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Ma Y, Li H, Zhou Z, Chen X, Ma L, Guray E, Balderston NL, Oathes DJ, Shinohara RT, Wolf DH, Nasrallah IM, Shou H, Satterthwaite TD, Davatzikos C, Fan Y. p Net: A toolbox for personalized functional networks modeling. bioRxiv 2024:2024.04.26.591367. [PMID: 38746228 PMCID: PMC11092457 DOI: 10.1101/2024.04.26.591367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet .
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Li T, Feng C, Wang J. Reconfiguration of the costly punishment network architecture in punishment decision-making. Psychophysiology 2024; 61:e14458. [PMID: 37941501 DOI: 10.1111/psyp.14458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/15/2023] [Accepted: 10/02/2023] [Indexed: 11/10/2023]
Abstract
Human costly punishment is rooted in multiple regions across large-scale functional systems, a collection of which constitutes the costly punishment network (CPN). Our previous study found that the CPN is intrinsically organized in an optimized and reliable manner to support individual costly punishment propensity. However, it remains unknown how the CPN is reconfigured in response to external cognitive demands in punishment decision-making. Here, we combined resting-state and task-functional magnetic resonance imaging to examine the task-related reconfigurations of intrinsic organizations of the CPN when participants made decisions of costly punishment in the Ultimatum Game. Although a strong consistency was observed in the overall pattern and each nodal profile between the intrinsic (task-free) and extrinsic (task-evoked) functional connectivity of the CPN, condition-general and condition-specific reconfigurations were also evident. Specifically, both unfair and fair conditions induced increases in functional connectivity between a few specific pairs of regions, and the unfair condition additionally induced increases in network efficiency of the CPN. Intriguingly, the specific changes in global efficiency of the CPN in the unfair condition were associated with individual differences in costly punishment after adjusting for the corresponding results in the fair condition, which were further identified for females but not for males. These findings were largely reproducible on independent samples. Collectively, our findings provide novel insights into how the CPN adaptively reconfigures its network architecture to support costly punishment.
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Affiliation(s)
- Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Chengdu, China
| | - Chunliang Feng
- School of Psychology, South China Normal University, Guangzhou, China
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Jinhui Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
- Institute of Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
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Hakonen M, Dahmani L, Lankinen K, Ren J, Barbaro J, Blazejewska A, Cui W, Kotlarz P, Li M, Polimeni JR, Turpin T, Uluç I, Wang D, Liu H, Ahveninen J. Individual connectivity-based parcellations reflect functional properties of human auditory cortex. bioRxiv 2024:2024.01.20.576475. [PMID: 38293021 PMCID: PMC10827228 DOI: 10.1101/2024.01.20.576475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Furthermore, connectional homogeneity within networks was highest for the individual-specific parcellations. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.
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Affiliation(s)
- M Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - L Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - K Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - J Ren
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J Barbaro
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - A Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - W Cui
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - P Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - M Li
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - T Turpin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - I Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - D Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - H Liu
- Division of Brain Sciences, Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - J Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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Lee TW, Tramontano G, Hinrichs C. Concordant dynamic changes of global network properties in the frontoparietal and limbic compartments: An EEG study. Biosystems 2024; 235:105101. [PMID: 38101726 DOI: 10.1016/j.biosystems.2023.105101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
INTRODUCTION Despite its complexity, deciphering nodal interaction is imperative to understanding a neural network. Network interaction is an even more complicated topic that must be addressed. This study aimed to examine the relationship between the brain waves of two canonical brain structures, i.e., the frontoparietal and limbic compartments, during a resting state. METHODS Electroencephalography (EEG) of 51 subjects in eye-closed condition was analyzed, and the eLORETA method was applied to convert the signals from the scalp to the brain. By way of community detection, representative neural nodes and the associated mean activities were retrieved. Total and lagged coherences were computed to indicate functional connectivity between those neural nodes. Two global network properties were elucidated based on the connectivity measures, i.e., global efficiency and mean functional connectivity strength. The temporal correlation of the global network indices between the two studied networks was explored. RESULTS It was found that there was a significant trend of positive correlation across the four metrics (lagged vs. total coherence x global efficiency vs. average connectivity). In other words, when the neural interaction in the FP network was stronger, so did that in the limbic network, and vice versa. Notably, the above interaction was not spectrally specific and only existed at a finer temporal scale (under hundreds of milliseconds level). CONCLUSION The concordant change in network properties indicates an intricate balance between FP and LM compartments. Possible mechanisms and implications for the findings are discussed.
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Affiliation(s)
- Tien-Wen Lee
- The NeuroCognitive Institute (NCI) Clinical Research Foundation, NJ, 07856, USA. http://neuroci.com
| | - Gerald Tramontano
- The NeuroCognitive Institute (NCI) Clinical Research Foundation, NJ, 07856, USA.
| | - Clay Hinrichs
- Hackettstown Medical Center, Atlantic Health System, NJ, 07840, USA.
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Pak V, Hashmi JA. Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks. Netw Neurosci 2023; 7:1248-1265. [PMID: 38144683 PMCID: PMC10631789 DOI: 10.1162/netn_a_00328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 06/23/2023] [Indexed: 12/26/2023] Open
Abstract
Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.
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Affiliation(s)
- Veronika Pak
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | - Javeria Ali Hashmi
- Department of Anesthesia, Pain Management, and Perioperative Medicine, Nova Scotia Health Authority, Halifax, NS, Canada
- Dalhousie University, Halifax, NS, Canada
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Molloy MF, Osher DE. A personalized cortical atlas for functional regions of interest. J Neurophysiol 2023; 130:1067-1080. [PMID: 37727907 PMCID: PMC10994647 DOI: 10.1152/jn.00108.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 09/21/2023] Open
Abstract
Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.
