1501
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O'Connor D, Potler NV, Kovacs M, Xu T, Ai L, Pellman J, Vanderwal T, Parra LC, Cohen S, Ghosh S, Escalera J, Grant-Villegas N, Osman Y, Bui A, Craddock RC, Milham MP. The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions. Gigascience 2017; 6:1-14. [PMID: 28369458 PMCID: PMC5466711 DOI: 10.1093/gigascience/giw011] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 12/05/2016] [Indexed: 01/08/2023] Open
Abstract
Background Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session. Findings Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities. Conclusions This resource provides a test-bed for quantifying the reliability of connectivity indices across subjects, conditions and time. The resource can be used to compare and optimize different frameworks for measuring connectivity and data collection parameters such as scan length. Additionally, investigators can explore the unique perspectives of the brain's functional architecture offered by each of the scan conditions.
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Affiliation(s)
- David O'Connor
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | | | - Meagan Kovacs
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Lei Ai
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - John Pellman
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | | | | | - Samantha Cohen
- The Graduate Center of the City University of New York, New York, NY
| | | | - Jasmine Escalera
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | | | - Yael Osman
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Anastasia Bui
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
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1502
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Vanderwal T, Eilbott J, Finn ES, Craddock RC, Turnbull A, Castellanos FX. Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 2017. [PMID: 28625875 DOI: 10.1016/j.neuroimage.2017.06.027] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.
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Affiliation(s)
- Tamara Vanderwal
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA.
| | - Jeffrey Eilbott
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - Emily S Finn
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - R Cameron Craddock
- Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA; Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA
| | - Adam Turnbull
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - F Xavier Castellanos
- Child Study Center at New York University Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA
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1503
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Lu KH, Jeong JY, Wen H, Liu Z. Spontaneous activity in the visual cortex is organized by visual streams. Hum Brain Mapp 2017; 38:4613-4630. [PMID: 28608643 DOI: 10.1002/hbm.23687] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 06/05/2017] [Accepted: 06/07/2017] [Indexed: 12/12/2022] Open
Abstract
Large-scale functional networks have been extensively studied using resting state functional magnetic resonance imaging (fMRI). However, the pattern, organization, and function of fine-scale network activity remain largely unknown. Here, we characterized the spontaneously emerging visual cortical activity by applying independent component (IC) analysis to resting state fMRI signals exclusively within the visual cortex. In this subsystem scale, we observed about 50 spatially ICs that were reproducible within and across subjects, and analyzed their spatial patterns and temporal relationships to reveal the intrinsic parcellation and organization of the visual cortex. The resulting visual cortical parcels were aligned with the steepest gradient of cortical myelination, and were organized into functional modules segregated along the dorsal/ventral pathways and foveal/peripheral early visual areas. Cortical distance could partly explain intra-hemispherical functional connectivity, but not interhemispherical connectivity; after discounting the effect of anatomical affinity, the fine-scale functional connectivity still preserved a similar visual-stream-specific modular organization. Moreover, cortical retinotopy, folding, and cytoarchitecture impose limited constraints to the organization of resting state activity. Given these findings, we conclude that spontaneous activity patterns in the visual cortex are primarily organized by visual streams, likely reflecting feedback network interactions. Hum Brain Mapp 38:4613-4630, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Kun-Han Lu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Jun Young Jeong
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Haiguang Wen
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Zhongming Liu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana
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1504
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Medaglia JD, Zurn P, Sinnott-Armstrong W, Bassett DS. Mind control as a guide for the mind. Nat Hum Behav 2017. [DOI: 10.1038/s41562-017-0119] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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1505
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Ferguson MA, Anderson JS, Spreng RN. Fluid and flexible minds: Intelligence reflects synchrony in the brain's intrinsic network architecture. Netw Neurosci 2017; 1:192-207. [PMID: 29911673 PMCID: PMC5988392 DOI: 10.1162/netn_a_00010] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 03/14/2017] [Indexed: 12/31/2022] Open
Abstract
Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease. In our study, we aimed to understand how individual differences in intellectual functioning are reflected in the intrinsic network architecture of the human brain. We applied statistical methods, known as spectral decompositions, in order to identify individual differences in the synchronous patterns of spontaneous brain activity that reliably predict core aspects of human intelligence. The synchrony of brain activity at rest across multiple discrete neural networks demonstrated positive relationships with fluid intelligence. In contrast, global synchrony within the brain’s network architecture reliably, and inversely, predicted mental flexibility, a core facet of intellectual functioning. The multinetwork systems approach described here represents a methodological and conceptual extension of earlier efforts that related differences in intellectual ability to variations in specific brain regions, networks, or their interactions. Our findings suggest that the neural basis of complex, integrative cognitive functions can be most completely understood from the perspective of network neuroscience.
