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Heukamp NJ, Moliadze V, Mišić M, Usai K, Löffler M, Flor H, Nees F. Beyond the chronic pain stage: default mode network perturbation depends on years lived with back pain. Pain 2025; 166:160-170. [PMID: 38985160 DOI: 10.1097/j.pain.0000000000003335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/07/2024] [Indexed: 07/11/2024]
Abstract
ABSTRACT Research has indicated that the default mode network (DMN) is perturbated in patients with chronic pain when compared with healthy controls, and this perturbation is correlated with the duration of pain during the chronic pain stage. It remains unclear whether DMN adaptations manifest during the subacute pain stage and progress over time because of the duration of pain experience, rather than being a specific correlate of the chronic pain stage. Furthermore, information regarding whether these adaptations are related to cognitive processes of adaptation is lacking. To this end, we examined the DMN in 31 patients with chronic back pain (CBP), 77 patients with subacute back pain (SBP), as well as 39 healthy pain-free controls (HC) applying a graph-theoretic network approach on functional resting-state magnetic resonance imaging. Beyond the comparison between groups, we used a linear analysis considering the years lived with pain (YLP) across all patients with back pain and additionally performed a mediation analysis of the role of cognitive pain coping. In line with previous studies, we found significant DMN perturbation in CBP compared with HC. However, this did not apply to the comparison of CBP with SBP. Instead, we observed a positive correlation between DMN perturbation and YLP. This was significantly mediated by coping attitudes towards pain. Default mode network perturbation may thus reflect neural adaptation processes to pain experience rather than a single correlate of the chronic pain stage and be modulated by cognitive adaption. This points to potentially underinvestigated significant adaptation processes that could enable more fine-grained patient stratification.
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Affiliation(s)
- Nils Jannik Heukamp
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Vera Moliadze
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Mina Mišić
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Katrin Usai
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Martin Löffler
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Psychology, Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Integrative Spinal Research Group, Department of Chiropractic Medicine, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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2
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Royer J, Paquola C, Valk SL, Kirschner M, Hong SJ, Park BY, Bethlehem RAI, Leech R, Yeo BTT, Jefferies E, Smallwood J, Margulies D, Bernhardt BC. Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community. Neuroinformatics 2024; 22:623-634. [PMID: 38568476 DOI: 10.1007/s12021-024-09660-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 11/21/2024]
Abstract
Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.
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Affiliation(s)
- Jessica Royer
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Casey Paquola
- Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Matthias Kirschner
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Thonex, Switzerland
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, USA
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Data Science, Inha University, Incheon, South Korea
- Department of Statistics and Data Science, Inha University, Incheon, South Korea
| | | | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, UK
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Daniel Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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3
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Korem N, Duek O, Ben-Zion Z, Spiller TR, Gordon C, Amen S, Levy I, Harpaz-Rotem I. Post-treatment alterations in white matter integrity in PTSD: Effects on symptoms and functional connectivity a secondary analysis of an RCT. Psychiatry Res Neuroimaging 2024; 343:111864. [PMID: 39111111 DOI: 10.1016/j.pscychresns.2024.111864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/26/2024] [Accepted: 07/31/2024] [Indexed: 08/26/2024]
Abstract
Post-traumatic stress disorder (PTSD) has been linked to altered communication within the limbic system, including reduced structural connectivity in the uncinate fasciculus (UNC; i.e., decreased fractional anisotropy; FA) and reduced resting-state functional connectivity (RSFC) between the hippocampus and ventromedial prefrontal cortex (vmPFC). Previous research has demonstrated attenuation of PTSD symptoms and alterations in RSFC following exposure-based psychotherapy. However, the relationship between changes in structural and functional connectivity patterns and PTSD symptoms following treatment remains unclear. To investigate this, we conducted a secondary analysis of data from a randomized clinical trial of intensive exposure therapy, evaluating alterations in UNC FA, hippocampus-vmPFC RSFC, and PTSD symptoms before (pre-treatment), 7 days after (post-treatment), and 30 days after (follow-up) the completion of therapy. Our results showed that post-treatment changes in RSFC were positively correlated with post-treatment and follow-up changes in UNC FA and that post-treatment changes in UNC FA were positively correlated with post-treatment and follow-up changes in PTSD symptoms. These findings suggest that early changes in functional connectivity are associated with sustained changes in anatomical connectivity, which in turn are linked to reduced PTSD symptom severity.
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Affiliation(s)
- Nachshon Korem
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA.
| | - Or Duek
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Epidemiology, Biostatistics and Community Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ziv Ben-Zion
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Tobias R Spiller
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA; Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Charles Gordon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Shelley Amen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Ifat Levy
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA; Yale University Departments of Psychology and Neuroscience, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Ilan Harpaz-Rotem
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA; Yale University Departments of Psychology and Neuroscience, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA
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4
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Del Vecchio M, Bontemps B, Lance F, Gannerie A, Sipp F, Albertini D, Cassani CM, Chatard B, Dupin M, Lachaux JP. Introducing HiBoP: a Unity-based visualization software for large iEEG datasets. J Neurosci Methods 2024; 409:110179. [PMID: 38823595 DOI: 10.1016/j.jneumeth.2024.110179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/02/2024] [Accepted: 05/22/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG. NEW METHOD We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data. RESULTS We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum. COMPARISON WITH EXISTING METHODS(S) Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares. CONCLUSION HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.
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Affiliation(s)
- Maria Del Vecchio
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma 43125, Italy
| | - Benjamin Bontemps
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Florian Lance
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Adrien Gannerie
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Florian Sipp
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Davide Albertini
- Dipartimento di Medicina e Chirurgia, Università di Parma, Via Volturno 39, Parma 43125, Italy
| | - Chiara Maria Cassani
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma 43125, Italy; Department of School of Advanced Studies, University of Camerino, Italy
| | - Benoit Chatard
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Maryne Dupin
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France.
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5
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Ding Z, Huang Y, Zeng X, Jiang S, Feng S, Wang Z, Wang L, Wang Z, Xu Y, Liu Y. Contrastive voxel clustering for multiscale modeling of brain network. Neuroimage 2024; 297:120755. [PMID: 39074761 DOI: 10.1016/j.neuroimage.2024.120755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.
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Affiliation(s)
- Zhiyuan Ding
- Johns Hopkins University School of Medicine, Baltimore, USA
| | - Yulang Huang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiangzhu Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Shiyin Jiang
- University of Electronic Science and Technology of China, Chengdu, China
| | | | | | - Ling Wang
- University of Electronic Science and Technology of China, Chengdu, China.
| | - Zeng Wang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yingying Xu
- Department of Radiology, Peking University Sixth Hospital, Beijing, China
| | - Yan Liu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
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6
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Ling Q, Liu A, Li Y, McKeown MJ, Chen X. fMRI-based spatio-temporal parcellations of the human brain. Curr Opin Neurol 2024; 37:369-380. [PMID: 38804205 DOI: 10.1097/wco.0000000000001280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
PURPOSE OF REVIEW Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. RECENT FINDINGS Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.
