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Li C, Lu Y, Yu S, Cui Y. TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis. Med Image Anal 2024; 97:103297. [PMID: 39154619 DOI: 10.1016/j.media.2024.103297] [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: 12/29/2023] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/20/2024]
Abstract
Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.
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Affiliation(s)
- Chengyi Li
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brian Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China
| | - Yuheng Lu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brian Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brian Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
| | - Yue Cui
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Brian Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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Cui Y, Li C, Lu Y, Ma L, Cheng L, Cao L, Yu S, Jiang T. Multimodal Connectivity-Based Individual Parcellation and Analysis for Humans and Rhesus Monkeys. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3343-3353. [PMID: 38656866 DOI: 10.1109/tmi.2024.3392946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Individual brains vary greatly in morphology, connectivity and organization. Individualized brain parcellation is capable of precisely localizing subject-specific functional regions. However, most individualization approaches have examined single modalities of data and have not generalized to nonhuman primates. The present study proposed a novel multimodal connectivity-based individual parcellation (MCIP) method, which optimizes within-region homogeneity, spatial continuity and similarity to a reference atlas with the fusion of personal functional and anatomical connectivity. Comprehensive evaluation demonstrated that MCIP outperformed state-of-the-art multimodal individualization methods in terms of functional and anatomical homogeneity, predictability of cognitive measures, heritability, reproducibility and generalizability across species. Comparative investigation showed a higher topographic variability in humans than that in macaques. Therefore, MCIP provides improved accurate and reliable mapping of brain functional regions over existing methods at an individual level across species, and could facilitate comparative and translational neuroscience research.
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Demeter DV, Greene DJ. The promise of precision functional mapping for neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01941-z. [PMID: 39085426 DOI: 10.1038/s41386-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.
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Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.
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Sacchet MD, Keshava P, Walsh SW, Potash RM, Li M, Liu H, Pizzagalli DA. Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:616-625. [PMID: 38417786 PMCID: PMC11156548 DOI: 10.1016/j.bpsc.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 02/10/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior. METHODS We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures. RESULTS Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits. CONCLUSIONS The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.
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Affiliation(s)
- Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
| | - Poorvi Keshava
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Shane W Walsh
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Ruby M Potash
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts; Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; McLean Imaging Center, McLean Hospital, Belmont, Massachusetts
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Wen X, Yang M, Qi S, Wu X, Zhang D. Automated individual cortical parcellation via consensus graph representation learning. Neuroimage 2024; 293:120616. [PMID: 38697587 DOI: 10.1016/j.neuroimage.2024.120616] [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: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China
| | - Xia Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
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6
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Ma L, Zhang Y, Zhang H, Cheng L, Yang Z, Lu Y, Shi W, Li W, Zhuo J, Wang J, Fan L, Jiang T. BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7446-7457. [PMID: 36315537 DOI: 10.1109/tnnls.2022.3213581] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.
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Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 PMCID: PMC11248096 DOI: 10.1038/s41593-024-01618-2] [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: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
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Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
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Nettekoven C, Zhi D, Shahshahani L, Pinho AL, Saadon-Grosman N, Buckner RL, Diedrichsen J. A hierarchical atlas of the human cerebellum for functional precision mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.14.557689. [PMID: 38260680 PMCID: PMC10802446 DOI: 10.1101/2023.09.14.557689] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from 7 large-scale datasets, outperforming existing group atlasses. The new atlas has three further advantages: First, the atlas allows for precision mapping in individuals: The integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the regions are hierarchically organized across 3 levels, allowing analyses at the appropriate level of granularity. Overall, the new atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.