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Affiliation(s)
- Michael A Ferguson
- Laboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, Cornell University, Ithaca, NY, 14853.,Departments of Bioengineering and Neuroradiology, University of Utah, Salt Lake City, UT, 84132
| | - Jeffrey S Anderson
- Departments of Bioengineering and Neuroradiology, University of Utah, Salt Lake City, UT, 84132
| | - R Nathan Spreng
- Laboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, Cornell University, Ithaca, NY, 14853
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1506
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Sadeghi M, Khosrowabadi R, Bakouie F, Mahdavi H, Eslahchi C, Pouretemad H. Screening of autism based on task-free fMRI using graph theoretical approach. Psychiatry Res Neuroimaging 2017; 263:48-56. [PMID: 28324694 DOI: 10.1016/j.pscychresns.2017.02.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 01/30/2017] [Accepted: 02/09/2017] [Indexed: 01/07/2023]
Abstract
Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data. Then, autism screening was developed based on characteristics of the functional networks using the following steps: A) local and global parameters of the brain functional network were calculated using graph theory. B) network parameters of the ASDs were statistically compared to the HCs. C) significantly altered parameters were used as input features of the screening system. D) performance of the system was verified using various classification techniques. The support vector machine showed superiority to others with an accuracy of 92%. Subsequently, reliability of the results was examined using an independent dataset including 20 ASDs and 20 HCs. Our findings suggest that local parameters of the brain functional network, despite the individual variability, can potentially be used for autism screening.
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Affiliation(s)
- Masoumeh Sadeghi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran; Department of Computer Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
| | - Fatemeh Bakouie
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Hoda Mahdavi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Hamidreza Pouretemad
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran; Faculty of Psychology and Educational Sciences, Shahid Beheshti University, Tehran, Iran
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1507
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Sohn WS, Lee TY, Yoo K, Kim M, Yun JY, Hur JW, Yoon YB, Seo SW, Na DL, Jeong Y, Kwon JS. Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks. Front Neurosci 2017; 11:238. [PMID: 28507502 PMCID: PMC5410606 DOI: 10.3389/fnins.2017.00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 04/11/2017] [Indexed: 11/13/2022] Open
Abstract
Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.
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Affiliation(s)
- William S Sohn
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea
| | - Tae Young Lee
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Kwangsun Yoo
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Minah Kim
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Ji-Won Hur
- Department of Psychology, Chung-Ang UniversitySeoul, South Korea
| | - Youngwoo Bryan Yoon
- Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea.,Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea.,Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
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1508
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Gorges M, Roselli F, Müller HP, Ludolph AC, Rasche V, Kassubek J. Functional Connectivity Mapping in the Animal Model: Principles and Applications of Resting-State fMRI. Front Neurol 2017; 8:200. [PMID: 28539914 PMCID: PMC5423907 DOI: 10.3389/fneur.2017.00200] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 04/24/2017] [Indexed: 12/25/2022] Open
Abstract
"Resting-state" fMRI has substantially contributed to the understanding of human and non-human functional brain organization by the analysis of correlated patterns in spontaneous activity within dedicated brain systems. Spontaneous neural activity is indirectly measured from the blood oxygenation level-dependent signal as acquired by echo planar imaging, when subjects quietly "resting" in the scanner. Animal models including disease or knockout models allow a broad spectrum of experimental manipulations not applicable in humans. The non-invasive fMRI approach provides a promising tool for cross-species comparative investigations. This review focuses on the principles of "resting-state" functional connectivity analysis and its applications to living animals. The translational aspect from in vivo animal models toward clinical applications in humans is emphasized. We introduce the fMRI-based investigation of the non-human brain's hemodynamics, the methodological issues in the data postprocessing, and the functional data interpretation from different abstraction levels. The longer term goal of integrating fMRI connectivity data with structural connectomes obtained with tracing and optical imaging approaches is presented and will allow the interrogation of fMRI data in terms of directional flow of information and may identify the structural underpinnings of observed functional connectivity patterns.