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Affiliation(s)
- Qinrui Ling
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Aiping Liu
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Yu Li
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Martin J McKeown
- Department of Medicine, University of British Columbia, Vancouver, Vancouver V6T2B5, Canada
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
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7
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Ranjbar A, Suratgar AA, Menhaj MB, Abbasi-Asl R. Structurally-constrained encoding framework using a multi-voxel reduced-rank latent model for human natural vision. J Neural Eng 2024; 21:046027. [PMID: 38986451 DOI: 10.1088/1741-2552/ad6184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective. Voxel-wise visual encoding models based on convolutional neural networks (CNNs) have emerged as one of the prominent predictive tools of human brain activity via functional magnetic resonance imaging signals. While CNN-based models imitate the hierarchical structure of the human visual cortex to generate explainable features in response to natural visual stimuli, there is still a need for a brain-inspired model to predict brain responses accurately based on biomedical data.Approach. To bridge this gap, we propose a response prediction module called the Structurally Constrained Multi-Output (SCMO) module to include homologous correlations that arise between a group of voxels in a cortical region and predict more accurate responses.Main results. This module employs all the responses across a visual area to predict individual voxel-wise BOLD responses and therefore accounts for the population activity and collective behavior of voxels. Such a module can determine the relationships within each visual region by creating a structure matrix that represents the underlying voxel-to-voxel interactions. Moreover, since each response module in visual encoding tasks relies on the image features, we conducted experiments using two different feature extraction modules to assess the predictive performance of our proposed module. Specifically, we employed a recurrent CNN that integrates both feedforward and recurrent interactions, as well as the popular AlexNet model that utilizes feedforward connections.Significance.We demonstrate that the proposed framework provides a reliable predictive ability to generate brain responses across multiple areas, outperforming benchmark models in terms of stability and coherency of features.
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Affiliation(s)
- Amin Ranjbar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Amir Abolfazl Suratgar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Mohammad Bagher Menhaj
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
- Distributed and Intelligence Optimization Research Laboratory (DIOR Lab.), Tehran, Iran
| | - Reza Abbasi-Asl
- Department of Neurology, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America
- UCSF Weill Institute for Neurosciences, San Francisco, CA, United States of America
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Wu YH, Podvalny E, Levinson M, He BJ. Network mechanisms of ongoing brain activity's influence on conscious visual perception. Nat Commun 2024; 15:5720. [PMID: 38977709 PMCID: PMC11231278 DOI: 10.1038/s41467-024-50102-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/28/2024] [Indexed: 07/10/2024] Open
Abstract
Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.
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Affiliation(s)
- Yuan-Hao Wu
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Ella Podvalny
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
- The Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Max Levinson
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Biyu J He
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
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9
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Gbyl K, Labanauskas V, Lundsgaard CC, Mathiassen A, Ryszczuk A, Siebner HR, Rostrup E, Madsen K, Videbech P. Electroconvulsive therapy disrupts functional connectivity between hippocampus and posterior default mode network. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110981. [PMID: 38373628 DOI: 10.1016/j.pnpbp.2024.110981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/13/2024] [Accepted: 02/16/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND The mechanisms underlying memory deficits after electroconvulsive therapy (ECT) remain unclear but altered functional interactions between hippocampus and neocortex may play a role. OBJECTIVES To test whether ECT reduces functional connectivity between hippocampus and posterior regions of the default mode network (DMN) and to examine whether altered hippocampal-neocortical functional connectivity correlates with memory impairment. A secondary aim was to explore if these connectivity changes are present 6 months after ECT. METHODS In-patients with severe depression (n = 35) received bitemporal ECT. Functional connectivity of the hippocampus was probed with resting-state fMRI before the first ECT-session, after the end of ECT, and at a six-month follow-up. Memory was assessed with the Verbal Learning Test - Delayed Recall. Seed-based connectivity analyses established connectivity of four hippocampal seeds, covering the anterior and posterior parts of the right and left hippocampus. RESULTS Compared to baseline, three of four hippocampal seeds became less connected to the core nodes of the posterior DMN in the week after ECT with Cohen's d ranging from -0.9 to -1.1. At the group level, patients showed post-ECT memory impairment, but individual changes in delayed recall were not correlated with the reduction in hippocampus-DMN connectivity. At six-month follow-up, no significant hippocampus-DMN reductions in connectivity were evident relative to pre-ECT, and memory scores had returned to baseline. CONCLUSION ECT leads to a temporary disruption of functional hippocampus-DMN connectivity in patients with severe depression, but the change in connectivity strength is not related to the individual memory impairment.
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Affiliation(s)
- Krzysztof Gbyl
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
| | - Vytautas Labanauskas
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Christoffer Cramer Lundsgaard
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - André Mathiassen
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Adam Ryszczuk
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Hartwig Roman Siebner
- Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Egill Rostrup
- Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
| | - Kristoffer Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Poul Videbech
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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10
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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11
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Abu Mhanna HY, Omar AF, Radzi YM, Oglat AA, Akhdar HF, Ewaidat HA, Almahmoud A, Badarneh LA, Malkawi AA, Malkawi A. Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery. J Multidiscip Healthc 2024; 17:2409-2424. [PMID: 38784380 PMCID: PMC11111578 DOI: 10.2147/jmdh.s470809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.