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Affiliation(s)
- M. Fiona Molloy
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States
| | - David E. Osher
- Department of Psychology, The Ohio State University, Columbus, Ohio, United States
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Koslov SR, Kable JW, Foster BL. Dissociable contributions of the medial parietal cortex to recognition memory. bioRxiv 2023:2023.09.12.557048. [PMID: 37745317 PMCID: PMC10515876 DOI: 10.1101/2023.09.12.557048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its ubiquitous, and yet distinct, functional characteristics during different types of retrieval tasks. Specifically, while recognition memory and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct patterns of response within MPC. This dissociation adds to growing evidence suggesting a common principle of functional organization across memory related brain structures, specifically regarding the control or content demands of memory-based decisions. To carefully examine this putative organization, we used a high-resolution fMRI dataset collected at ultra-high field (7T) while subjects performed thousands of recognition-memory trials to identify MPC regions responsive to recognition-decisions or semantic content of stimuli within and across individuals. We observed interleaving, though distinct, functional subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli, but rarely both. In addition, this functional dissociation within MPC was further accentuated by distinct profiles of connectivity bias with the hippocampus during task and rest. Finally, we show that recent observations of person and place selectivity within MPC reflect category specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, these data better account for how distinct patterns of MPC responses can occur as a result of task demands during episodic retrieval and may reflect a common principle of organization throughout hippocampal-neocortical memory systems.
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Affiliation(s)
- Seth R. Koslov
- Department of Neurosurgery, Perelman School of Medicine; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Joseph W. Kable
- Department of Psychology; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Brett L. Foster
- Department of Neurosurgery, Perelman School of Medicine; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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Belov V, Kozyrev V, Singh A, Sacchet MD, Goya-Maldonado R. Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS. Sci Rep 2023; 13:12615. [PMID: 37537227 PMCID: PMC10400653 DOI: 10.1038/s41598-023-38946-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 07/18/2023] [Indexed: 08/05/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.
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Affiliation(s)
- Vladimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Von-Siebold Str. 5, 37075, Göttingen, Germany
| | - Vladislav Kozyrev
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Von-Siebold Str. 5, 37075, Göttingen, Germany
- Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Aditya Singh
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Von-Siebold Str. 5, 37075, Göttingen, Germany
| | - Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Von-Siebold Str. 5, 37075, Göttingen, Germany.
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Bukhari H, Su C, Dhamala E, Gu Z, Jamison K, Kuceyeski A. Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score. Hum Brain Mapp 2023; 44:3541-3554. [PMID: 37042411 PMCID: PMC10203814 DOI: 10.1002/hbm.26296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/10/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023] Open
Abstract
Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
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Affiliation(s)
- Hussain Bukhari
- Department of NeuroscienceWeill Cornell MedicineNew YorkNew YorkUSA
| | - Chang Su
- Department of BiostatisticsYale UniversityNew HavenConnecticutUSA
| | - Elvisha Dhamala
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Zijin Gu
- Department of Electrical and Computer EngineeringCornell UniversityIthacaNew YorkUSA
| | - Keith Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
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Hua L, Gao F, Xia X, Guo Q, Zhao Y, Huang S, Yuan Z. Individual-specific functional connectivity improves prediction of Alzheimer's disease's symptoms in elderly people regardless of APOE ε4 genotype. Commun Biol 2023; 6:581. [PMID: 37258640 DOI: 10.1038/s42003-023-04952-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023] Open
Abstract
To date, reliable biomarkers remain unclear that could link functional connectivity to patients' symptoms for detecting and predicting the process from normal aging to Alzheimer's disease (AD) in elderly people with specific genotypes. To address this, individual-specific functional connectivity is constructed for elderly participants with/without APOE ε4 allele. Then, we utilize recursive feature selection-based machine learning to reveal individual brain-behavior relationships and to predict the symptom transition in different genotypes. Our findings reveal that compared with conventional atlas-based functional connectivity, individual-specific functional connectivity exhibits higher classification and prediction performance from normal aging to AD in both APOE ε4 groups, while no significant performance is detected when the data of two genotyping groups are combined. Furthermore, individual-specific between-network connectivity constitutes a major contributor to assessing cognitive symptoms. This study highlights the essential role of individual variation in cortical functional anatomy and the integration of brain and behavior in predicting individualized symptoms.
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Affiliation(s)
- Lin Hua
- Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
- Centre for Cognitive and Brain Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
| | - Fei Gao
- Institute of Modern Languages and Linguistics, Fudan University, Shanghai, 200433, China
| | - Xiaoluan Xia
- Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
- Centre for Cognitive and Brain Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
| | - Qiwei Guo
- Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
- Centre for Cognitive and Brain Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
| | - Yonghua Zhao
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China
| | - Shaohui Huang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China.
- Centre for Cognitive and Brain Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, SAR 999078, China.
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Yang H, Wu G, Li Y, Ma Y, Chen R, Pines A, Xu T, Sydnor VJ, Satterthwaite TD, Cui Z. Connectional Hierarchy in Human Brain Revealed by Individual Variability of Functional Network Edges. bioRxiv 2023:2023.03.08.531800. [PMID: 36945479 PMCID: PMC10028904 DOI: 10.1101/2023.03.08.531800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The human cerebral cortex is connected by intricate inter-areal wiring at the macroscale. The cortical hierarchy from primary sensorimotor to higher-order association areas is a unifying organizational principle across various neurobiological properties; however, previous studies have not clarified whether the connections between cortical regions exhibit a similar hierarchical pattern. Here, we identify a connectional hierarchy indexed by inter-individual variability of functional connectivity edges, which continuously progresses along a hierarchical gradient from within-network connections to between-network edges connecting sensorimotor and association networks. We found that this connectional hierarchy of variability aligns with both hemodynamic and electromagnetic connectivity strength and is constrained by structural connectivity strength. Moreover, the patterning of connectional hierarchy is related to inter-regional similarity in transcriptional and neurotransmitter receptor profiles. Using the Neurosynth cognitive atlas and cortical vulnerability maps in 13 brain disorders, we found that the connectional hierarchy of variability is associated with similarity networks of cognitive relevance and that of disorder vulnerability. Finally, we found that the prominence of this hierarchical gradient of connectivity variability declines during youth. Together, our results reveal a novel hierarchal organizational principle at the connectional level that links multimodal and multiscale human connectomes to individual variability in functional connectivity.
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Affiliation(s)
- Hang Yang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Guowei Wu
- Chinese Institute for Brain Research, Beijing, 102206, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yaoxin Li
- Chinese Institute for Brain Research, Beijing, 102206, China
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yiyao Ma
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Adam Pines
- Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China
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12
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Bai L, Yin B, Lei S, Li T, Wang S, Pan Y, Gan S, Jia X, Li X, Xiong F, Yan Z, Bai G. Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury. J Neurotrauma 2023; 40:63-73. [PMID: 35747994 DOI: 10.1089/neu.2021.0450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.
