan A Hernández-Tamames
- Department of Electronics Technology, Universidad Rey Juan Carlos, C/Tulipán S/N, Móstoles, Madrid, 28933, Spain
| | - Francisco Del-Pozo
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - José Ángel Pineda-Pardo
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica De Madrid, Campus De Montegancedo, Pozuelo De Alarcón, Madrid, 28223, Spain.,CINAC, HM Puerta del Sur, Hospitales de Madrid, 28938 Móstoles, and CEU-San Pablo University, 28003, Madrid, Spain
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587
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Cheng H, Newman S, Goñi J, Kent JS, Howell J, Bolbecker A, Puce A, O’Donnell BF, Hetrick WP. Nodal centrality of functional network in the differentiation of schizophrenia. Schizophr Res 2015; 168:345-52. [PMID: 26299706 PMCID: PMC4591247 DOI: 10.1016/j.schres.2015.08.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Revised: 08/05/2015] [Accepted: 08/07/2015] [Indexed: 01/01/2023]
Abstract
A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.
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Affiliation(s)
- Hu Cheng
- Imaging Research Facility, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
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588
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Joliot M, Jobard G, Naveau M, Delcroix N, Petit L, Zago L, Crivello F, Mellet E, Mazoyer B, Tzourio-Mazoyer N. AICHA: An atlas of intrinsic connectivity of homotopic areas. J Neurosci Methods 2015. [DOI: 10.1016/j.jneumeth.2015.07.013] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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589
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Eickhoff SB, Thirion B, Varoquaux G, Bzdok D. Connectivity-based parcellation: Critique and implications. Hum Brain Mapp 2015; 36:4771-92. [PMID: 26409749 DOI: 10.1002/hbm.22933] [Citation(s) in RCA: 192] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Revised: 07/22/2015] [Accepted: 07/30/2015] [Indexed: 12/13/2022] Open
Abstract
Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.
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Affiliation(s)
- Simon B Eickhoff
- Institut Für Neurowissenschaften Und Medizin (INM-1), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.,Institut Für Klinische Neurowissenschaften Und Medizinische Psychologie, Heinrich-Heine Universität Düsseldorf, Düsseldorf, 40225, Germany
| | - Bertrand Thirion
- Parietal Team, INRIA, Neurospin, Bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France
| | - Gaël Varoquaux
- Parietal Team, INRIA, Neurospin, Bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France
| | - Danilo Bzdok
- Institut Für Neurowissenschaften Und Medizin (INM-1), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.,Institut Für Klinische Neurowissenschaften Und Medizinische Psychologie, Heinrich-Heine Universität Düsseldorf, Düsseldorf, 40225, Germany.,Parietal Team, INRIA, Neurospin, Bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France.,Department of Psychiatry, Psychotherapy and Psychosomatics, Uniklinik RWTH, 52074, Aachen, Germany
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590
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Scheinost D, Kwon SH, Shen X, Lacadie C, Schneider KC, Dai F, Ment LR, Constable RT. Preterm birth alters neonatal, functional rich club organization. Brain Struct Funct 2015; 221:3211-22. [PMID: 26341628 DOI: 10.1007/s00429-015-1096-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2015] [Accepted: 07/25/2015] [Indexed: 10/23/2022]
Abstract
Alterations in neural networks are associated with the cognitive difficulties of the prematurely born. Using functional magnetic resonance imaging, we analyzed functional connectivity for preterm (PT) and term neonates at term equivalent age. Specifically, we constructed whole-brain networks and examined rich club (RC) organization, a common construct among complex systems where important (or "rich") nodes connect preferentially to other important nodes. Both PT and term neonates showed RC organization with PT neonates exhibiting significantly reduced connections between these RC nodes. Additionally, PT neonates showed evidence of weaker functional segregation. Our results suggest that PT birth is associated with fundamental changes of functional organization in the developing brain.
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Affiliation(s)
- Dustin Scheinost
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA.
| | - Soo Hyun Kwon
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA
| | - Xilin Shen
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA
| | - Cheryl Lacadie
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA
| | - Karen C Schneider
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA
| | - Feng Dai
- Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USA
| | - Laura R Ment
- Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA.,Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA.,Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
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591
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Langen CD, White T, Ikram MA, Vernooij MW, Niessen WJ. Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia. PLoS One 2015; 10:e0137484. [PMID: 26331844 PMCID: PMC4557994 DOI: 10.1371/journal.pone.0137484] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 08/16/2015] [Indexed: 11/18/2022] Open
Abstract
Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.
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Affiliation(s)
- Carolyn D. Langen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- * E-mail:
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
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592
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Ferreira LK, Regina ACB, Kovacevic N, Martin MDGM, Santos PP, Carneiro CDG, Kerr DS, Amaro E, McIntosh AR, Busatto GF. Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders. Cereb Cortex 2015; 26:3851-65. [DOI: 10.1093/cercor/bhv190] [Citation(s) in RCA: 132] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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593
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Sohn WS, Yoo K, Lee YB, Seo SW, Na DL, Jeong Y. Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis. Front Neurosci 2015; 9:280. [PMID: 26321904 PMCID: PMC4531302 DOI: 10.3389/fnins.2015.00280] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 07/24/2015] [Indexed: 01/22/2023] Open
Abstract
The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs) for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with aging.