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Affiliation(s)
| | - Ahmad Fairuz Omar
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | - Yasmin Md Radzi
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | - Ammar A Oglat
- Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, 13133, Jordan
| | - Hanan Fawaz Akhdar
- Physics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia
| | - Haytham Al Ewaidat
- Department of Allied Medical Sciences-Radiologic Technology, Jordan University of Science and Technology (J.U.S.T), Irbid, 22110, Jordan
| | - Abdallah Almahmoud
- Department of Allied Medical Sciences-Radiologic Technology, Jordan University of Science and Technology (J.U.S.T), Irbid, 22110, Jordan
| | - Laith Al Badarneh
- School of Physics, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
| | | | - Ahmed Malkawi
- Business Department, Al-Zaytoonah University, Amman, 594, Jordan
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12
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Gao Y, Feng R, Ouyang X, Zhou Z, Bao W, Li Y, Zhuo L, Hu X, Li H, Zhang L, Huang G, Huang X. Multivariate association between psychosocial environment, behaviors, and brain functional networks in adolescent depression. Asian J Psychiatr 2024; 95:104009. [PMID: 38520945 DOI: 10.1016/j.ajp.2024.104009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/06/2024] [Accepted: 03/17/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Adolescent depression shows high clinical heterogeneity. Brain functional networks serve as a powerful tool for investigating neural mechanisms underlying depression profiles. A key challenge is to characterize how variation in brain functional organization links to behavioral features and psychosocial environmental influences. METHODS We recruited 80 adolescents with major depressive disorder (MDD) and 42 healthy controls (HCs). First, we estimated the differences in functional connectivity of resting-state networks (RSN) between the two groups. Then, we used sparse canonical correlation analysis to characterize patterns of associations between RSN connectivity and symptoms, cognition, and psychosocial environmental factors in MDD adolescents. Clustering analysis was applied to stratify patients into homogenous subtypes according to these brain-behavior-environment associations. RESULTS MDD adolescents showed significantly hyperconnectivity between the ventral attention and cingulo-opercular networks compared with HCs. We identified one reliable pattern of covariation between RSN connectivity and clinical/environmental features in MDD adolescents. In this pattern, psychosocial factors, especially the interpersonal and family relationships, were major contributors to variation in connectivity of salience, cingulo-opercular, ventral attention, subcortical and somatosensory-motor networks. Based on this association, we categorized patients into two subgroups which showed different environment and symptoms characteristics, and distinct connectivity alterations. These differences were covered up when the patients were taken as a whole group. CONCLUSION This study identified the environmental exposures associated with specific functional networks in MDD youths. Our findings emphasize the importance of the psychosocial context in assessing brain function alterations in adolescent depression and have the potential to promote targeted treatment and precise prevention.
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Affiliation(s)
- Yingxue Gao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Ruohan Feng
- Department of Radiology, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Xinqin Ouyang
- Department of Radiology, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Zilin Zhou
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Weijie Bao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Yang Li
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Lihua Zhuo
- Department of Radiology, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Xinyue Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Hailong Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Lianqing Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Guoping Huang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China
| | - Xiaoqi Huang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China; The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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13
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Ciric R, Xu A, Poldrack RA. hyve, a compositional visualisation engine for brain imaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590179. [PMID: 38659772 PMCID: PMC11042383 DOI: 10.1101/2024.04.18.590179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries-such as volumes, surfaces, and networks-have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.
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Affiliation(s)
- Rastko Ciric
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Anna Xu
- Department of Psychology, Stanford University, Stanford, CA, USA
- Stanford Data Science, Stanford University, Stanford, CA, USA
| | - Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
- Stanford Data Science, Stanford University, Stanford, CA, USA
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14
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane JM, Malhotra AK. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol Psychiatry 2024; 29:929-938. [PMID: 38177349 PMCID: PMC11176002 DOI: 10.1038/s41380-023-02381-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA.
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John M Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anil K Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
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15
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Luo Z, Yin E, Zeng LL, Shen H, Su J, Peng L, Yan Y, Hu D. Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas. iScience 2024; 27:109206. [PMID: 38439977 PMCID: PMC10910261 DOI: 10.1016/j.isci.2024.109206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/24/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.
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Affiliation(s)
- Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
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16
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn. PLoS Comput Biol 2024; 20:e1011942. [PMID: 38498530 PMCID: PMC10977879 DOI: 10.1371/journal.pcbi.1011942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/28/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
| | - Hanad Sharmarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - François Paugam
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila—Institut Québécois d’Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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17
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Bruno A, Lothmann K, Bludau S, Mohlberg H, Amunts K. New organizational principles and 3D cytoarchitectonic maps of the dorsolateral prefrontal cortex in the human brain. FRONTIERS IN NEUROIMAGING 2024; 3:1339244. [PMID: 38455685 PMCID: PMC10917992 DOI: 10.3389/fnimg.2024.1339244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/29/2024] [Indexed: 03/09/2024]
Abstract
Areas of the dorsolateral prefrontal cortex (DLPFC) are part of the frontoparietal control, default mode, salience, and ventral attention networks. The DLPFC is involved in executive functions, like working memory, value encoding, attention, decision-making, and behavioral control. This functional heterogeneity is not reflected in existing neuroanatomical maps. For example, previous cytoarchitectonic studies have divided the DLPFC into two or four areas. Macroanatomical parcellations of this region rely on gyri and sulci, which are not congruent with cytoarchitectonic parcellations. Therefore, this study aimed to provide a microstructural analysis of the human DLPFC and 3D maps of cytoarchitectonic areas to help address the observed functional variability in studies of the DLPFC. We analyzed ten human post-mortem brains in serial cell-body stained brain sections and mapped areal boundaries using a statistical image analysis approach. Five new areas (i.e., SFG2, SFG3, SFG4, MFG4, and MFG5) were identified on the superior and middle frontal gyrus, i.e., regions corresponding to parts of Brodmann areas 9 and 46. Gray level index profiles were used to determine interregional cytoarchitectural differences. The five new areas were reconstructed in 3D, and probability maps were generated in commonly used reference spaces, considering the variability of areas in stereotaxic space. Hierarchical cluster analysis revealed a high degree of similarity within the identified DLPFC areas while neighboring areas (frontal pole, Broca's region, area 8, and motoric areas) were separable. Comparisons with functional imaging studies revealed specific functional profiles of the DLPFC areas. Our results indicate that the new areas do not follow a simple organizational gradient assumption in the DLPFC. Instead, they are more similar to those of the ventrolateral prefrontal cortex (Broca's areas 44, 45) and frontopolar areas (Fp1, Fp2) than to the more posterior areas. Within the DLPFC, the cytoarchitectonic similarities between areas do not seem to follow a simple anterior-to-posterior gradient either, but cluster along other principles. The new maps are part of the publicly available Julich Brain Atlas and provide a microstructural reference for existing and future imaging studies. Thus, our study represents a further step toward deciphering the structural-functional organization of the human prefrontal cortex.