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Affiliation(s)
- Lijun Bai
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Bo Yin
- Department of Neurosurgery, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuoyan Lei
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China
| | - Tianhui Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Yizhen Pan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shuoqiu Gan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Feng Xiong
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guanghui Bai
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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13
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Betzel RF, Cutts SA, Greenwell S, Faskowitz J, Sporns O. Individualized event structure drives individual differences in whole-brain functional connectivity. Neuroimage 2022. [DOI: 10.1016/j.neuroimage.2022.118993] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/25/2021] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
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14
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Wu Z, Cao M, Di X, Wu K, Gao Y, Li X. Regional Topological Aberrances of White Matter- and Gray Matter-Based Functional Networks for Attention Processing May Foster Traumatic Brain Injury-Related Attention Deficits in Adults. Brain Sci 2021; 12:brainsci12010016. [PMID: 35053760 PMCID: PMC8774280 DOI: 10.3390/brainsci12010016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 12/31/2022] Open
Abstract
Traumatic brain injury (TBI) is highly prevalent in adults. TBI-related functional brain alterations have been linked with common post-TBI neurobehavioral sequelae, with unknown neural substrates. This study examined the systems-level functional brain alterations in white matter (WM) and gray matter (GM) for visual sustained-attention processing, and their interactions and contributions to post-TBI attention deficits. Task-based functional MRI data were collected from 42 adults with TBI and 43 group-matched normal controls (NCs), and analyzed using the graph theoretic technique. Global and nodal topological properties were calculated and compared between the two groups. Correlation analyses were conducted between the neuroimaging measures that showed significant between-group differences and the behavioral symptom measures in attention domain in the groups of TBI and NCs, respectively. Significantly altered nodal efficiencies and/or degrees in several WM and GM nodes were reported in the TBI group, including the posterior corona radiata (PCR), posterior thalamic radiation (PTR), postcentral gyrus (PoG), and superior temporal sulcus (STS). Subjects with TBI also demonstrated abnormal systems-level functional synchronization between the PTR and STS in the right hemisphere, hypo-interaction between the PCR and PoG in the left hemisphere, as well as the involvement of systems-level functional aberrances in the PCR in TBI-related behavioral impairments in the attention domain. The findings of the current study suggest that TBI-related systems-level functional alterations associated with these two major-association WM tracts, and their anatomically connected GM regions may play critical role in TBI-related behavioral deficits in attention domains.
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Affiliation(s)
- Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
| | - Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 510630, China;
| | - Yu Gao
- Department of Psychology, Brooklyn College, The City University of New York, New York, NY 11210, USA;
- The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Xiaobo Li
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
- Correspondence: or ; Tel.: +1-973-596-5880
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15
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Lee TW, Tramontano G. Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements. AIMS Neurosci 2021; 8:526-542. [PMID: 34877403 PMCID: PMC8611189 DOI: 10.3934/neuroscience.2021028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/01/2021] [Indexed: 11/24/2022] Open
Abstract
To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.
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Affiliation(s)
- Tien-Wen Lee
- The Neuro Cognitive Institute (NCI) Clinical Research Foundation, NJ 07856, US.,Department of Psychiatry, Dajia Lee's General Hospital, Lee's Medical Corporation, Taichung 43748, Taiwan
| | - Gerald Tramontano
- The Neuro Cognitive Institute (NCI) Clinical Research Foundation, NJ 07856, US
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16
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Lewis JD, Bezgin G, Fonov VS, Collins DL, Evans AC. A sub+cortical fMRI-based surface parcellation. Hum Brain Mapp 2021; 43:616-632. [PMID: 34761459 PMCID: PMC8720195 DOI: 10.1002/hbm.25675] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.
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Affiliation(s)
- John D Lewis
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Gleb Bezgin
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Verdun, Quebec, Canada
| | - Vladimir S Fonov
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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17
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Brooks SJ, Parks SM, Stamoulis C. Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:3133-3136. [PMID: 34891905 DOI: 10.1109/embc46164.2021.9630267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ongoing large-scale human brain studies are generating complex neuroimaging data from thousands of individuals that can be leveraged to derive data-driven, anatomically accurate brain parcellations. However, despite their promise and many strengths, these data are highly heterogeneous, a characteristic that may affect the anatomical accuracy and generalization of the template but has received relatively little attention. Using multiple similarity measures and thresholding approaches, this study investigated the topological intra- and inter-individual variability of restingstate (rs) functional edge maps (often used for brain parcellation), estimated from rs-fMRI connectivity in n = 5878 children from the Adolescent Brain Cognitive Development (ABCD) study. Findings from this initial investigation indicate that choosing a subject- vs cohort-based threshold for estimating edge maps from connectivity matrices does not significantly impact the map topology. In contrast, the choice of similarity measure and non-linear relationship between similarity and edge map sparsity may have a significant impact on map classification and the generation of parcellation atlases. Multi-level classification revealed multiple clusters with a potentially complex mapping onto biological variables beyond simple demographics.Clinical Relevance- Case-control neuroimaging studies should use domain-specific (e.g., demographics-specific) atlases for parcellating the brain, to improve accuracy and rigor of cohort comparisons. To be generalizable, such atlases need to be derived from large datasets, which are inherently heterogeneous. In a cohort of 5878 children (age ~9-10 years), this study systematically assessed the impact of heterogeneity and similarity of edge maps, which are derived from rs-fMRI connectivity and typically used to generate parcellation atlases.
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18
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Kim M, Yan C, Yang D, Liang P, Kaufer DI, Wu G. Constructing Connectome Atlas by Graph Laplacian Learning. Neuroinformatics 2021; 19:233-249. [PMID: 32712763 PMCID: PMC7855351 DOI: 10.1007/s12021-020-09482-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.
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Affiliation(s)
- Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - Chenggang Yan
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
| | - Defu Yang
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Peipeng Liang
- Department of Psychology, Capital Normal University, Beijing, 100073, China
| | - Daniel I Kaufer
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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19
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Lawrence RM, Bridgeford EW, Myers PE, Arvapalli GC, Ramachandran SC, Pisner DA, Frank PF, Lemmer AD, Nikolaidis A, Vogelstein JT. Standardizing human brain parcellations. Sci Data 2021; 8:78. [PMID: 33686079 PMCID: PMC7940391 DOI: 10.1038/s41597-021-00849-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 01/29/2021] [Indexed: 11/09/2022] Open
Abstract
Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .