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Affiliation(s)
- Caroline Nettekoven
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
- Department of Computer Science, Western University, London, Ontario, Canada
| | - Da Zhi
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
- Department of Computer Science, Western University, London, Ontario, Canada
| | - Ladan Shahshahani
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Ana Luísa Pinho
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
- Department of Computer Science, Western University, London, Ontario, Canada
| | | | | | - Jörn Diedrichsen
- Western Institute for Neuroscience, Western University, London, Ontario, Canada
- Department of Computer Science, Western University, London, Ontario, Canada
- Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
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Li J, Wang Q, Li K, Yao L, Guo X. Tracking Age-Related Topological Changes in Individual Brain Morphological Networks Across the Human Lifespan. J Magn Reson Imaging 2024; 59:1841-1851. [PMID: 37702277 DOI: 10.1002/jmri.28984] [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: 06/15/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Many studies have shown topological alterations associated with age in population-based brain morphological networks. However, it is not clear how individual brain morphological networks change with age across the lifespan. PURPOSE To characterize age-related topological changes in individual networks and investigate the relationships between individual- and group-based brain networks at the nodal, modular, and connectome levels. STUDY TYPE Retrospective analysis. POPULATION One hundred seventy-nine healthy subjects (108 males and 71 females), aged 6-85 years with a median age of 32 years and an inter-quartile range (IQR) of 26 years. FIELD STRENGTH/SEQUENCE T1-weighted images using the magnetization-prepared rapid gradient echo (MPRAGE) sequences. ASSESSMENT Two nodal-level indicators (nodal similarity and node matching), five modular-level indicators (modularity, intra/inter-module similarity, adjusted mutual information [AMI], and module variation), and five connectome-level indicators (global efficiency, characteristic path length, clustering coefficient, local efficiency, and individual contribution) were calculated in brain morphological networks. Regression models for different indicators were built to examine their lifetime trajectory patterns. STATISTICAL TESTS Single-sample t-test, Mantel's test, Pearson correlation coefficient. A P value <0.05 was considered statistically significant. RESULTS Among 68 nodes, 34 nodes showed significant age-related patterns (all P < 0.05, FDR-corrected) in nodal similarity, including linear decline and quadratic trends. The lifespan trajectory of the connectome-level topological attributes of the individual networks presented U-shaped or inverse U-shaped trends with age. Between the individual- and group-based brain networks, the average nodal similarity was 0.67 and the average AMI of module partitions was 0.57. DATA CONCLUSION The lifespan trajectories of the nodal similarity mainly followed linear decreasing and nonlinear trends, whereas the modularity and the global topological attributes exhibited nonlinear patterns. There was a high degree of consistency at both nodal similarity and modular division between the individual and group networks. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jingming Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Qian Wang
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Ke Li
- Strategic Support Force Medical Center, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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10
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Day TKM, Hermosillo R, Conan G, Randolph A, Perrone A, Earl E, Byington N, Hendrickson TJ, Elison JT, Fair DA, Feczko E. Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample. Dev Cogn Neurosci 2024; 66:101355. [PMID: 38354531 PMCID: PMC10875197 DOI: 10.1016/j.dcn.2024.101355] [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: 02/14/2023] [Revised: 01/06/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.
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Affiliation(s)
- Trevor K M Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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11
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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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12
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Butler ER, Samia N, White S, Gratton C, Nusslock R. Neuroimmune mechanisms connecting violence with internalizing symptoms: A high-dimensional multimodal mediation analysis. Hum Brain Mapp 2024; 45:e26615. [PMID: 38339956 DOI: 10.1002/hbm.26615] [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: 09/05/2023] [Revised: 12/27/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms (ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ ,95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.
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Affiliation(s)
- Ellyn R Butler
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - Noelle Samia
- Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA
| | - Stuart White
- Nebraska Children and Families Foundation, Lincoln, Nebraska, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Institute for Policy Research, Northwestern University, Evanston, Illinois, USA
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13
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Liu X, Hu Y, Hao Y, Yang L. Individual differences in the neural architecture in semantic processing. Sci Rep 2024; 14:170. [PMID: 38168133 PMCID: PMC10761854 DOI: 10.1038/s41598-023-49538-8] [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: 05/24/2023] [Accepted: 12/09/2023] [Indexed: 01/05/2024] Open
Abstract
Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China.