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Affiliation(s)
- Ariane Bruno
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kimberley Lothmann
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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18
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Heukamp NJ, Banaschewski T, Bokde AL, Desrivières S, Grigis A, Garavan H, Gowland P, Heinz A, Kandić M, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Papadopoulos Orfanos D, Lemaitre H, Löffler M, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Usai K, Vaidya N, Walter H, Whelan R, Schumann G, Flor H, Nees F. Adolescents' pain-related ontogeny shares a neural basis with adults' chronic pain in basothalamo-cortical organization. iScience 2024; 27:108954. [PMID: 38322983 PMCID: PMC10845062 DOI: 10.1016/j.isci.2024.108954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/19/2023] [Accepted: 01/15/2024] [Indexed: 02/08/2024] Open
Abstract
During late adolescence, the brain undergoes ontogenic organization altering subcortical-cortical circuitry. This includes regions implicated in pain chronicity, and thus alterations in the adolescent ontogenic organization could predispose to pain chronicity in adulthood - however, evidence is lacking. Using resting-state functional magnetic resonance imaging from a large European longitudinal adolescent cohort and an adult cohort with and without chronic pain, we examined links between painful symptoms and brain connectivity. During late adolescence, thalamo-, caudate-, and red nucleus-cortical connectivity were positively and subthalamo-cortical connectivity negatively associated with painful symptoms. Thalamo-cortical connectivity, but also subthalamo-cortical connectivity, was increased in adults with chronic pain compared to healthy controls. Our results indicate a shared basis in basothalamo-cortical circuitries between adolescent painful symptomatology and adult pain chronicity, with the subthalamic pathway being differentially involved, potentially due to a hyperconnected thalamo-cortical pathway in chronic pain and ontogeny-driven organization. This can inform neuromodulation-based prevention and early intervention.
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Affiliation(s)
- Nils Jannik Heukamp
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King’s College London, London, UK
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Mina Kandić
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
| | - Martin Löffler
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Clinical Psychology, Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Integrative Spinal Research Group, Department of Chiropractic Medicine, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075 Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Katrin Usai
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Berlin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - IMAGEN Consortium
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King’s College London, London, UK
- NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont 05405, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
- Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
- Clinical Psychology, Department of Experimental Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Integrative Spinal Research Group, Department of Chiropractic Medicine, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075 Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin, Berlin, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Berlin, Ireland
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
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19
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Thirion B, Aggarwal H, Ponce AF, Pinho AL, Thual A. Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark. Brain Struct Funct 2024; 229:161-181. [PMID: 38012283 DOI: 10.1007/s00429-023-02723-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/11/2023] [Indexed: 11/29/2023]
Abstract
The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.
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Affiliation(s)
| | | | | | - Ana Luísa Pinho
- Department of Computer Science, Western University, London, ON, Canada
- Western Institute for Neuroscience, Western University, London, ON, Canada
| | - Alexis Thual
- Inria, CEA, Université Paris-Saclay, 91120, Palaiseau, France
- Inserm, Collège de France, Paris, France
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20
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Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn DC, Fox P, Habes M. Riemannian frameworks for the harmonization of resting-state functional MRI scans. Med Image Anal 2024; 91:103043. [PMID: 38029722 PMCID: PMC11157681 DOI: 10.1016/j.media.2023.103043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023]
Abstract
Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.
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Affiliation(s)
- Nicolas Honnorat
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
| | - Sudha Seshadri
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ron Killiany
- Center for Biomedical Imaging, Boston University Medical School, Boston, MA, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Fox
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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21
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Rastegarnia S, St-Laurent M, DuPre E, Pinsard B, Bellec P. Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset. Neuroimage 2023; 283:120395. [PMID: 37832707 DOI: 10.1016/j.neuroimage.2023.120395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57-67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54-62 % accuracy). Feature importance maps derived from MLP -our best-performing model- revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions.
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Affiliation(s)
- Shima Rastegarnia
- Université de Montréal, Montréal, QC, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.
| | - Marie St-Laurent
- Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | | | - Basile Pinsard
- Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Pierre Bellec
- Université de Montréal, Montréal, QC, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
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22
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Hirata R, Yoshimura S, Kobayashi K, Aki M, Shibata M, Ueno T, Miyagi T, Oishi N, Murai T, Fujiwara H. Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese. Sci Rep 2023; 13:19724. [PMID: 37957246 PMCID: PMC10643712 DOI: 10.1038/s41598-023-47034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/08/2023] [Indexed: 11/15/2023] Open
Abstract
Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.
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Affiliation(s)
- Risa Hirata
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan
| | - Sayaka Yoshimura
- Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Organization for Promotion of Neurodevelopmental Disorder Research, Kyoto, Japan
| | - Key Kobayashi
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Morio Aki
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Mami Shibata
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Tsukasa Ueno
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
- Integrated Clinical Education Center, Kyoto University Hospital, Kyoto, Japan
| | - Takashi Miyagi
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshiya Murai
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Hironobu Fujiwara
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan.
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan.
- Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
- The General Research Division, Osaka University Research Center on Ethical, Legal and Social Issues, Kyoto, Japan.
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23
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Hougaard A, Gaist D, Garde E, Iversen P, Madsen CG, Kyvik KO, Ashina M, Siebner HR, Madsen KH. Lack of reproducibility of resting-state functional MRI findings in migraine with aura. Cephalalgia 2023; 43:3331024231212574. [PMID: 37950678 DOI: 10.1177/03331024231212574] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2023]
Abstract
BACKGROUND Several studies have applied resting-state functional MRI to examine whether functional brain connectivity is altered in migraine with aura patients. These studies had multiple limitations, including small sample sizes, and reported conflicting results. Here, we performed a large, cross-sectional brain imaging study to reproduce previous findings. METHODS We recruited women aged 30-60 years from the nationwide Danish Twin Registry. Resting-state functional MRI of women with migraine with aura, their co-twins, and unrelated migraine-free twins was performed at a single centre. We carried out an extensive series of brain connectivity data analyses. Patients were compared to migraine-free controls and to co-twins. RESULTS Comparisons were based on data from 160 patients, 30 co-twins, and 136 controls. Patients were similar to controls with regard to age, and several lifestyle characteristics. We replicated clear effects of age on resting-state networks. In contrast, we failed to detect any differences, and to replicate previously reported differences, in functional connectivity between migraine patients with aura and non-migraine controls or their co-twins in any of the analyses. CONCLUSION Given the large sample size and the unbiased population-based design of our study, we conclude that women with migraine with aura have normal resting-state brain connectivity outside of migraine attacks.