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20
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Abstract
Coordinating among the demands of the external environment and internal plans requires cognitive control supported by a fronto-parietal control network (FPCN). Evidence suggests that multiple control systems span the FPCN whose operations are poorly understood. Previously (Nee and D'Esposito, 2016; 2017), we detailed frontal dynamics that support control processing, but left open their role in broader cortical function. Here, I show that the FPCN consists of an external/present-oriented to internal/future-oriented cortical gradient extending outwardly from sensory-motor cortices. Areas at the ends of this gradient act in a segregative manner, exciting areas at the same level, but suppressing areas at different levels. By contrast, areas in the middle of the gradient excite areas at all levels, promoting integration of control processing. Individual differences in integrative dynamics predict higher level cognitive ability and amenability to neuromodulation. These data suggest that an intermediary zone within the FPCN underlies integrative processing that supports cognitive control.
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Affiliation(s)
- Derek Evan Nee
- Department of Psychology, Florida State UniversityTallahasseeUnited States
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21
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Nozais V, Boutinaud P, Verrecchia V, Gueye MF, Hervé PY, Tzourio C, Mazoyer B, Joliot M. Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis. Neuroinformatics 2021. [PMID: 33543442 DOI: 10.1007/s12021-021-09514-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2021] [Indexed: 12/12/2022]
Abstract
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.
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22
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Wang H, Sun J, Cui D, Wang X, Jin J, Li Y, Liu Z, Yin T. Quantitative assessment of inter-individual variability in fMRI-based human brain atlas. Quant Imaging Med Surg 2021; 11:810-822. [PMID: 33532279 DOI: 10.21037/qims-20-404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Inter-individual variability is an inherent and ineradicable feature of group-level brain atlases that undermines their reliability for clinical and other applications. To date, there have been no reports quantifying inter-individual variability in brain atlases. Methods In the present study, we compared inter-individual variability in nine brain atlases by task-based functional magnetic resonance imaging (MRI) mapping of motor and temporal lobe language regions in both cerebral hemispheres. We analyzed complete motor and language task-based fMRI and T1 data for 893 young, healthy subjects in the Human Connectome Project database. Euclidean distances (EDs) between hotspots in specific brain regions were calculated from task-based fMRI and brain atlas data. General linear model parameters were used to investigate the influence of different brain atlases on signal extraction. Finally, the inter-individual variability of ED and extracted signals and interdependence of relevant indicators were statistically evaluated. Results We found that inter-individual variability of ED varied across the nine brain atlases (P<0.0001 for motor regions and P<0.0001 for language regions). There was no correlation between parcel number and inter-individual variability in left to right (LtoR; P=0.7959 for motor regions and P=0.2002 for language regions) and right to left (RtoL; P=0.7654 for motor regions and P=0.3544 for language regions) ED; however, LtoR (P≤0.0001) and RtoL (P≤0.0001) inter-individual variability differed according to brain region: the LtoR (P=0.0008) and RtoL (P=0.0004) inter-individual variability was greater for the right hand than for the left hand, the LtoR (P=0.0019) and RtoL (P=0.0179) inter-individual variability was greater for the right language than for the left language, but there was no such difference between the right foot and left foot (LtoR, P=0.2469 and RtoL, P=0.6140). Inter-individual variability in one motor region was positively correlated with mean values in the other three motor regions (left hand, P=0.0145; left foot, P=0.0103; right hand, P=0.1318; right foot, P=0.3785). Inter-individual variability in language region was positively correlated with mean values in the four motor regions (left language, P=0.0422; right language, P=0.0514). Signal extraction for LtoR (P<0.0001) and RtoL (P<0.0001) varied across the nine brain atlases, which also showed differences in inter-individual variability. Conclusions These results underscore the importance of quantitatively assessing the inter-individual variability of a brain atlas prior to use, and demonstrate that mapping motor regions by task-based fMRI is an effective method for quantitatively assessing the inter-individual variability in a brain atlas.
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Affiliation(s)
- He Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Jinping Sun
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China
| | - Dong Cui
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Xin Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Jingna Jin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Ying Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Zhipeng Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China
| | - Tao Yin
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin, China.,Neuroscience Center, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
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23
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Ogawa A, Osada T, Tanaka M, Kamagata K, Aoki S, Konishi S. Connectivity-based localization of human hypothalamic nuclei in functional images of standard voxel size. Neuroimage 2020; 221:117205. [PMID: 32735999 DOI: 10.1016/j.neuroimage.2020.117205] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 07/17/2020] [Accepted: 07/23/2020] [Indexed: 12/19/2022] Open
Abstract
Despite their critical roles in autonomic functions, individual hypothalamic nuclei have not been extensively investigated in humans using functional magnetic resonance imaging, partly due to the difficulty in resolving individual nuclei contained in the small structure of the hypothalamus. Areal parcellation analyses enable discrimination of individual hypothalamic nuclei but require a higher spatial resolution, which necessitates long scanning time or large amounts of data to compensate for the low signal-to-noise ratio in 3T or 1.5T scanners. In this study, we present analytic procedures to estimate likely locations of individual nuclei in the standard 2-mm resolution based on our higher resolution dataset. The spatial profiles of functional connectivity with the cerebral cortex for each nucleus in the medial hypothalamus were calculated using our higher resolution dataset. Voxels in the hypothalamus in standard resolution images from the Human Connectome Project (HCP) database that predominantly shared connectivity profiles with the same nucleus were subsequently identified. Voxels representing individual nuclei, as identified with the analytic procedures, were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate. These results suggest that these analytic procedures are capable of refining voxels that represent individual hypothalamic nuclei in standard resolution. Our results highlight the potential utility of these procedures in various settings such as patient studies, where lengthy scans are infeasible.
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Affiliation(s)
- Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan; Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan; Sportology Center, Juntendo University School of Medicine, Tokyo, Japan; Advanced Research Institute for Health Science, Juntendo University School of Medicine, Tokyo, Japan.