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Affiliation(s)
- Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Francesco Roselli
- Department of Neurology, University of Ulm, Ulm, Germany
- Department of Anatomy and Cell Biology, University of Ulm, Ulm, Germany
| | | | | | - Volker Rasche
- Core Facility Small Animal MRI, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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1509
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Fortenbaugh FC, Corbo V, Poole V, McGlinchey R, Milberg W, Salat D, DeGutis J, Esterman M. Interpersonal early-life trauma alters amygdala connectivity and sustained attention performance. Brain Behav 2017; 7:e00684. [PMID: 28523226 PMCID: PMC5434189 DOI: 10.1002/brb3.684] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 01/06/2017] [Accepted: 02/16/2017] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Interpersonal early life trauma (I-ELT) is associated with a myriad of functional impairments in adulthood, increased risk of drug addiction, and neuropsychiatric disorders. While deficits in emotional regulation and amygdala functioning are well characterized, deficits in general cognitive functioning have also been documented. However, the neural underpinnings of cognitive dysfunction in adults with a history of I-ELT and the potential relationship between amygdala-based functional connectivity and behavioral performance are currently poorly understood. This study examined how I-ELT affects the cognitive and neural mechanisms supporting sustained attention. METHODS A total of 66 Veterans (18 with and 48 without a history of I-ELT) completed a nonemotional sustained attention task during functional MRI. RESULTS The individuals with I-ELT showed significant impairments in sustained attention (i.e., higher error rates, greater response variability). This cohort exhibited increased amygdala functional connectivity with the prefrontal cortex and decreased functional connectivity with the parahippocampal gyrus when compared to those without I-ELT. These connections were significantly correlated with individual differences in sustained attention performance. Notably, classification analyses revealed that the pattern of amygdala connectivity across the whole brain was able to classify I-ELT status with 70% accuracy. CONCLUSION These results provide evidence of a lasting negative impact for those with a history of I-ELT on sustained attention ability. They also highlight a critical role for amygdala functioning in cognitive control and sustained attention for those with a history of I-ELT, which may underlie the observed attention deficits in clinical assessments and cognitive tests involving both emotional and nonemotional stimuli.
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Affiliation(s)
- Francesca C Fortenbaugh
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Department of Psychiatry Harvard Medical School Boston MA USA
| | - Vincent Corbo
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Department of Psychology School of Arts and Science Southern New Hampshire University Manchester NH USA
| | - Victoria Poole
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Institute of Aging Research Hebrew SeniorLife Boston MA USA
| | - Regina McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Department of Psychiatry Harvard Medical School Boston MA USA
| | - William Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Department of Psychiatry Harvard Medical School Boston MA USA
| | - David Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA
| | - Joseph DeGutis
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Department of Medicine Harvard Medical School Boston MA USA
| | - Michael Esterman
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education, and Clinical Center (GRECC) VA Boston Healthcare System Boston MA USA.,Neuroimaging Research for Veterans (NeRVe) Center VA Boston Healthcare System Boston MA USA.,Department of Psychiatry Boston University School of Medicine Boston MA USA
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1510
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Liao X, Cao M, Xia M, He Y. Individual differences and time-varying features of modular brain architecture. Neuroimage 2017; 152:94-107. [DOI: 10.1016/j.neuroimage.2017.02.066] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Revised: 02/18/2017] [Accepted: 02/23/2017] [Indexed: 01/07/2023] Open
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1511
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Nikolaidis A, Baniqued PL, Kranz MB, Scavuzzo CJ, Barbey AK, Kramer AF, Larsen RJ. Multivariate Associations of Fluid Intelligence and NAA. Cereb Cortex 2017; 27:2607-2616. [PMID: 27005991 DOI: 10.1093/cercor/bhw070] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measure N-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence.
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Affiliation(s)
- Aki Nikolaidis
- Beckman Institute for Advanced Science and Technology.,Neuroscience Program and
| | - Pauline L Baniqued
- Beckman Institute for Advanced Science and Technology.,Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Michael B Kranz
- Beckman Institute for Advanced Science and Technology.,Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Claire J Scavuzzo
- Neuroscience Program and.,Psychology Department, University of Alberta, Edmonton, Alberta, Canada
| | - Aron K Barbey
- Beckman Institute for Advanced Science and Technology
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology.,Neuroscience Program and.,Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ryan J Larsen
- Beckman Institute for Advanced Science and Technology
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1512
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Dimitriadis SI, Salis C, Tarnanas I, Linden DE. Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). Front Neuroinform 2017; 11:28. [PMID: 28491032 PMCID: PMC5405139 DOI: 10.3389/fninf.2017.00028] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/29/2017] [Indexed: 12/25/2022] Open
Abstract
The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.