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Affiliation(s)
- Anders Hougaard
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital - Rigshospitalet Glostrup, Glostrup, Denmark
- Department of Neurology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - David Gaist
- Research Unit for Neurology, Odense University Hospital, Odense, Denmark; University of Southern Denmark, Odense, Denmark
| | - Ellen Garde
- Department of Public Health, Section of Environmental Health, University of Copenhagen, Copenhagen, Denmark
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
| | - Pernille Iversen
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
| | - Camilla G Madsen
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Radiology, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
| | - Kirsten O Kyvik
- Department of Public Health, Section of Environmental Health, University of Copenhagen, Copenhagen, Denmark
- The Danish Twin Registry, Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, Odense, Denmark
- Odense Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Messoud Ashina
- Danish Headache Center, Department of Neurology, Copenhagen University Hospital - Rigshospitalet Glostrup, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hartwig R Siebner
- Department of Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Neurology, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
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24
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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] [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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25
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Duek O, Korem N, Li Y, Kelmendi B, Amen S, Gordon C, Milne M, Krystal JH, Levy I, Harpaz-Rotem I. Long term structural and functional neural changes following a single infusion of Ketamine in PTSD. Neuropsychopharmacology 2023; 48:1648-1658. [PMID: 37270621 PMCID: PMC10517133 DOI: 10.1038/s41386-023-01606-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 06/05/2023]
Abstract
NMDA receptor antagonists have a vital role in extinction, learning, and reconsolidation processes. During the reconsolidation window, memories are activated into a labile state and can be reconsolidated in an altered form. This concept might have significant clinical implications in treating PTSD. In this pilot study we tested the potential of a single infusion of ketamine, followed by brief exposure therapy, to enhance post-retrieval extinction of PTSD trauma memories. 27 individuals diagnosed with PTSD were randomly assigned to receive either ketamine (0.5 mg/kg 40 min; N = 14) or midazolam (0.045 mg/kg; N = 13) after retrieval of the traumatic memory. 24 h following infusion, participants received a four-day trauma-focused psychotherapy. Symptoms and brain activity were assessed before treatment, at the end of treatment, and at 30-day follow-up. Amygdala activation to trauma scripts (a major biomarker of fear response) served as the main study outcome. Although PTSD symptoms improved equally in both groups, post-treatment, ketamine recipients showed a lower amygdala (-0.33, sd = 0.13, 95%HDI [-0.56,-0.04]) and hippocampus (-0.3 (sd = 0.19), 95%HDI [-0.65, 0.04]; marginal effect) reactivation to trauma memories, compared to midazolam recipients. Post-retrieval ketamine administration was also associated with decreased connectivity between the amygdala and hippocampus (-0.28, sd = 0.11, 95%HDI [-0.46, -0.11]), with no change in amygdala-vmPFC connectivity. Moreover, reduction in fractional anisotropy in bi-lateral uncinate fasciculus was seen in the Ketamine recipients compared with the midazolam recipients (right: post-treatment: -0.01108, 95% HDI [-0.0184,-0.003]; follow-up: -0.0183, 95% HDI [-0.02719,-0.0107]; left: post-treatment: -0.019, 95% HDI [-0.028,-0.011]; follow-up: -0.017, 95% HDI [-0.026,-0.007]). Taken together it is possible that ketamine may enhance post-retrieval extinction of the original trauma memories in humans. These preliminary findings show promising direction toward the capacity to rewrite human traumatic memories and modulate the fear response for at least 30 days post-extinction. When combined with psychotherapy for PTSD, further investigation of ketamine dose, timing of administration, and frequency of administration, is warranted.
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Affiliation(s)
- Or Duek
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA.
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA.
| | - Nachshon Korem
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA
- Departments of Comparative Medicine and Neuroscience, Yale University School of Medicine, New-Haven, CT, USA
| | - Yutong Li
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
| | - Ben Kelmendi
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA
| | - Shelley Amen
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA
| | - Charles Gordon
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA
| | - Madison Milne
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA
| | - Ifat Levy
- Departments of Comparative Medicine and Neuroscience, Yale University School of Medicine, New-Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Ilan Harpaz-Rotem
- Department of Psychiatry, Yale University School of Medicine, New-Haven, CT, USA.
- The National Center for PTSD, VA CT Healthcare System, West Haven, CT, USA.
- Department of Psychology, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
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26
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Ling Q, Liu A, Li Y, Mi T, Chan P, Liu Y, Chen X. Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3556-3565. [PMID: 37682656 DOI: 10.1109/tnsre.2023.3310340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.
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27
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Zhao B, Li T, Li Y, Fan Z, Xiong D, Wang X, Gao M, Smith SM, Zhu H. An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-23. [PMID: 38770197 PMCID: PMC11105703 DOI: 10.1162/imag_a_00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
- These authors contributed equally to this work
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- These authors contributed equally to this work
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mufeng Gao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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28
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Rubio J, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal D, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Maholtra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. RESEARCH SQUARE 2023:rs.3.rs-3185688. [PMID: 37609149 PMCID: PMC10441472 DOI: 10.21203/rs.3.rs-3185688/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Northwell Health
| | - Todd Lencz
- Zucker School of Medicine at Hofstra/Northwell
| | - Hengyi Cao
- The Feinstein Institute for Medical Research
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29
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Kleven H, Gillespie TH, Zehl L, Dickscheid T, Bjaalie JG, Martone ME, Leergaard TB. AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures. Sci Data 2023; 10:486. [PMID: 37495585 PMCID: PMC10372146 DOI: 10.1038/s41597-023-02389-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 07/14/2023] [Indexed: 07/28/2023] Open
Abstract
Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.
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Affiliation(s)
- Heidi Kleven
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | | | - Lyuba Zehl
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jan G Bjaalie
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maryann E Martone
- Department of Neurosciences, University of California, San Diego, USA
| | - Trygve B Leergaard
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
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30
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Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Malhotra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.17.23292779. [PMID: 37503088 PMCID: PMC10371185 DOI: 10.1101/2023.07.17.23292779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
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Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anil Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
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31
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Boerger TF, Pahapill P, Butts AM, Arocho-Quinones E, Raghavan M, Krucoff MO. Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities. Front Hum Neurosci 2023; 17:1170419. [PMID: 37520929 PMCID: PMC10372448 DOI: 10.3389/fnhum.2023.1170419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/16/2023] [Indexed: 08/01/2023] Open
Abstract
In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.
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Affiliation(s)
- Timothy F. Boerger
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Peter Pahapill
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alissa M. Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
- Mayo Clinic, Rochester, MN, United States
| | - Elsa Arocho-Quinones
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Max O. Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United States
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32
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537240. [PMID: 37131781 PMCID: PMC10153168 DOI: 10.1101/2023.04.18.537240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Hanad Sharmarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - Fraçois Paugam
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila - Institut Québécois d'Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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33
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Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S. Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions. Brain 2023; 146:1686-1696. [PMID: 36059063 PMCID: PMC10319760 DOI: 10.1093/brain/awac315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 02/03/2023] Open
Abstract
Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.