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24
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Wang D, Li M, Wang M, Schoeppe F, Ren J, Chen H, Öngür D, Brady RO Jr, Baker JT, Liu H. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry 2020; 25:2119-29. [PMID: 30443042 DOI: 10.1038/s41380-018-0276-1] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/09/2018] [Accepted: 08/13/2018] [Indexed: 12/23/2022]
Abstract
Neuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks. However, these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient's current symptoms, predict future symptoms, or predict a treatment response. Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships. Here, we employed a novel approach to defining individual-specific functional connectivity in 158 patients diagnosed with schizophrenia (n = 49), schizoaffective disorder (n = 37), or bipolar disorder with psychosis (n = 72), and identified neuroimaging features that track psychotic symptoms in a dimension- or disorder-specific fashion. Using individually specified functional connectivity, we were able to estimate positive, negative, and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores. Comparing optimized estimation models among schizophrenia spectrum patients, positive and negative symptoms were associated with largely non-overlapping sets of cortical connections. Comparing between schizophrenia spectrum and bipolar disorder patients, the models for positive symptoms were largely non-overlapping between the two disorder classes. Finally, models derived using conventional region definition strategies performed at chance levels for most symptom domains. Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms, as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest.
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25
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Suda A, Osada T, Ogawa A, Tanaka M, Kamagata K, Aoki S, Hattori N, Konishi S. Functional Organization for Response Inhibition in the Right Inferior Frontal Cortex of Individual Human Brains. Cereb Cortex 2020; 30:6325-6335. [PMID: 32666077 PMCID: PMC7609925 DOI: 10.1093/cercor/bhaa188] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 06/13/2020] [Accepted: 06/16/2020] [Indexed: 01/10/2023] Open
Abstract
The right inferior frontal cortex (IFC) is critical to response inhibition. The right IFC referred in the human studies of response inhibition is located in the posterior part of the inferior frontal gyrus and the surrounding regions and consists of multiple areas that implement distinct functions. Recent studies using resting-state functional connectivity have parcellated the cerebral cortex and revealed across-subject variability of parcel-based cerebrocortical networks. However, how the right IFC of individual brains is functionally organized and what functional properties the IFC parcels possess regarding response inhibition remain elusive. In the present functional magnetic resonance imaging study, precision functional mapping of individual human brains was adopted to the parcels in the right IFC to evaluate their functional properties related to response inhibition. The right IFC consisted of six modules or subsets of subregions, and the spatial organization of the modules varied considerably across subjects. Each module revealed unique characteristics of brain activity and its correlation to behavior related to response inhibition. These results provide updated functional features of the IFC and demonstrate the importance of individual-focused approaches in studying response inhibition in the right IFC.
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Affiliation(s)
- Akimitsu Suda
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan.,Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan.,Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo 113-8421, Japan.,Sportology Center, Juntendo University School of Medicine, Tokyo 113-8421, Japan.,Advanced Research Institute for Health Science, Juntendo University School of Medicine, Tokyo 113-8421, Japan
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26
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Nikolaidis A, Solon Heinsfeld A, Xu T, Bellec P, Vogelstein J, Milham M. Bagging improves reproducibility of functional parcellation of the human brain. Neuroimage 2020; 214:116678. [PMID: 32119986 PMCID: PMC7302537 DOI: 10.1016/j.neuroimage.2020.116678] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/22/2020] [Accepted: 02/23/2020] [Indexed: 12/21/2022] Open
Abstract
Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as 6 min of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.
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Affiliation(s)
- Aki Nikolaidis
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA.
| | | | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA
| | - Pierre Bellec
- University of Montreal, PO Box 6128 Downtown STN Montreal QC, H3C 3J7, Canada
| | - Joshua Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400 N. Charles St Baltimore, MD, 21218, USA
| | - Michael Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA
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27
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Fujimoto U, Ogawa A, Osada T, Tanaka M, Suda A, Hattori N, Kamagata K, Aoki S, Konishi S. Network Centrality Reveals Dissociable Brain Activity during Response Inhibition in Human Right Ventral Part of Inferior Frontal Cortex. Neuroscience 2020; 433:163-173. [DOI: 10.1016/j.neuroscience.2020.03.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 01/17/2023]
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28
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Abstract
The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.
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Affiliation(s)
- Jiajia Zhao
- School of Psychology, Center for Studies of Psychological Application, Institute of Cognitive Neuroscience, South China Normal University, Guangzhou, 510631, China
| | - Chao Tang
- School of Psychology, Center for Studies of Psychological Application, Institute of Cognitive Neuroscience, South China Normal University, Guangzhou, 510631, China
| | - Jingxin Nie
- School of Psychology, Center for Studies of Psychological Application, Institute of Cognitive Neuroscience, South China Normal University, Guangzhou, 510631, China.
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29
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Hacker CD, Roland JL, Kim AH, Shimony JS, Leuthardt EC. Resting-state network mapping in neurosurgical practice: a review. Neurosurg Focus 2019; 47:E15. [PMID: 31786561 PMCID: PMC9841914 DOI: 10.3171/2019.9.focus19656] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 09/12/2019] [Indexed: 01/18/2023]
Abstract
Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.
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Affiliation(s)
- Carl D. Hacker
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Jarod L. Roland
- Department of Neurosurgery, University of California, San Francisco, California
| | - Albert H. Kim
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric C. Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
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30
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Zhao X, Ji J, Zhang A. Artificial bee colony clustering with self-adaptive crossover and stepwise search for brain functional parcellation in fMRI data. Soft comput 2019; 23:8689-8709. [DOI: 10.1007/s00500-018-3467-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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31
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Abstract
Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.,Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.,The Child Study Center, Yale University School of Medicine, New Haven, CT, USA.,Department of Statistics and Data Science, Yale University, USA
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32
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Mirchi N, Betzel RF, Bernhardt BC, Dagher A, Mišic B. Tracking mood fluctuations with functional network patterns. Soc Cogn Affect Neurosci 2019; 14:47-57. [PMID: 30481361 PMCID: PMC6318473 DOI: 10.1093/scan/nsy107] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 11/21/2018] [Indexed: 12/12/2022] Open
Abstract
Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.
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Affiliation(s)
- Nykan Mirchi
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Richard F Betzel
- Department of Psychological, and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Mišic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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33
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Kong R, Li J, Orban C, Sabuncu MR, Liu H, Schaefer A, Sun N, Zuo XN, Holmes AJ, Eickhoff SB, Yeo BTT. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb Cortex 2019; 29:2533-2551. [PMID: 29878084 PMCID: PMC6519695 DOI: 10.1093/cercor/bhy123] [Citation(s) in RCA: 293] [Impact Index Per Article: 58.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Indexed: 01/28/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.