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Affiliation(s)
- Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff UniversityCardiff, UK.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, UK.,School of Psychology, Cardiff UniversityCardiff, UK.,Neuroinformatics.GRoup, School of Psychology, Cardiff UniversityCardiff, UK
| | - Christos Salis
- Department of Informatics and Telecommunications Engineering, University of Western MacedoniaKozani, Greece
| | - Ioannis Tarnanas
- Health-IS Lab, Chair of Information Management, ETH ZurichZurich, Switzerland.,3rd Department of Neurology, Medical School, Aristotle University of ThessalonikiThessaloniki, Greece
| | - David E Linden
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff UniversityCardiff, UK.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, UK.,Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff UniversityCardiff, UK
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1513
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van Duinkerken E, Schoonheim MM, IJzerman RG, Moll AC, Landeira-Fernandez J, Klein M, Diamant M, Snoek FJ, Barkhof F, Wink AM. Altered eigenvector centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus. Hum Brain Mapp 2017; 38:3623-3636. [PMID: 28429383 DOI: 10.1002/hbm.23617] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 04/04/2017] [Accepted: 04/06/2017] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector centrality mapping (ECM) and degree centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree centrality) of regions within the whole-brain network hierarchy. METHODS Therefore, ECM and degree centrality of resting-state functional MRI-scans were compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition. RESULTS In all patients versus controls, ECM and degree centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (PFWE < 0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right cuneus and occipital fusiform gyrus versus controls (PFWE < 0.05). In all patients, ECM and degree centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (PFWE < 0.05), but not to subcortical gray matter volume or cognition (PFDR > 0.05). CONCLUSION The findings suggested reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression. Hum Brain Mapp 38:3623-3636, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Eelco van Duinkerken
- Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands.,Amsterdam Diabetes Center/Department of Internal Medicine, VU University Medical Center, Amsterdam, The Netherlands.,Department of Psychology, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil
| | - Menno M Schoonheim
- Department of Anatomy and Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Richard G IJzerman
- Amsterdam Diabetes Center/Department of Internal Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Jesus Landeira-Fernandez
- Department of Psychology, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil
| | - Martin Klein
- Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands
| | - Michaela Diamant
- Amsterdam Diabetes Center/Department of Internal Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Frank J Snoek
- Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Alle-Meije Wink
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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1514
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Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 2017; 170:5-30. [PMID: 28412442 DOI: 10.1016/j.neuroimage.2017.04.014] [Citation(s) in RCA: 198] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/15/2017] [Accepted: 04/05/2017] [Indexed: 11/21/2022] Open
Abstract
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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1515
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Liao X, Vasilakos AV, He Y. Small-world human brain networks: Perspectives and challenges. Neurosci Biobehav Rev 2017; 77:286-300. [PMID: 28389343 DOI: 10.1016/j.neubiorev.2017.03.018] [Citation(s) in RCA: 232] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/19/2017] [Accepted: 03/31/2017] [Indexed: 12/15/2022]
Abstract
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
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Affiliation(s)
- Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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1516
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Rosenberg MD, Finn ES, Scheinost D, Constable RT, Chun MM. Characterizing Attention with Predictive Network Models. Trends Cogn Sci 2017; 21:290-302. [PMID: 28238605 PMCID: PMC5366090 DOI: 10.1016/j.tics.2017.01.011] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 01/16/2017] [Accepted: 01/25/2017] [Indexed: 11/22/2022]
Abstract
Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.
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Affiliation(s)
- M D Rosenberg
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - E S Finn
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - D Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R T Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - M M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
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1517
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Razoux F, Russig H, Mueggler T, Baltes C, Dikaiou K, Rudin M, Mansuy IM. Transgenerational disruption of functional 5-HT 1AR-induced connectivity in the adult mouse brain by traumatic stress in early life. Mol Psychiatry 2017; 22:519-526. [PMID: 27671475 DOI: 10.1038/mp.2016.146] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 04/06/2016] [Accepted: 05/06/2016] [Indexed: 11/09/2022]
Abstract
Traumatic stress in early life is a strong risk factor for psychiatric disorders that can affect individuals across several generations. Although the underlying mechanisms have been proposed to implicate serotonergic transmission in the brain, the neural circuits involved remain poorly delineated. Using pharmacological functional magnetic resonance imaging in mice, we demonstrate that traumatic stress in postnatal life alters 5-HT1A receptor-evoked local and global functions in both, the exposed animals and their progeny when adult. Disrupted functional connectivity is consistent across generations and match limbic circuits implicated in mood disorders, but also networks not previously linked to traumatic stress. These findings underscore the neurobiology and functional mapping of transgenerational effects of early life experiences.