| | - Yiwen Hu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Yaokun Hao
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
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14
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Mamah D, Mutiso V, Musyimi C, Harms MP, Anokhin AP, Chen S, Torous J, Muyela L, Nashed J, Al-Hosni Y, Odera A, Yarber A, Golosheykin S, Faghankhani M, Sneed M, Ndetei DM. Kenya Psychosis-Risk Outcomes Study (KePROS): Development of an Accelerated Medicine Partnership Schizophrenia-Aligned Project in Africa. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae009. [PMID: 39144113 PMCID: PMC11207935 DOI: 10.1093/schizbullopen/sgae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis The Accelerating Medicines Partnership Schizophrenia (AMP SCZ) funds a longitudinal study of 43 research sites across 5 continents to develop tools to stratify developmental trajectories of youth at clinical high risk for psychosis (CHR) and identify homogenous targets for future clinical trials. However, there are no sites in Africa, leaving a critical gap in our knowledge of clinical and biological outcomes among CHR individuals. Study Design We describe the development of the Kenya Psychosis-Risk Outcomes Study (KePROS), a 5-year NIH-funded project in Kenya designed to harmonize with AMP SCZ. The study will recruit over 100 CHR and 50 healthy participants and conduct multiple clinical and biomarker assessments over 2 years. Capacity building is a key component of the study, including the construction of an electroencephalography (EEG) laboratory and the upgrading of a local 3 T magnetic resonance imaging (MRI) machine. We detail community recruitment, study methodologies and protocols, and unique challenges with this pioneering research in Africa. Study Results This paper is descriptive only. Planned future analyses will investigate possible predictors of clinical outcomes and will be compared to results from other global populations. Conclusions KePROS will provide the research community with a rich longitudinal clinical and biomarker dataset from an African country in the developing Global South, which can be used alongside AMP SCZ data to delineate CHR outcome groups for future treatment development. Training in mental health assessment and investment in cutting-edge biomarker assessment and other technologies is needed to facilitate the inclusion of African countries in large-scale research consortia.
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Affiliation(s)
- Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Victoria Mutiso
- African Mental Health Research and Training Foundation, Nairobi, Kenya
| | - Christine Musyimi
- African Mental Health Research and Training Foundation, Nairobi, Kenya
| | - Michael P Harms
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - ShingShiun Chen
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center at Harvard Medical School, Boston, MA, USA
| | - Levi Muyela
- African Mental Health Research and Training Foundation, Nairobi, Kenya
| | - Jerome Nashed
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Yazen Al-Hosni
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Arthur Odera
- African Mental Health Research and Training Foundation, Nairobi, Kenya
| | - Alaina Yarber
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Semyon Golosheykin
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Masoomeh Faghankhani
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Megan Sneed
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - David M Ndetei
- African Mental Health Research and Training Foundation, Nairobi, Kenya
- Department of Psychiatry, University of Nairobi, Nairobi, Kenya
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15
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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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16
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Willbrand EH, Bunge SA, Weiner KS. Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci. J Cogn Neurosci 2023; 35:1846-1867. [PMID: 37677051 PMCID: PMC10586811 DOI: 10.1162/jocn_a_02049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.
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17
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D'Andrea CB, Laumann TO, Newbold DJ, Nelson SM, Nielsen AN, Chauvin R, Marek S, Greene DJ, Dosenbach NUF, Gordon EM. Substructure of the brain's Cingulo-Opercular network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561772. [PMID: 37873065 PMCID: PMC10592749 DOI: 10.1101/2023.10.10.561772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.