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Affiliation(s)
- Ru Kong
- 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
| | - Jingwei Li
- 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
| | - Csaba Orban
- 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
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Hesheng Liu
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alexander Schaefer
- 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
| | - Nanbo Sun
- 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
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Sciences and Research Center for Lifespan Development of Brain and Mind (CLIMB), Institute of Psychology, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - 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, Charlestown, MA, USA
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
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34
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Li M, Wang D, Ren J, Langs G, Stoecklein S, Brennan BP, Lu J, Chen H, Liu H. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol 2019; 17:e2007032. [PMID: 30908490 PMCID: PMC6448916 DOI: 10.1371/journal.pbio.2007032] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 04/04/2019] [Accepted: 03/05/2019] [Indexed: 12/13/2022] Open
Abstract
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.
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Affiliation(s)
- Meiling Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Sophia Stoecklein
- Institute of Clinical Radiology, Ludwig-Maximilians University of Munich, Munich Germany
| | - Brian P. Brennan
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts, United States of America
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Beijing, China
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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35
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Osada T, Ohta S, Ogawa A, Tanaka M, Suda A, Kamagata K, Hori M, Aoki S, Shimo Y, Hattori N, Shimizu T, Enomoto H, Hanajima R, Ugawa Y, Konishi S. An Essential Role of the Intraparietal Sulcus in Response Inhibition Predicted by Parcellation-Based Network. J Neurosci 2019; 39:2509-21. [PMID: 30692225 DOI: 10.1523/JNEUROSCI.2244-18.2019] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 12/28/2018] [Accepted: 01/04/2019] [Indexed: 01/04/2023] Open
Abstract
The posterior parietal cortex (PPC) features close anatomical and functional relationships with the prefrontal cortex. However, the necessity of the PPC in executive functions has been questioned. The present study used the stop-signal task to examine response inhibition, an executive function that inhibits prepotent response tendency. The brain activity and resting-state functional connectivity were measured to analyze a parcellation-based network that was aimed at identifying a candidate PPC region essential for response inhibition in humans. The intraparietal sulcus (IPS) was activated during response inhibition and connected with the inferior frontal cortex and the presupplementary motor area, the two frontal regions known to be necessary for response inhibition. Next, transcranial magnetic stimulation (TMS) was used to test the essential role of the IPS region for response inhibition. TMS over the IPS region prolonged the stop-signal reaction time (SSRT), the standard behavioral index used to evaluate stopping performance, when stimulation was applied 30-0 ms before stopping. On the contrary, stimulation over the temporoparietal junction region, an area activated during response inhibition but lacking connectivity with the two frontal regions, did not show changes in SSRT. These results indicate that the IPS identified using the parcellation-based network plays an essential role in executive functions.SIGNIFICANCE STATEMENT Based on the previous neuropsychological studies reporting no impairment in executive functions after lesions in the posterior parietal cortex (PPC), the necessity of PPC in executive functions has been questioned. Here, contrary to the long-lasting view, by using recently developed analysis in functional MRI ("parcellation-based network analysis"), we identified the intraparietal sulcus (IPS) region in the PPC as essential for response inhibition: one executive function to stop actions that are inaccurate in a given context. The necessity of IPS for response inhibition was further tested by an interventional technique of transcranial magnetic stimulation. Stimulation to the IPS disrupted the performance of stopping. Our findings suggest that the IPS plays essential roles in executive functions.
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36
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Han L, Savalia NK, Chan MY, Agres PF, Nair AS, Wig GS. Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan. Cereb Cortex 2018; 28:4403-4423. [PMID: 30307480 PMCID: PMC6215466 DOI: 10.1093/cercor/bhy218] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/03/2018] [Indexed: 12/26/2022] Open
Abstract
Adult aging is associated with differences in structure, function, and connectivity of brain areas. Age-based brain comparisons have typically rested on the assumption that brain areas exhibit a similar spatial organization across age; we evaluate this hypothesis directly. Area parcellation methods that identify locations where resting-state functional correlations (RSFC) exhibit abrupt transitions (boundary-mapping) are used to define cortical areas in cohorts of individuals sampled across a large range of the human adult lifespan (20-93 years). Most of the strongest areal boundaries are spatially consistent across age. Differences in parcellation boundaries are largely explained by differences in cortical thickness and anatomical alignment in older relative to younger adults. Despite the parcellation similarities, age-specific parcellations exhibit better internal validity relative to a young-adult parcellation applied to older adults' data, and age-specific parcels are better able to capture variability in task-evoked functional activity. Incorporating age-specific parcels as nodes in RSFC network analysis reveals that the spatial topography of the brain's large-scale system organization is comparable throughout aging, but confirms that the segregation of systems declines with increasing age. These observations demonstrate that many features of areal organization are consistent across adulthood, and reveal sources of age-related brain variation that contribute to the differences.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Neil K Savalia
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Anupama S Nair
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
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37
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Abstract
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
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Affiliation(s)
- Chendi Wang
- University of British Columbia, Electrical and Computer Engineering , ICICS x421-2366 Main Mall , Vancouver, British Columbia, Canada , V6T 1Z4 ;
| | - Bernard Ng
- University of British Columbia, Department of Statistics , Vancouver, British Columbia, Canada ;
| | - Rafeef Garbi
- University of British Columbia, Electrical and Computer Engineering, Vancouver, British Columbia, Canada ;
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38
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Luo Y, Schulz KP, Alvarez TL, Halperin JM, Li X. Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD. Cortex 2018; 109:234-244. [PMID: 30391878 DOI: 10.1016/j.cortex.2018.09.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 08/27/2018] [Accepted: 09/25/2018] [Indexed: 12/13/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder that persists into adulthood in a sizeable portion of afflicted children. The persistence of ADHD elevates the risk for adverse outcomes that result in substantial individual and societal burden. The objective of this study is to assess neurobiological substrates associated with variability of clinical outcomes in childhood ADHD, which has considerable value for the development of novel interventions that target mechanisms associated with recovery. A total of 36 young adults who were diagnosed with ADHD combined-type during childhood and 33 group-matched controls were involved in the study. Adults with childhood ADHD were further divided into 17 persisters and 19 remitters based on DSM-5 criteria. Functional magnetic resonance imaging data during a cue-evoked attention task were collected from each subject. The cue-evoked attention processing network was constructed using graph theoretic techniques. Network properties, including global-, local-, and nodal-efficiency, and network hubs were computed. Group comparisons of the network properties were conducted. Significantly lower nodal efficiency in right inferior frontal gyrus and reduced left side frontal-parietal functional interactions were observed in both remitters and persisters relative to the controls. The ADHD persisters showed a unique pattern of significantly lower nodal efficiency in right middle frontal gyrus (MFG) and hyper-interactions between bilateral MFG. This study suggests that right MFG functional impairments may relate to inactive fronto-parietal functional interactions for sensory and cognitive information processing and symptom persistence in young adults with childhood ADHD.