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Affiliation(s)
- Clara A Moreau
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Kuldeep Kumar
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Annabelle Harvey
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Guillaume Huguet
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Sebastian G W Urchs
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Laura M Schultz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hanad Sharmarke
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Khadije Jizi
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | | | - Nadine Younis
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Petra Tamer
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Jean-Louis Martineau
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Pierre Orban
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, UdeM, Montréal, QC H1N 3V2, Canada
- Département de Psychiatrie et d’Addictologie, Université de Montréal, Pavillon Roger-Gaudry, C.P. 6128, Succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Ana Isabel Silva
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Sarah Lippé
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
| | - Carrie E Bearden
- Integrative Center for Neurogenetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA 90095, USA
- Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Biobehavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Harvard Medical School, Cambridge, MA 02115, USA
- Boston Children’s Hospital, Tommy Fuss Center for Neuropsychiatric Disease Research, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, CA, USA
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université Paris Cité, Paris, France
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, Montreal, QC H3W 1W5, Canada
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, QC H3T 1C5, Canada
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Li J, Liu Y, Wisnowski JL, Leahy RM. Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain. Neuroimage 2023; 270:119944. [PMID: 36801371 PMCID: PMC10092006 DOI: 10.1016/j.neuroimage.2023.119944] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 01/06/2023] [Accepted: 02/14/2023] [Indexed: 02/21/2023] Open
Abstract
The human brain is a complex network that exhibits dynamic fluctuations in activity across space and time. Depending on the analysis method, canonical brain networks identified from resting-state fMRI (rs-fMRI) are typically constrained to be either orthogonal or statistically independent in their spatial and/or temporal domains. We avoid imposing these potentially unnatural constraints through the combination of a temporal synchronization process ("BrainSync") and a three-way tensor decomposition method ("NASCAR") to jointly analyze rs-fMRI data from multiple subjects. The resulting set of interacting networks comprises minimally constrained spatiotemporal distributions, each representing one component of functionally coherent activity across the brain. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. This functional network atlas could help explore group and individual differences in neurocognitive function, as we demonstrate in the context of ADHD and IQ prediction.
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Affiliation(s)
- Jian Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yijun Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Wisnowski
- Radiology and Pediatrics, Division of Neonatology, Children's Hospital Los Angeles, Los Angeles, CA, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
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Kong R, Tan YR, Wulan N, Ooi LQR, Farahibozorg SR, Harrison S, Bijsterbosch JD, Bernhardt BC, Eickhoff S, Yeo BTT. Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior. Neuroimage 2023; 273:120044. [PMID: 36940760 DOI: 10.1016/j.neuroimage.2023.120044] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 03/23/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.
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Affiliation(s)
- Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Yan Rui Tan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Naren Wulan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samuel Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Simon 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
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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36
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Gong W, Bai S, Zheng YQ, Smith SM, Beckmann CF. Supervised Phenotype Discovery From Multimodal Brain Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:834-849. [PMID: 36318559 DOI: 10.1109/tmi.2022.3218720] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.
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37
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Riegner G, Posey G, Oliva V, Jung Y, Mobley W, Zeidan F. Disentangling self from pain: mindfulness meditation-induced pain relief is driven by thalamic-default mode network decoupling. Pain 2023; 164:280-291. [PMID: 36095039 PMCID: PMC9823141 DOI: 10.1097/j.pain.0000000000002731] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/14/2022] [Indexed: 02/06/2023]
Abstract
ABSTRACT For millenniums, mindfulness was believed to diminish pain by reducing the influence of self-appraisals of noxious sensations. Today, mindfulness meditation is a highly popular and effective pain therapy that is believed to engage multiple, nonplacebo-related mechanisms to attenuate pain. Recent evidence suggests that mindfulness meditation-induced pain relief is associated with the engagement of unique cortico-thalamo-cortical nociceptive filtering mechanisms. However, the functional neural connections supporting mindfulness meditation-based analgesia remain unknown. This mechanistically focused clinical trial combined functional magnetic resonance imaging with psychophysical pain testing (49°C stimulation and pain visual analogue scales) to identify the neural connectivity supporting the direct modulation of pain-related behavioral and neural responses by mindfulness meditation. We hypothesized that mindfulness meditation-based pain relief would be reflected by greater decoupling between brain mechanisms supporting appraisal (prefrontal) and nociceptive processing (thalamus). After baseline pain testing, 40 participants were randomized to a well-validated, 4-session mindfulness meditation or book-listening regimen. Functional magnetic resonance imaging and noxious heat (49°C; right calf) were combined during meditation to test study hypotheses. Mindfulness meditation significantly reduced behavioral and neural pain responses when compared to the controls. Preregistered (NCT03414138) whole-brain analyses revealed that mindfulness meditation-induced analgesia was moderated by greater thalamus-precuneus decoupling and ventromedial prefrontal deactivation, respectively, signifying a pain modulatory role across functionally distinct neural mechanisms supporting self-referential processing. Two separate preregistered seed-to-seed analyses found that mindfulness meditation-based pain relief was also associated with weaker contralateral thalamic connectivity with the prefrontal and primary somatosensory cortex, respectively. Thus, we propose that mindfulness meditation is associated with a novel self-referential nociceptive gating mechanism to reduce pain.
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Affiliation(s)
- Gabriel Riegner
- Department of Anesthesiology; University of California San Diego; La Jolla, CA, 92037; United States
| | - Grace Posey
- Department of Medicine; Tulane University School of Medicine; New Orleans, LA, 70112; United States
| | - Valeria Oliva
- Department of Anesthesiology; University of California San Diego; La Jolla, CA, 92037; United States
| | - Youngkyoo Jung
- Department of Radiology; University of California Davis; Sacramento, CA, 95817; United States
| | - William Mobley
- Department of Neurosciences; University of California San Diego; La Jolla, CA, 92093; United States
| | - Fadel Zeidan
- Department of Anesthesiology; University of California San Diego; La Jolla, CA, 92037; United States
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38
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Fürtjes AE, Cole JH, Couvy-Duchesne B, Ritchie SJ. A quantified comparison of cortical atlases on the basis of trait morphometricity. Cortex 2023; 158:110-126. [PMID: 36516597 DOI: 10.1016/j.cortex.2022.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences. METHODS Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas. RESULTS Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated. DISCUSSION Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.
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Affiliation(s)
- Anna E Fürtjes
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Baptiste Couvy-Duchesne
- Paris Brain Institute (ICM), Inserm (U 1127), CNRS (UMR 7225), Sorbonne University, Inria Paris, Aramis Project-team, Paris, France; Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
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Hahn S, Owens MM, Yuan D, Juliano AC, Potter A, Garavan H, Allgaier N. Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study. Cereb Cortex 2022; 33:176-194. [PMID: 35238352 PMCID: PMC9758581 DOI: 10.1093/cercor/bhac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 11/13/2022] Open
Abstract
The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.