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Affiliation(s)
- Carolina Badke D'Andrea
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO 63310, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Dillan J Newbold
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota 55455, USA
| | - Ashley N Nielsen
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Roselyne Chauvin
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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18
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Rajesh A, Seider NA, Newbold DJ, Adeyemo B, Marek S, Greene DJ, Snyder AZ, Shimony JS, Laumann TO, Dosenbach NUF, Gordon EM. Structure-Function Coupling in Highly Sampled Individual Brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.04.560909. [PMID: 37873167 PMCID: PMC10592963 DOI: 10.1101/2023.10.04.560909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Structural connections (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to structural connections may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 minutes of diffusion-weighted MRI for SC and 360 minutes of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas. SIGNIFICANCE STATEMENT Structural connections between distant regions of the human brain support networked function that enables cognition and behavior. Improving our understanding of how structure enables function could allow better insight into how brain disconnection injuries impair brain function.Previous work using neuroimaging suggested that structure-function relationships vary systematically across the brain, with structure better explaining function in basic visual/motor areas than in higher-order areas. However, this work was conducted in group-averaged data, which may obscure details of individual-specific structure-function relationships.Using individual-specific densely sampled neuroimaging data, we found that in addition to visual/motor regions, structure strongly predicts function in specific circuits of the higher-order cingulate gyrus. The cingulate's structure-function relationship suggests that its organization may be unique among higher-order cortical regions.
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19
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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20
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Bijsterbosch JD, Farahibozorg SR, Glasser MF, Essen DV, Snyder LH, Woolrich MW, Smith SM. Evaluating functional brain organization in individuals and identifying contributions to network overlap. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558809. [PMID: 37790508 PMCID: PMC10542549 DOI: 10.1101/2023.09.21.558809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.
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Affiliation(s)
- Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri 63110, USA
| | | | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri 63110, USA
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri 63110, USA
| | - David Van Essen
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri 63110, USA
| | - Lawrence H Snyder
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri 63110, USA
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
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21
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Yang H, Yao X, Zhang H, Meng C, Biswal B. Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification. Brain Struct Funct 2023; 228:1755-1769. [PMID: 37572108 DOI: 10.1007/s00429-023-02689-w] [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: 04/04/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Xing Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, 607 Fenster Hall, Newark, NJ, 07102, USA.
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22
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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23
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Kwon Y, Salvo JJ, Anderson N, Holubecki AM, Lakshman M, Yoo K, Kay K, Gratton C, Braga RM. Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553585. [PMID: 37645962 PMCID: PMC10462098 DOI: 10.1101/2023.08.16.553585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.
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Affiliation(s)
- Y Kwon
- Northwestern University Department of Neurology
| | - J J Salvo
- Northwestern University Department of Neurology
| | - N Anderson
- Northwestern University Department of Neurology
| | | | - M Lakshman
- Northwestern University Department of Neurology
| | - K Yoo
- Yale University Department of Psychology
| | - K Kay
- University of Minnesota Department of Radiology
| | - C Gratton
- Florida State University Department of Psychology
| | - R M Braga
- Northwestern University Department of Neurology
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24
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Smith DM, Kraus BT, Dworetsky A, Gordon EM, Gratton C. Brain hubs defined in the group do not overlap with regions of high inter-individual variability. Neuroimage 2023; 277:120195. [PMID: 37286152 PMCID: PMC10427117 DOI: 10.1016/j.neuroimage.2023.120195] [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: 05/26/2022] [Revised: 04/18/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023] Open
Abstract
Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.
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Affiliation(s)
- Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, United States; Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
| | - Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, IL, United States
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, Evanston, IL, United States; Department of Psychology, Florida State University, Tallahassee, FL, United States
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, United States; Department of Psychology, Florida State University, Tallahassee, FL, United States; Department of Neurology, Northwestern University, Evanston, IL, United States.
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25
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Motzkin JC, Kanungo I, D’Esposito M, Shirvalkar P. Network targets for therapeutic brain stimulation: towards personalized therapy for pain. FRONTIERS IN PAIN RESEARCH 2023; 4:1156108. [PMID: 37363755 PMCID: PMC10286871 DOI: 10.3389/fpain.2023.1156108] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.
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Affiliation(s)
- Julian C. Motzkin
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
| | - Ishan Kanungo
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Mark D’Esposito
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Prasad Shirvalkar
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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26
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Mamah D. A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2023; 8:e230005. [PMID: 37427077 PMCID: PMC10327607 DOI: 10.20900/jpbs.20230005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.