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Affiliation(s)
- Yuyang Luo
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA
| | - Kurt P Schulz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Tara L Alvarez
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA
| | - Jeffrey M Halperin
- Department of Psychology, Queens College, City University of New York, NY, USA
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, NJ, USA; Department of Electric/Computer Engineering, New Jersey Institute of Technology, NJ, USA.
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39
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Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, Eickhoff SB, Yeo BTT. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex 2018; 28:3095-3114. [PMID: 28981612 PMCID: PMC6095216 DOI: 10.1093/cercor/bhx179] [Citation(s) in RCA: 1238] [Impact Index Per Article: 206.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Revised: 04/26/2017] [Accepted: 06/23/2017] [Indexed: 12/17/2022] Open
Abstract
A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
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Affiliation(s)
- Alexander Schaefer
- 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
| | - Ru Kong
- 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
| | - Evan M Gordon
- VISN 17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA
| | - Timothy O Laumann
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Sciences, Institute of Psychology, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - 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, Charlestown, MA, USA
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore
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40
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Ogawa A, Osada T, Tanaka M, Hori M, Aoki S, Nikolaidis A, Milham MP, Konishi S. Striatal subdivisions that coherently interact with multiple cerebrocortical networks. Hum Brain Mapp 2018; 39:4349-4359. [PMID: 29975005 PMCID: PMC6220841 DOI: 10.1002/hbm.24275] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 06/03/2018] [Accepted: 06/06/2018] [Indexed: 12/21/2022] Open
Abstract
The striatum constitutes the cortical‐basal ganglia loop and receives input from the cerebral cortex. Previous MRI studies have parcellated the human striatum using clustering analyses of structural/functional connectivity with the cerebral cortex. However, it is currently unclear how the striatal regions functionally interact with the cerebral cortex to organize cortical functions in the temporal domain. In the present human functional MRI study, the striatum was parcellated using boundary mapping analyses to reveal the fine architecture of the striatum by focusing on local gradient of functional connectivity. Boundary mapping analyses revealed approximately 100 subdivisions of the striatum. Many of the striatal subdivisions were functionally connected with specific combinations of cerebrocortical functional networks, such as somato‐motor (SM) and ventral attention (VA) networks. Time‐resolved functional connectivity analyses further revealed coherent interactions of multiple connectivities between each striatal subdivision and the cerebrocortical networks (i.e., a striatal subdivision‐SM connectivity and the same striatal subdivision‐VA connectivity). These results suggest that the striatum contains a large number of subdivisions that mediate functional coupling between specific combinations of cerebrocortical networks.
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Affiliation(s)
- Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan.,Sportology Center, Juntendo University School of Medicine, Tokyo, Japan
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, New York, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, New York, USA
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo, Japan.,Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo, Japan.,Sportology Center, Juntendo University School of Medicine, Tokyo, Japan
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41
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Guntupalli JS, Feilong M, Haxby JV. A computational model of shared fine-scale structure in the human connectome. PLoS Comput Biol 2018; 14:e1006120. [PMID: 29664910 PMCID: PMC5922579 DOI: 10.1371/journal.pcbi.1006120] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 04/27/2018] [Accepted: 04/03/2018] [Indexed: 12/20/2022] Open
Abstract
Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study. Resting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory—shared fine-scale structure, a dominant but mostly unexplored component of the human connectome—for analysis and study.
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Affiliation(s)
- J. Swaroop Guntupalli
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
- Vicarious AI, Union City, CA, United States of America
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
| | - James V. Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
- Center for Cognitive Neuroscience, Dartmouth College, Hanover, NH, United States of America
- * E-mail:
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42
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Haak KV, Marquand AF, Beckmann CF. Connectopic mapping with resting-state fMRI. Neuroimage 2018; 170:83-94. [PMID: 28666880 DOI: 10.1016/j.neuroimage.2017.06.075] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 06/19/2017] [Accepted: 06/26/2017] [Indexed: 11/24/2022] Open
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43
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Jakobsen E, Liem F, Klados MA, Bayrak Ş, Petrides M, Margulies DS. Automated individual-level parcellation of Broca's region based on functional connectivity. Neuroimage 2018; 170:41-53. [DOI: 10.1016/j.neuroimage.2016.09.069] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 09/28/2016] [Accepted: 09/29/2016] [Indexed: 10/20/2022] Open
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44
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Webb-Vargas Y, Chen S, Fisher A, Mejia A, Xu Y, Crainiceanu C, Caffo B, Lindquist MA. Big Data and Neuroimaging. Stat Biosci 2017; 9:543-558. [PMID: 29335670 PMCID: PMC5766007 DOI: 10.1007/s12561-017-9195-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 05/04/2017] [Indexed: 10/19/2022]
Abstract
Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.
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45
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Osada T, Suzuki R, Ogawa A, Tanaka M, Hori M, Aoki S, Tamura Y, Watada H, Kawamori R, Konishi S. Functional subdivisions of the hypothalamus using areal parcellation and their signal changes related to glucose metabolism. Neuroimage 2017; 162:1-12. [DOI: 10.1016/j.neuroimage.2017.08.056] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/20/2017] [Accepted: 08/21/2017] [Indexed: 12/14/2022] Open
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46
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Grayson DS, Fair DA. Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. Neuroimage 2017; 160:15-31. [PMID: 28161313 PMCID: PMC5538933 DOI: 10.1016/j.neuroimage.2017.01.079] [Citation(s) in RCA: 249] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Revised: 01/16/2017] [Accepted: 01/31/2017] [Indexed: 02/08/2023] Open
Abstract
The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.
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Affiliation(s)
- David S Grayson
- The MIND Institute, University of California Davis, Sacramento, CA 95817, USA; Center for Neuroscience, University of California Davis, Davis, CA 95616, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA.