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Affiliation(s)
- F Razoux
- Laboratory of Neuroepigenetics, University and ETH Zurich, Brain Research Institute, Center for Neuroscience Zürich, Zurich, Switzerland.,Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - H Russig
- Laboratory of Neuroepigenetics, University and ETH Zurich, Brain Research Institute, Center for Neuroscience Zürich, Zurich, Switzerland
| | - T Mueggler
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - C Baltes
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - K Dikaiou
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - M Rudin
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Institute of Pharmacology and Toxicology, University of Zurich, Center for Neuroscience Zürich, Zurich, Switzerland
| | - I M Mansuy
- Laboratory of Neuroepigenetics, University and ETH Zurich, Brain Research Institute, Center for Neuroscience Zürich, Zurich, Switzerland
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1518
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Betzel RF, Satterthwaite TD, Gold JI, Bassett DS. Positive affect, surprise, and fatigue are correlates of network flexibility. Sci Rep 2017; 7:520. [PMID: 28364117 PMCID: PMC5428446 DOI: 10.1038/s41598-017-00425-z] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 02/21/2017] [Indexed: 12/02/2022] Open
Abstract
Advances in neuroimaging have made it possible to reconstruct functional networks from the activity patterns of brain regions distributed across the cerebral cortex. Recent work has shown that flexible reconfiguration of human brain networks over short timescales supports cognitive flexibility and learning. However, modulating network flexibility to enhance learning requires an understanding of an as-yet unknown relationship between flexibility and brain state. Here, we investigate the relationship between network flexibility and affect, leveraging an unprecedented longitudinal data set. We demonstrate that indices associated with positive mood and surprise are both associated with network flexibility - positive mood portends a more flexible brain while increased levels of surprise portend a less flexible brain. In both cases, these relationships are driven predominantly by a subset of brain regions comprising the somatomotor system. Our results simultaneously suggest a network-level mechanism underlying learning deficits in mood disorders as well as a potential target - altering an individual's mood or task novelty - to improve learning.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Neuropsychiatry Section, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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1519
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1520
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Takamura T, Hanakawa T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J Neural Transm (Vienna) 2017; 124:821-839. [PMID: 28337552 DOI: 10.1007/s00702-017-1710-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 03/14/2017] [Indexed: 12/15/2022]
Abstract
Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).
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Affiliation(s)
- T Takamura
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - T Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan.
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1521
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Jiang Y, Abiri R, Zhao X. Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback. Front Aging Neurosci 2017; 9:52. [PMID: 28348527 PMCID: PMC5346575 DOI: 10.3389/fnagi.2017.00052] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 02/22/2017] [Indexed: 12/03/2022] Open
Abstract
Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.
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Affiliation(s)
- Yang Jiang
- Aging Brain and Cognition Laboratory, Department of Behavioral Science, College of Medicine, University of KentuckyLexington, KY, USA; Sanders-Brown Center on Aging, College of Medicine, University of KentuckyLexington, KY, USA
| | - Reza Abiri
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee Knoxville, TN, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of TennesseeKnoxville, TN, USA; Institute for Medical Engineering and Science, Massachusetts Institute of TechnologyCambridge, MA, USA
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1522
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1523
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Sato JR, White TP, Biazoli CE. Commentary: A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity. Front Neurosci 2017; 11:85. [PMID: 28275335 PMCID: PMC5319983 DOI: 10.3389/fnins.2017.00085] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 02/07/2017] [Indexed: 12/24/2022] Open
Affiliation(s)
- João R Sato
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo Andre, Brazil
| | - Thomas P White
- School of Psychology, University of Birmingham Birmingham, UK
| | - Claudinei E Biazoli
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo Andre, Brazil
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1524
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Berry AS, Sarter M, Lustig C. Distinct Frontoparietal Networks Underlying Attentional Effort and Cognitive Control. J Cogn Neurosci 2017; 29:1212-1225. [PMID: 28253080 DOI: 10.1162/jocn_a_01112] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We investigated the brain activity patterns associated with stabilizing performance during challenges to attention. Our findings revealed distinct patterns of frontoparietal activity and functional connectivity associated with increased attentional effort versus preserved performance during challenged attention. Participants performed a visual signal detection task with and without presentation of a perceptual-attention challenge (changing background). The challenge condition increased activation in frontoparietal regions including right mid-dorsal/dorsolateral PFC (RPFC), approximating Brodmann's area 9, and superior parietal cortex. We found that greater behavioral impact of the challenge condition was correlated with greater RPFC activation, suggesting that increased engagement of cognitive control regions is not always sufficient to maintain high levels of performance. Functional connectivity between RPFC and ACC increased during the challenge condition and was also associated with performance declines, suggesting that the level of synchronized engagement of these regions reflects individual differences in attentional effort. Pretask, resting-state RPFC-ACC connectivity did not predict subsequent performance, suggesting that RPFC-ACC connectivity increased dynamically during task performance in response to performance decrement and error feedback. In contrast, functional connectivity between RPFC and superior parietal cortex not only during the task but also during pretask rest was associated with preserved performance in the challenge condition. Together, these data suggest that resting frontoparietal connectivity predicts performance on attention tasks that rely on those same cognitive control networks and that, under challenging conditions, other control regions dynamically couple with this network to initiate the engagement of cognitive control.