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Affiliation(s)
- Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, 63110, USA
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27
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Wu D, Li X. Graph propagation network captures individual specificity of the relationship between functional and structural connectivity. Hum Brain Mapp 2023; 44:3885-3896. [PMID: 37186004 DOI: 10.1002/hbm.26320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Functional connectivity (FC) network characterizes the functional interactions between brain regions and is considered to root in the underlying structural connectivity (SC) network. If this is the case, individual variations in SC should cause corresponding individual variations in FC. However, divergences exist in the correspondence between direct SC and FC and researchers still cannot capture individual differences in FC via direct SC. As brain regions may interact through multi-hop indirect SC pathways, we conceived that one can capture the individual specific SC-FC relationship via incorporating indirect SC pathways appropriately. In this study, we designed graph propagation network (GPN) that models the information propagation between brain regions based on the SC network. Effects of interactions through multi-hop SC pathways naturally emerge from the multilayer information propagation in GPN. We predicted the individual differences in FC network based on SC network via multilayer GPN and results indicate that multilayer GPN incorporating effects of multi-hop indirect SCs greatly enhances the ability to predict individual FC network. Furthermore, the SC-FC relationship evaluated via the prediction accuracy is negatively correlated with the functional gradient, suggesting that the SC-FC relationship gradually uncouples along the functional hierarchy spanning from unimodal to transmodal cortex. We also revealed important intermediate brain regions along multi-hop SC pathways involving in the individual SC-FC relationship. These results suggest that multilayer GPN can serve as a method to establish individual SC-FC relationship at the macroneuroimaging level.
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Affiliation(s)
- Dongya Wu
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Xin Li
- School of Mathematics, Northwest University, Xi'an, China
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28
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Siddiqi SH, Kandala S, Hacker CD, Trapp NT, Leuthardt EC, Carter AR, Brody DL. Individualized precision targeting of dorsal attention and default mode networks with rTMS in traumatic brain injury-associated depression. Sci Rep 2023; 13:4052. [PMID: 36906616 PMCID: PMC10008633 DOI: 10.1038/s41598-022-21905-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 10/05/2022] [Indexed: 03/13/2023] Open
Abstract
At the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. Thus, we sought to identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. We used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These "RSNM targets" were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region ("sgACC-derived targets"). The TBI-D cohort was also randomized to receive active (n = 9) or sham (n = 4) rTMS to RSNM targets with 20 daily sessions of sequential high-frequency left-sided stimulation and low-frequency right-sided stimulation. We found that the group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were thus identified based on DAN anti-correlation and DMN correlation. These RSNM targets showed greater test-retest reliability than sgACC-derived targets. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was also stronger and more reliable for RSNM-derived targets than for sgACC-derived targets. Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment also led to increased connectivity within and between the stimulation sites, the sgACC, and the DMN. Overall, these results suggest that RSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes.
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Affiliation(s)
- Shan H Siddiqi
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA. .,Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA. .,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD, 20814, USA.