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Affiliation(s)
- Sage Hahn
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Max M Owens
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - DeKang Yuan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Anthony C Juliano
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Alexandra Potter
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Hugh Garavan
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
| | - Nicholas Allgaier
- Departments of Complex Systems and Psychiatry, University of Vermont, Burlington, VT 05401, United States
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40
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Abdallah M, Iovene V, Zanitti G, Wassermann D. Meta-analysis of the functional neuroimaging literature with probabilistic logic programming. Sci Rep 2022; 12:19431. [PMID: 36371447 PMCID: PMC9653422 DOI: 10.1038/s41598-022-21801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis by designing NeuroLang: a domain-specific language to express and test hypotheses using probabilistic first-order logic programming. By leveraging formalisms found at the crossroads of artificial intelligence and knowledge representation, NeuroLang provides the expressivity to address a larger repertoire of hypotheses in a meta-analysis, while seamlessly modeling the uncertainty inherent to neuroimaging data. We demonstrate the language's capabilities in conducting comprehensive neuroimaging meta-analysis through use-case examples that address questions of structure-function associations. Specifically, we infer the specific functional roles of three canonical brain networks, support the role of the visual word-form area in visuospatial attention, and investigate the heterogeneous organization of the frontoparietal control network.
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Affiliation(s)
- Majd Abdallah
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Valentin Iovene
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Gaston Zanitti
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Demian Wassermann
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France.
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41
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Thomas AW, Ré C, Poldrack RA. Interpreting mental state decoding with deep learning models. Trends Cogn Sci 2022; 26:972-986. [PMID: 36223760 DOI: 10.1016/j.tics.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 01/12/2023]
Abstract
In mental state decoding, researchers aim to identify the set of mental states (e.g., experiencing happiness or fear) that can be reliably identified from the activity patterns of a brain region (or network). Deep learning (DL) models are highly promising for mental state decoding because of their unmatched ability to learn versatile representations of complex data. However, their widespread application in mental state decoding is hindered by their lack of interpretability, difficulties in applying them to small datasets, and in ensuring their reproducibility and robustness. We recommend approaching these challenges by leveraging recent advances in explainable artificial intelligence (XAI) and transfer learning, and also provide recommendations on how to improve the reproducibility and robustness of DL models in mental state decoding.
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Affiliation(s)
- Armin W Thomas
- Stanford Data Science, Stanford University, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Russell A Poldrack
- Stanford Data Science, Stanford University, Stanford, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA
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42
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Jakubiec L, Chirokoff V, Abdallah M, Sanz-Arigita E, Dupuy M, Swendsen J, Berthoz S, Gierski F, Guionnet S, Misdrahi D, Serre F, Auriacombe M, Fatseas M. The Executive Functioning Paradox in Substance Use Disorders. Biomedicines 2022; 10:2728. [PMID: 36359247 PMCID: PMC9687962 DOI: 10.3390/biomedicines10112728] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 08/26/2023] Open
Abstract
Deficits in neurocognitive functioning are trait-like vulnerabilities that have been widely studied in persons with substance use disorders (SUD), but their role in the craving-use association and relapse vulnerability remains poorly understood. The main objectives of this study were to examine whether executive capacities moderate the magnitude of the craving-substance use relationship, and if this influence is correlated with the functional connectivity of cerebral networks, combining rsfMRI examinations and ecological momentary assessment (EMA). Eighty-six patients beginning outpatient treatment for alcohol, tobacco or cannabis addiction and 40 healthy controls completed neuropsychological tests followed by EMA to collect real-time data on craving. Fifty-four patients and 30 healthy controls also completed a resting-state fMRI before the EMA. Among the patients with SUD, better verbal fluency and resistance to interference capacities were associated with a greater propensity to use substances when the individual was experiencing craving. Preliminary rsfMRI results identified specific networks that interacted with executive performance capacities to influence the magnitude of the craving-use association. Individuals with better executive functioning may be more prone to relapse after craving episodes. Specifically, better resistance to interference and cognitive flexibility skills may reduce attention to distracting stimuli, leading to a greater awareness of craving and susceptibility to use substances.
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Affiliation(s)
- Louise Jakubiec
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
- Department of Addictology, CHU Bordeaux, 33000 Bordeaux, France
- Department of Addictology, CH Charles Perrens, 33000 Bordeaux, France
| | - Valentine Chirokoff
- University of Bordeaux, CNRS, INCIA, UMR 5287, EPHE PSL Research University, 33000 Bordeaux, France
| | - Majd Abdallah
- Parietal Team, INRIA Saclay Ile-de-France, 91120 Palaiseau, France
| | | | - Maud Dupuy
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
| | - Joel Swendsen
- University of Bordeaux, CNRS, INCIA, UMR 5287, EPHE PSL Research University, 33000 Bordeaux, France
| | - Sylvie Berthoz
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
- Institut Mutualiste Montsouris, Department of Psychiatry for Adolescents and Young Adults, 75014 Paris, France
| | - Fabien Gierski
- Laboratoire Cognition, Santé et Société, University of Reims Champagne Ardenne, 51571 Reims, France
| | - Sarah Guionnet
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
| | - David Misdrahi
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
- Department of Addictology, CH Charles Perrens, 33000 Bordeaux, France
| | - Fuschia Serre
- University of Bordeaux, CNRS, SANPSY, UMR 6033, 33000 Bordeaux, France
| | - Marc Auriacombe
- Department of Addictology, CH Charles Perrens, 33000 Bordeaux, France
- University of Bordeaux, CNRS, SANPSY, UMR 6033, 33000 Bordeaux, France
| | - Melina Fatseas
- University of Bordeaux, CNRS, INCIA, UMR 5287, 33000 Bordeaux, France
- Department of Addictology, CHU Bordeaux, 33000 Bordeaux, France
- Department of Addictology, CH Charles Perrens, 33000 Bordeaux, France
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43
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Abdallah M, Zanitti GE, Iovene V, Wassermann D. Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis. eLife 2022; 11:e76926. [PMID: 36169404 PMCID: PMC9578708 DOI: 10.7554/elife.76926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research. Moreover, recent efforts aiming to map the LPFC through meta-analysis are limited, either in scope or in the inferred specificity of structure-function associations. These limitations are in part due to the limited expressiveness of commonly-used data analysis tools, which restricts the breadth and complexity of questions that can be expressed in a meta-analysis. Here, we adopt NeuroLang, a novel approach to more expressive meta-analysis based on probabilistic first-order logic programming, to infer the organizing principles of the LPFC from 14,371 neuroimaging studies. Our findings reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second most variance in meta-analytic connectivity across the LPFC. Moreover, we identify a unimodal-to-transmodal spectrum of coactivation patterns along with a concrete-to-abstract axis of structure-function associations extending from caudal to rostral regions of the LPFC. Finally, we infer inter-hemispheric asymmetries along the principal rostrocaudal gradient, identifying hemisphere-specific associations with topics of language, memory, response inhibition, and sensory processing. Overall, this study provides a comprehensive meta-analytic mapping of the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.