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Affiliation(s)
- Anne S Berry
- 1 University of Michigan.,2 Lawrence Berkeley National Laboratory
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1525
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Qiu M, Scheinost D, Ramani R, Constable RT. Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks. Neuroimage 2017; 148:130-140. [PMID: 28069540 PMCID: PMC5410383 DOI: 10.1016/j.neuroimage.2016.12.080] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/13/2016] [Accepted: 12/28/2016] [Indexed: 01/17/2023] Open
Abstract
Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing additional evidence that connectivity differences were not fully explained by CBF. In conclusion, these results provide further evidence of alterations in large-scale brain networks are associated with reduced consciousness and suggest that different modalities capture unique aspects of these large scale changes.
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Affiliation(s)
- Maolin Qiu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | | | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
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1526
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Gates KM, Lane ST, Varangis E, Giovanello K, Guskiewicz K. Unsupervised Classification During Time-Series Model Building. MULTIVARIATE BEHAVIORAL RESEARCH 2017; 52:129-148. [PMID: 27925768 PMCID: PMC8549846 DOI: 10.1080/00273171.2016.1256187] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
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Affiliation(s)
| | | | - E Varangis
- a University of North Carolina , Chapel Hill
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1527
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Abstract
PURPOSE OF REVIEW We review the literature on the use and potential use of computational psychiatry methods in Borderline Personality Disorder. RECENT FINDINGS Computational approaches have been used in psychiatry to increase our understanding of the molecular, circuit, and behavioral basis of mental illness. This is of particular interest in BPD, where the collection of ecologically valid data, especially in interpersonal settings, is becoming more common and more often subject to quantification. Methods that test learning and memory in social contexts, collect data from real-world settings, and relate behavior to molecular and circuit networks are yielding data of particular interest. SUMMARY Research in BPD should focus on collaborative efforts to design and interpret experiments with direct relevance to core BPD symptoms and potential for translation to the clinic.
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Affiliation(s)
| | - Dylan Stahl
- Yale University Department of Psychiatry
- Knox College
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1528
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Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads. Neuroimage 2017; 152:31-37. [PMID: 28254510 DOI: 10.1016/j.neuroimage.2017.02.078] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 02/21/2017] [Accepted: 02/26/2017] [Indexed: 12/25/2022] Open
Abstract
Despite emerging evidence suggesting a biological basis to our social tiles, our understanding of the neural processes which link two minds is unknown. We implemented a novel approach, which included connectome similarity analysis using resting state intrinsic networks of parent-child dyads as well as daily diaries measured across 14 days. Intrinsic resting-state networks for both parents and their adolescent child were identified using independent component analysis (ICA). Results indicate that parents and children who had more similar RSN connectome also had more similar day-to-day emotional synchrony. Furthermore, dyadic RSN connectome similarity was associated with children's emotional competence, suggesting that being neurally in-tune with their parents confers emotional benefits. We provide the first evidence that dyadic RSN similarity is associated with emotional synchrony in what is often our first and most essential social bond, the parent-child relationship.
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1529
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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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1530
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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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1531
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Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval. J Neurosci 2017; 37:2986-2998. [PMID: 28202612 DOI: 10.1523/jneurosci.2324-16.2017] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 01/11/2017] [Accepted: 02/06/2017] [Indexed: 11/21/2022] Open
Abstract
Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions.SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically, even within the same brain regions, the nature of representation during retrieval was qualitatively different from that during encoding. These results suggest that memory retrieval is not a faithful replay of past event but rather involves additional constructive processes to serve adaptive functions.
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1532
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Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, Siegle GJ. Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. Biol Psychiatry 2017; 81:347-357. [PMID: 27712830 PMCID: PMC5215983 DOI: 10.1016/j.biopsych.2016.06.023] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 06/17/2016] [Accepted: 06/17/2016] [Indexed: 01/19/2023]
Abstract
BACKGROUND There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention. METHODS Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis. RESULTS Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables. CONCLUSIONS Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap.
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Affiliation(s)
| | | | | | | | | | - Michael E. Thase
- Perelman School of Medicine of the University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center
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1533
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Functional connectomics from a "big data" perspective. Neuroimage 2017; 160:152-167. [PMID: 28232122 DOI: 10.1016/j.neuroimage.2017.02.031] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Revised: 01/21/2017] [Accepted: 02/13/2017] [Indexed: 01/10/2023] Open
Abstract
In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field.