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Carl D Hacker
- Department of Neurosurgery, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Nicholas T Trapp
- Department of Psychiatry, University of Iowa Carver College of Medicine, 500 Newton Rd, Iowa City, IA, 52246, USA
| | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - Alexandre R Carter
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD, 20814, USA.,Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, 63110, USA
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29
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Porter A, Nielsen A, Dorn M, Dworetsky A, Edmonds D, Gratton C. Masked features of task states found in individual brain networks. Cereb Cortex 2023; 33:2879-2900. [PMID: 35802477 PMCID: PMC10016040 DOI: 10.1093/cercor/bhac247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ashley Nielsen
- Department of Neurology, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States
| | - Megan Dorn
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Donnisa Edmonds
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
- Department of Neurology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
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30
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Foster BL, Koslov SR, Aponik-Gremillion L, Monko ME, Hayden BY, Heilbronner SR. A tripartite view of the posterior cingulate cortex. Nat Rev Neurosci 2023; 24:173-189. [PMID: 36456807 PMCID: PMC10041987 DOI: 10.1038/s41583-022-00661-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/03/2022]
Abstract
The posterior cingulate cortex (PCC) is one of the least understood regions of the cerebral cortex. By contrast, the anterior cingulate cortex has been the subject of intensive investigation in humans and model animal systems, leading to detailed behavioural and computational theoretical accounts of its function. The time is right for similar progress to be made in the PCC given its unique anatomical and physiological properties and demonstrably important contributions to higher cognitive functions and brain diseases. Here, we describe recent progress in understanding the PCC, with a focus on convergent findings across species and techniques that lay a foundation for establishing a formal theoretical account of its functions. Based on this converging evidence, we propose that the broader PCC region contains three major subregions - the dorsal PCC, ventral PCC and retrosplenial cortex - that respectively support the integration of executive, mnemonic and spatial processing systems. This tripartite subregional view reconciles inconsistencies in prior unitary theories of PCC function and offers promising new avenues for progress.
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Affiliation(s)
- Brett L Foster
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Seth R Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lyndsey Aponik-Gremillion
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA.,Department of Health Sciences, Dumke College for Health Professionals, Weber State University, Ogden, UT, USA
| | - Megan E Monko
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.,Center for Magnetic Resonance Research and Center for Neural Engineering, University of Minnesota, Minneapolis, MN, USA
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31
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Bai L, Yin B, Lei S, Li T, Wang S, Pan Y, Gan S, Jia X, Li X, Xiong F, Yan Z, Bai G. Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury. J Neurotrauma 2023; 40:63-73. [PMID: 35747994 DOI: 10.1089/neu.2021.0450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.
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Affiliation(s)
- Lijun Bai
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Bo Yin
- Department of Neurosurgery, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuoyan Lei
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China
| | - Tianhui Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Yizhen Pan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Shuoqiu Gan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Feng Xiong
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guanghui Bai
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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32
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A Holistic Analysis of Individual Brain Activity Revealed the Relationship of Brain Areal Activity with the Entire Brain's Activity. Brain Sci 2022; 13:brainsci13010006. [PMID: 36671988 PMCID: PMC9855953 DOI: 10.3390/brainsci13010006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The relationship between brain areal activity and the entire brain's activity is unknown, and understanding this relationship is imperative for understanding the neural mechanisms of human brain function at systems level. The complex activity of human brains varies from area to area and from time to time across the whole brain. BOLD-fMRI measures this spatiotemporal activity at a large-scale systems level. The BOLD time signal of an area reflects a collective neuronal activity of over one million neurons under that area, and the temporal correlation of this time signal with that of every point in the brain yields a full spatial map that characterizes the entire brain's functional co-activity (FC) relative to that area's activity. Here we show a quantitative relationship between brain areal activity and the activity of the entire brain. The temporal correlation coefficient r of the signal time courses of two areas quantifies the degree of co-activity between the two areas, and the spatial correlation coefficient R of their corresponding two FC maps quantifies the co-activity between these two maps. We found that a modified sigmoid function quantified this R with r, i.e., Rr=1+ra-1-ra1+ra+1-ra, revealing a relationship between the activity of brain areas and that of the entire brain. The parameter a in this equation was found to be associated with the mean degree of the temporal co-activity among all brain areas, and its value was brain functional state dependent too. Our study demonstrated a novel approach for analyzing fMRI data to holistically characterize the entire brain's activity quantitatively for any brain functional state in individual humans.