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Affiliation(s)
- Majd Abdallah
- MIND team, Inria, CEA, Université Paris-SaclayPalaiseauFrance
- NeuroSpin, CEA, Université Paris-SaclayGif-sur-YvetteFrance
| | - Gaston E Zanitti
- MIND team, Inria, CEA, Université Paris-SaclayPalaiseauFrance
- NeuroSpin, CEA, Université Paris-SaclayGif-sur-YvetteFrance
| | - Valentin Iovene
- MIND team, Inria, CEA, Université Paris-SaclayPalaiseauFrance
- NeuroSpin, CEA, Université Paris-SaclayGif-sur-YvetteFrance
| | - Demian Wassermann
- MIND team, Inria, CEA, Université Paris-SaclayPalaiseauFrance
- NeuroSpin, CEA, Université Paris-SaclayGif-sur-YvetteFrance
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44
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Peng L, Luo Z, Zeng LL, Hou C, Shen H, Zhou Z, Hu D. Parcellating the human brain using resting-state dynamic functional connectivity. Cereb Cortex 2022; 33:3575-3590. [PMID: 35965076 DOI: 10.1093/cercor/bhac293] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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Bruno A, Bludau S, Mohlberg H, Amunts K. Cytoarchitecture, intersubject variability, and 3D mapping of four new areas of the human anterior prefrontal cortex. Front Neuroanat 2022; 16:915877. [PMID: 36032993 PMCID: PMC9403835 DOI: 10.3389/fnana.2022.915877] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/01/2022] [Indexed: 11/30/2022] Open
Abstract
The dorsolateral prefrontal cortex (DLPFC) plays a key role in cognitive control and executive functions, including working memory, attention, value encoding, decision making, monitoring, and controlling behavioral strategies. However, the relationships between this variety of functions and the underlying cortical areas, which specifically contribute to these functions, are not yet well-understood. Existing microstructural maps differ in the number, localization, and extent of areas of the DLPFC. Moreover, there is a considerable intersubject variability both in the sulcal pattern and in the microstructure of this region, which impedes comparison with functional neuroimaging studies. The aim of this study was to provide microstructural, cytoarchitectonic maps of the human anterior DLPFC in 3D space. Therefore, we analyzed 10 human post-mortem brains and mapped their borders using a well-established approach based on statistical image analysis. Four new areas (i.e., SFS1, SFS2, MFG1, and MFG2) were identified in serial, cell-body stained brain sections that occupy the anterior superior frontal sulcus and middle frontal gyrus, i.e., a region corresponding to parts of Brodmann areas 9 and 46. Differences between areas in cytoarchitecture were captured using gray level index profiles, reflecting changes in the volume fraction of cell bodies from the surface of the brain to the cortex-white matter border. A hierarchical cluster analysis of these profiles indicated that areas of the anterior DLPFC displayed higher cytoarchitectonic similarity between each other than to areas of the neighboring frontal pole (areas Fp1 and Fp2), Broca's region (areas 44 and 45) of the ventral prefrontal cortex, and posterior DLPFC areas (8d1, 8d2, 8v1, and 8v2). Area-specific, cytoarchitectonic differences were found between the brains of males and females. The individual areas were 3D-reconstructed, and probability maps were created in the MNI Colin27 and ICBM152casym reference spaces to take the variability of areas in stereotaxic space into account. The new maps contribute to Julich-Brain and are publicly available as a resource for studying neuroimaging data, helping to clarify the functional and organizational principles of the human prefrontal cortex.
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Affiliation(s)
- Ariane Bruno
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- *Correspondence: Ariane Bruno
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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46
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Comprehensive decoding mental processes from Web repositories of functional brain images. Sci Rep 2022; 12:7050. [PMID: 35488032 PMCID: PMC9054752 DOI: 10.1038/s41598-022-10710-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/05/2022] [Indexed: 11/08/2022] Open
Abstract
Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope-from domain-specific to system-level analysis-and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault-an open repository of fMRI statistical maps with unconstrained annotations-and Cognitive Atlas-an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity.
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47
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Bernhardt BC, Smallwood J, Keilholz S, Margulies DS. Gradients in Brain Organization. Neuroimage 2022; 251:118987. [PMID: 35151850 DOI: 10.1016/j.neuroimage.2022.118987] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 02/08/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
- Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | | | - Shella Keilholz
- Biomedical Engineering, Emory University / Georgia Institute of Technology, Atlanta, Georgia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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48
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Geffen T, Smallwood J, Finke C, Olbrich S, Sjoerds Z, Schlagenhauf F. Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD). Neuroimage Clin 2021; 33:102915. [PMID: 34933233 PMCID: PMC8688720 DOI: 10.1016/j.nicl.2021.102915] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/06/2021] [Accepted: 12/12/2021] [Indexed: 01/26/2023]
Abstract
Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD.
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Affiliation(s)
- Tal Geffen
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Germany.
| | | | - Carsten Finke
- Department of Neurology, Charité - Universitätsmedizin, Berlin, Germany; Humboldt-Universitaet zu Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Zurich, Switzerland
| | - Zsuzsika Sjoerds
- Cognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain & Cognition, Leiden University, Leiden, Netherlands
| | - Florian Schlagenhauf
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Germany; Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
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49
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Li YP, Cooper SR, Braver TS. The role of neural load effects in predicting individual differences in working memory function. Neuroimage 2021; 245:118656. [PMID: 34678433 PMCID: PMC8880845 DOI: 10.1016/j.neuroimage.2021.118656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/18/2021] [Accepted: 10/11/2021] [Indexed: 12/02/2022] Open
Abstract
Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.
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Affiliation(s)
- Y Peeta Li
- Department of Psychology, University of Oregon, 1227 University St, Eugene, OR 97403, United States.
| | - Shelly R Cooper
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States
| | - Todd S Braver
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, 1 Brookings Drive, Saint Louis, MO 63130, United States
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50
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Domhof JWM, Jung K, Eickhoff SB, Popovych OV. Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels. Netw Neurosci 2021; 5:798-830. [PMID: 34746628 PMCID: PMC8567834 DOI: 10.1162/netn_a_00202] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models. The structural and functional connectivities of the brain, which reflect the anatomical connections of axonal bundles and the amount of coactivation between brain regions, respectively, only weakly correlate with each other. In order to enhance and investigate this relationship, large-scale whole-brain dynamical models were involved in this branch of research. However, how the definitions of the brain regions parcellated according to a so-called brain atlas influence these models has so far not been systematically assessed. In this article, we show that this influence can be large, and link group-averaged, atlas-induced deviations to network properties extracted from both types of connectivity. Additionally, we demonstrate that the same association does not apply to subject-specific variations. These results may contribute to the further personalization of the whole-brain models.
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Affiliation(s)
- Justin W M Domhof
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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