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1534
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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: 86] [Impact Index Per Article: 12.3] [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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1535
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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: 599] [Impact Index Per Article: 85.6] [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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1536
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Biazoli CE, Salum GA, Pan PM, Zugman A, Amaro E, Rohde LA, Miguel EC, Jackowski AP, Bressan RA, Sato JR. Commentary: Functional connectome fingerprint: identifying individuals using patterns of brain connectivity. Front Hum Neurosci 2017; 11:47. [PMID: 28223928 PMCID: PMC5293761 DOI: 10.3389/fnhum.2017.00047] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 01/23/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Claudinei E Biazoli
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABCSanto Andre, Brazil; Department of Radiology, School of Medicine, University of São PauloSão Paulo, Brazil
| | - Giovanni A Salum
- Hospital de Clinicas de Porto Alegre and Department of Psychiatry, Federal University of Rio Grande do SulPorto Alegre, Brazil; National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil
| | - Pedro M Pan
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Interdisciplinary Lab for Clinical Neurosciences, Universidade Federal de São PauloSão Paulo, Brazil
| | - André Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Interdisciplinary Lab for Clinical Neurosciences, Universidade Federal de São PauloSão Paulo, Brazil
| | - Edson Amaro
- Department of Radiology, School of Medicine, University of São Paulo São Paulo, Brazil
| | - Luis A Rohde
- Hospital de Clinicas de Porto Alegre and Department of Psychiatry, Federal University of Rio Grande do SulPorto Alegre, Brazil; National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil
| | - Euripedes C Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Department of Psychiatry, School of Medicine, University of São PauloSão Paulo, Brazil
| | - Andrea P Jackowski
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Interdisciplinary Lab for Clinical Neurosciences, Universidade Federal de São PauloSão Paulo, Brazil
| | - Rodrigo A Bressan
- National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Interdisciplinary Lab for Clinical Neurosciences, Universidade Federal de São PauloSão Paulo, Brazil
| | - João R Sato
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABCSanto Andre, Brazil; Department of Radiology, School of Medicine, University of São PauloSão Paulo, Brazil; National Institute of Developmental Psychiatry for Children and Adolescents, CNPqSão Paulo, Brazil; Interdisciplinary Lab for Clinical Neurosciences, Universidade Federal de São PauloSão Paulo, Brazil
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1537
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Gordon EM, Laumann TO, Adeyemo B, Gilmore AW, Nelson SM, Dosenbach NUF, Petersen SE. Individual-specific features of brain systems identified with resting state functional correlations. Neuroimage 2017; 146:918-939. [PMID: 27640749 PMCID: PMC5321842 DOI: 10.1016/j.neuroimage.2016.08.032] [Citation(s) in RCA: 143] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 08/11/2016] [Accepted: 08/16/2016] [Indexed: 01/06/2023] Open
Abstract
Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals' cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.
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Affiliation(s)
- Evan M Gordon
- VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA; Departments of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
| | - Timothy O Laumann
- Departments of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Departments of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian W Gilmore
- Departments of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven M Nelson
- VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Nico U F Dosenbach
- Departments of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven E Petersen
- Departments of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA; Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, USA
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1538
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1539
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Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing. Sci Rep 2017; 7:41414. [PMID: 28112247 PMCID: PMC5256084 DOI: 10.1038/srep41414] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 12/19/2016] [Indexed: 12/13/2022] Open
Abstract
Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands.
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1540
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Bolt T, Nomi JS, Rubinov M, Uddin LQ. Correspondence between evoked and intrinsic functional brain network configurations. Hum Brain Mapp 2017; 38:1992-2007. [PMID: 28052450 DOI: 10.1002/hbm.23500] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 12/14/2016] [Accepted: 12/14/2016] [Indexed: 02/01/2023] Open
Abstract
Much of the literature exploring differences between intrinsic and task-evoked brain architectures has examined changes in functional connectivity patterns between specific brain regions. While informative, this approach overlooks important overall functional changes in hub organization and network topology that may provide insights about differences in integration between intrinsic and task-evoked states. Examination of changes in overall network organization, such as a change in the concentration of hub nodes or a quantitative change in network organization, is important for understanding the underlying processes that differ between intrinsic and task-evoked brain architectures. The present study used graph-theoretical techniques applied to publicly available neuroimaging data collected from a large sample of individuals (N = 202), and a within-subject design where resting-state and several task scans were collected from each participant as part of the Human Connectome Project. We demonstrate that differences between intrinsic and task-evoked brain networks are characterized by a task-general shift in high-connectivity hubs from primarily sensorimotor/auditory processing areas during the intrinsic state to executive control/salience network areas during task performance. In addition, we demonstrate that differences between intrinsic and task-evoked architectures are associated with changes in overall network organization, such as increases in network clustering, global efficiency and integration between modules. These findings offer a new perspective on the principles guiding functional brain organization by identifying unique and divergent properties of overall network organization between the resting-state and task performance. Hum Brain Mapp 38:1992-2007, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Taylor Bolt