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47
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Preti MG, Van De Ville D. Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization. Sci Rep 2017; 7:12773. [PMID: 28986564 PMCID: PMC5630612 DOI: 10.1038/s41598-017-12993-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/14/2017] [Indexed: 12/18/2022] Open
Abstract
Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging sheds light onto moment-to-moment reconfigurations of large-scale functional brain networks. Due to computational limits, connectivity is typically computed using pre-defined atlases, a non-trivial choice that might influence results. Here, we leverage new computational methods to retrieve dFC at the voxel level in terms of dominant patterns of fluctuations, and demonstrate that this new representation is informative to derive meaningful brain parcellations, capturing both long-range interactions and fine-scale local organization. Specifically, voxelwise dFC dominant patterns were captured through eigenvector centrality followed by clustering across time/subjects to yield most representative dominant patterns (RDPs). Voxel-wise labeling according to positive/negative contributions to RDPs, led to 37 unique labels identifying strikingly symmetric dFC long-range patterns. These included 449 contiguous regions, defining a fine-scale parcellation consistent with known cortical/subcortical subdivisions. Our contribution provides an alternative to obtain a whole-brain parcellation that is for the first time driven by voxel-level dFC and bridges the gap between voxel-based approaches and graph theoretical analysis.
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Affiliation(s)
- Maria Giulia Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. .,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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48
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Lee TW, Xue SW. Examination of the validity of the atlas-informed approach to functional parcellation: a resting functional MRI study. Neuroreport 2017; 28:649-53. [PMID: 28538521 DOI: 10.1097/WNR.0000000000000808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
With the advancement in MRI, functional parcellation (FP) of brain structure(s) has become an important topic. However, the large number of voxels is a major obstacle. A-priori partitioning of the brain into several regions of interest (ROIs) is the main data-reduction strategy to simplify brain informatics. This study aims to examine the validity of ROI-based approach to FP by exploring the concordance of the relative distance structures between voxel-wise (raw data) and atlas-informed analyses. Structural and resting state functional MRI (rfMRI) scans of 26 right-handed healthy individuals were selected from the Rockland dataset. Four target regions were included in the analyses, that is, left and right thalamus and amygdala. For each voxel in the target region, four classes of correlation maps (sampling strategies) were constructed from the rfMRI: whole brain, cortex, 150 ROIs, and 70 ROIs (ROIs are informed by anatomical atlases). The relative distance metric between two different voxels was defined as the mean absolute difference of their associated correlation maps. Considering all the possible pairs of voxels in a target region, the relative distance structure was derived and stored in a matrix (distance map). For every target region, the distance maps were very similar across the four classes of sampling strategies, with the grand mean correlation coefficient reaching 0.95. The results confirm the validity of previous ROI-based analyses of rfMRI data in FP. The rationale and limitation are discussed and an analytic strategy of whole-brain FP is proposed.
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49
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Vanni MP, Chan AW, Balbi M, Silasi G, Murphy TH. Mesoscale Mapping of Mouse Cortex Reveals Frequency-Dependent Cycling between Distinct Macroscale Functional Modules. J Neurosci 2017; 37:7513-33. [PMID: 28674167 DOI: 10.1523/JNEUROSCI.3560-16.2017] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 06/13/2017] [Accepted: 06/19/2017] [Indexed: 12/17/2022] Open
Abstract
Connectivity mapping based on resting-state activity in mice has revealed functional motifs of correlated activity. However, the rules by which motifs organize into larger functional modules that lead to hemisphere wide spatial-temporal activity sequences is not clear. We explore cortical activity parcellation in head-fixed, quiet awake GCaMP6 mice from both sexes by using mesoscopic calcium imaging. Spectral decomposition of spontaneous cortical activity revealed the presence of two dominant frequency modes (<1 and ∼3 Hz), each of them associated with a unique spatial signature of cortical macro-parcellation not predicted by classical cytoarchitectonic definitions of cortical areas. Based on assessment of 0.1-1 Hz activity, we define two macro-organizing principles: the first being a rotating polymodal-association pinwheel structure around which activity flows sequentially from visual to barrel then to hindlimb somatosensory; the second principle is correlated activity symmetry planes that exist on many levels within a single domain such as intrahemispheric reflections of sensory and motor cortices. In contrast, higher frequency activity >1 Hz yielded two larger clusters of coactivated areas with an enlarged default mode network-like posterior region. We suggest that the apparent constrained structure for intra-areal cortical activity flow could be exploited in future efforts to normalize activity in diseases of the nervous system.SIGNIFICANCE STATEMENT Increasingly, functional connectivity mapping of spontaneous activity is being used to reveal the organization of the brain. However, because the brain operates across multiple space and time domains a more detailed understanding of this organization is necessary. We used in vivo wide-field calcium imaging of the indicator GCaMP6 in head-fixed, awake mice to characterize the organization of spontaneous cortical activity at different spatiotemporal scales. Correlation analysis defines the presence of two to three superclusters of activity that span traditionally defined functional territories and were frequency dependent. This work helps define the rules for how different cortical areas interact in time and space. We provide a framework necessary for future studies that explore functional reorganization of brain circuits in disease models.
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50
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Abstract
The visual neurosciences have made enormous progress in recent decades, in part because of the ability to drive visual areas by their sensory inputs, allowing researchers to define visual areas reliably across individuals and across species. Similar strategies for parcellating higher-order cortex have proven elusive. Here, using a novel experimental task and nonlinear population receptive field modeling, we map and characterize the topographic organization of several regions in human frontoparietal cortex. We discover representations of both polar angle and eccentricity that are organized into clusters, similar to visual cortex, where multiple gradients of polar angle of the contralateral visual field share a confluent fovea. This is striking because neural activity in frontoparietal cortex is believed to reflect higher-order cognitive functions rather than external sensory processing. Perhaps the spatial topography in frontoparietal cortex parallels the retinotopic organization of sensory cortex to enable an efficient interface between perception and higher-order cognitive processes. Critically, these visual maps constitute well-defined anatomical units that future studies of frontoparietal cortex can reliably target.
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Affiliation(s)
- Wayne E Mackey
- Center for Neural Science, New York University, New York, United States
| | - Jonathan Winawer
- Center for Neural Science, New York University, New York, United States
- Department of Psychology, New York University, New York, United States
| | - Clayton E Curtis
- Center for Neural Science, New York University, New York, United States
- Department of Psychology, New York University, New York, United States
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