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Affiliation(s)
- William S Sohn
- Department of Bio and Brain Engineering, KAIST Daejeon, South Korea
| | - Kwangsun Yoo
- Department of Bio and Brain Engineering, KAIST Daejeon, South Korea
| | - Young-Beom Lee
- Department of Bio and Brain Engineering, KAIST Daejeon, South Korea
| | - Sang W Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University Seoul, South Korea ; Neuroscience Center, Samsung Medical Center Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University Seoul, South Korea ; Neuroscience Center, Samsung Medical Center Seoul, South Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, KAIST Daejeon, South Korea
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594
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Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT. The (in)stability of functional brain network measures across thresholds. Neuroimage 2015; 118:651-61. [PMID: 26021218 DOI: 10.1016/j.neuroimage.2015.05.046] [Citation(s) in RCA: 162] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Revised: 05/12/2015] [Accepted: 05/16/2015] [Indexed: 11/25/2022] Open
Abstract
The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.
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Affiliation(s)
| | - Dustin Scheinost
- Department of Diagnostic Radiology, Yale School of Medicine, USA
| | - Emily S Finn
- Interdepartmental Neuroscience Program, Yale University, USA
| | - Xilin Shen
- Department of Diagnostic Radiology, Yale School of Medicine, USA
| | - R Todd Constable
- Department of Diagnostic Radiology, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
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595
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Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding. NEUROIMAGE-CLINICAL 2015; 8:536-42. [PMID: 26110111 PMCID: PMC4477107 DOI: 10.1016/j.nicl.2015.05.009] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Revised: 05/15/2015] [Accepted: 05/19/2015] [Indexed: 11/23/2022]
Abstract
Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications. Focal epilepsy is a neurological disorder known to affect neural networks. We examine fMRI network changes during rest in extratemporal epilepsy patients. Extratemporal epilepsy subjects show increased (isolated) local network regularity. Network isolation may represent a ‘seizure-protection’ mechanism in focal epilepsy.
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596
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Abstract
In blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI), current methods typically acquire ∼ 500,000 imaging voxels at each time point, and then use computer algorithms to reduce this data to the coefficients of a few hundred parcels or networks. This suggests that the amount of relevant information present in the fMRI signal is relatively small, and presents an opportunity to greatly improve the speed and signal to noise ratio (SNR) of the fMRI process. In this work, a theoretical framework is presented for calculating the coefficients of functional networks directly from highly undersampled fMRI data. Using predefined functional parcellations or networks and a compact k-space trajectory that samples data at optimal spatial scales, the problem of estimating network coefficients is reformulated to allow for direct least squares estimation, without Fourier encoding. By simulation, this approach is shown to allow for acceleration of the imaging process under ideal circumstances by nearly three orders of magnitude.
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Affiliation(s)
- Eric C Wong
- Departments of Radiology and Psychiatry, University of California , San Diego, La Jolla, California
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597
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Ryali S, Chen T, Padmanabhan A, Cai W, Menon V. Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI. J Neurosci Methods 2015; 240:128-40. [PMID: 25450335 PMCID: PMC4276438 DOI: 10.1016/j.jneumeth.2014.11.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 11/19/2014] [Accepted: 11/20/2014] [Indexed: 01/18/2023]
Abstract
BACKGROUND Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data. NEW METHOD To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index. RESULTS A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum. COMPARISON WITH EXISTING METHODS Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters. CONCLUSIONS A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.
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Affiliation(s)
- Srikanth Ryali
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States.
| | - Tianwen Chen
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Aarthi Padmanabhan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Weidong Cai
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States; Program in Neuroscience, Stanford University School of Medicine, Stanford, CA 94305, United States; Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, United States
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598
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Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2015. [PMID: 26221668 DOI: 10.1007/978-3-319-19992-4_7] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.
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599
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Honnorat N, Eavani H, Satterthwaite TD, Gur RE, Gur RC, Davatzikos C. GraSP: geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex. Neuroimage 2014; 106:207-21. [PMID: 25462796 DOI: 10.1016/j.neuroimage.2014.11.008] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 08/21/2014] [Accepted: 11/04/2014] [Indexed: 01/21/2023] Open
Abstract
Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method's utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods.
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Affiliation(s)
- N Honnorat
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - H Eavani
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - T D Satterthwaite
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - R E Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - R C Gur
- Brain and Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - C Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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600
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Gordon EM, Laumann TO, Adeyemo B, Huckins JF, Kelley WM, Petersen SE. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cereb Cortex 2014; 26:288-303. [PMID: 25316338 DOI: 10.1093/cercor/bhu239] [Citation(s) in RCA: 850] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.
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Affiliation(s)
| | | | | | - Jeremy F Huckins
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - William M Kelley
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Steven E Petersen
- Department of Neurology Department of Psychology Department of Radiology Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, USA
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