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Mikail Rubinov
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom.,Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida
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1541
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The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread. Neuroimage 2017; 145:377-388. [DOI: 10.1016/j.neuroimage.2016.04.049] [Citation(s) in RCA: 223] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 04/16/2016] [Accepted: 04/20/2016] [Indexed: 12/27/2022] Open
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1542
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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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1543
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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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1544
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Parker Jones O, Voets NL, Adcock JE, Stacey R, Jbabdi S. Resting connectivity predicts task activation in pre-surgical populations. NEUROIMAGE-CLINICAL 2016; 13:378-385. [PMID: 28123949 PMCID: PMC5222953 DOI: 10.1016/j.nicl.2016.12.028] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 11/26/2016] [Accepted: 12/22/2016] [Indexed: 12/02/2022]
Abstract
Injury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise and reliable language maps can be inferred in patient populations from resting scans of idle brain activity. We trained a predictive model on pairs of resting-state and task-evoked data and tested it to predict activation of unseen patients and healthy controls based on their resting-state data alone. A well-validated language task (category fluency) was used in acquiring the task-evoked fMRI data. Although patients showed greater variation in their actual language maps, our models successfully learned variations in both patient and control responses from the individual resting-connectivity features. Importantly, we further demonstrate that a model trained exclusively on the more-homogenous control group can be used to predict task activations in patients. These results are the first to show that resting connectivity robustly predicts individual differences in neural response in cases of pathological variability. A method for identifying eloquent areas in the brain from resting fMRI is proposed. It uses supervised learning to predict task contrasts from resting connectivity. Good predictions were obtained in controls and in pre-surgical patient populations. Patient diagnoses included epilepsy, tumours, and vascular lesions. Language maps in patients could be predicted from models trained on controls.
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Affiliation(s)
- O Parker Jones
- FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - N L Voets
- FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK; Oxford Epilepsy Research Group, NDCN, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - J E Adcock
- Oxford Epilepsy Research Group, NDCN, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK; Department of Neurology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - R Stacey
- Department of Neurosurgery, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - S Jbabdi
- FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
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1545
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1546
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Ma Y, Hamilton C, Zhang N. Dynamic Connectivity Patterns in Conscious and Unconscious Brain. Brain Connect 2016; 7:1-12. [PMID: 27846731 DOI: 10.1089/brain.2016.0464] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Brain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. In this study, we acquired resting-state functional magnetic resonance imaging data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding window method and k-means clustering. Our results demonstrate that whole-brain networks contained several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level.
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Affiliation(s)
- Yuncong Ma
- 1 Department of Biomedical Engineering, Pennsylvania State University, University Park , Pennsylvania
| | - Christina Hamilton
- 2 The Huck Institutes of Life Sciences, Pennsylvania State University, University Park , Pennsylvania
| | - Nanyin Zhang
- 1 Department of Biomedical Engineering, Pennsylvania State University, University Park , Pennsylvania.,2 The Huck Institutes of Life Sciences, Pennsylvania State University, University Park , Pennsylvania
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1547
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Campbell KL, Schacter DL. Aging and the Resting State: Cognition is not Obsolete. LANGUAGE, COGNITION AND NEUROSCIENCE 2016; 32:692-694. [PMID: 28603744 PMCID: PMC5464414 DOI: 10.1080/23273798.2016.1265658] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 11/22/2016] [Indexed: 06/07/2023]
Affiliation(s)
- Karen L. Campbell
- Department of Psychology, Harvard University, Cambridge, MA 02138, United States
| | - Daniel L. Schacter
- Department of Psychology, Harvard University, Cambridge, MA 02138, United States
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1548
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Tsukahara JS, Harrison TL, Engle RW. The relationship between baseline pupil size and intelligence. Cogn Psychol 2016; 91:109-123. [DOI: 10.1016/j.cogpsych.2016.10.001] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 09/23/2016] [Accepted: 10/12/2016] [Indexed: 01/08/2023]
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1549
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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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1550
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Liuzzi L, Gascoyne LE, Tewarie PK, Barratt EL, Boto E, Brookes MJ. Optimising experimental design for MEG resting state functional connectivity measurement. Neuroimage 2016; 155:565-576. [PMID: 27903441 DOI: 10.1016/j.neuroimage.2016.11.064] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 11/16/2016] [Accepted: 11/25/2016] [Indexed: 12/21/2022] Open
Abstract
The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.
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Affiliation(s)
- Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Lauren E Gascoyne
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Prejaas K Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Eleanor L Barratt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK.
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