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33
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Setton R, Mwilambwe-Tshilobo L, Girn M, Lockrow AW, Baracchini G, Hughes C, Lowe AJ, Cassidy BN, Li J, Luh WM, Bzdok D, Leahy RM, Ge T, Margulies DS, Misic B, Bernhardt BC, Stevens WD, De Brigard F, Kundu P, Turner GR, Spreng RN. Age differences in the functional architecture of the human brain. Cereb Cortex 2022; 33:114-134. [PMID: 35231927 PMCID: PMC9758585 DOI: 10.1093/cercor/bhac056] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/04/2022] [Accepted: 01/26/2022] [Indexed: 11/12/2022] Open
Abstract
The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience-dependent changes, or both, is uncertain. We employed a multimethod strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n = 181) and older (n = 120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multiscale, multimethod approaches to characterize the architecture of functional brain aging.
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Affiliation(s)
- Roni Setton
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Amber W Lockrow
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Giulia Baracchini
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Colleen Hughes
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | | | - Jian Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Wen-Ming Luh
- National Institutes of Health, National Institute on Aging, Baltimore, MD, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- School of Computer Science, McGill University, Montreal, QC, Canada
- Mila – Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Richard M Leahy
- Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
| | - Bratislav Misic
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - W Dale Stevens
- Department of Psychology, York University, Toronto, ON, Canada
| | - Felipe De Brigard
- Department of Philosophy, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Durham, NC, USA
| | - Prantik Kundu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gary R Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
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34
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Lynch CJ, Elbau IG, Ng TH, Wolk D, Zhu S, Ayaz A, Power JD, Zebley B, Gunning FM, Liston C. Automated optimization of TMS coil placement for personalized functional network engagement. Neuron 2022; 110:3263-3277.e4. [PMID: 36113473 DOI: 10.1016/j.neuron.2022.08.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/20/2022] [Accepted: 08/05/2022] [Indexed: 12/11/2022]
Abstract
Transcranial magnetic stimulation (TMS) is used to treat multiple psychiatric and neurological conditions by manipulating activity in particular brain networks and circuits, but individual responses are highly variable. In clinical settings, TMS coil placement is typically based on either group average functional maps or scalp heuristics. Here, we found that this approach can inadvertently target different functional networks in depressed patients due to variability in their functional brain organization. More precise TMS targeting should be feasible by accounting for each patient's unique functional neuroanatomy. To this end, we developed a targeting approach, termed targeted functional network stimulation (TANS). The TANS approach improved stimulation specificity in silico in 8 highly sampled patients with depression and 6 healthy individuals and in vivo when targeting somatomotor functional networks representing the upper and lower limbs. Code for implementing TANS and an example dataset are provided as a resource.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA.
| | - Immanuel G Elbau
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Tommy H Ng
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Danielle Wolk
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Shasha Zhu
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Aliza Ayaz
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Benjamin Zebley
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, 413 East 69th Street, Box 204, New York, NY 10021, USA.
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35
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Aponik-Gremillion L, Chen YY, Bartoli E, Koslov SR, Rey HG, Weiner KS, Yoshor D, Hayden BY, Sheth SA, Foster BL. Distinct population and single-neuron selectivity for executive and episodic processing in human dorsal posterior cingulate. eLife 2022; 11:e80722. [PMID: 36169132 PMCID: PMC9519147 DOI: 10.7554/elife.80722] [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: 06/01/2022] [Accepted: 08/22/2022] [Indexed: 12/02/2022] Open
Abstract
Posterior cingulate cortex (PCC) is an enigmatic region implicated in psychiatric and neurological disease, yet its role in cognition remains unclear. Human studies link PCC to episodic memory and default mode network (DMN), while findings from the non-human primate emphasize executive processes more associated with the cognitive control network (CCN) in humans. We hypothesized this difference reflects an important functional division between dorsal (executive) and ventral (episodic) PCC. To test this, we utilized human intracranial recordings of population and single unit activity targeting dorsal PCC during an alternated executive/episodic processing task. Dorsal PCC population responses were significantly enhanced for executive, compared to episodic, task conditions, consistent with the CCN. Single unit recordings, however, revealed four distinct functional types with unique executive (CCN) or episodic (DMN) response profiles. Our findings provide critical electrophysiological data from human PCC, bridging incongruent views within and across species, furthering our understanding of PCC function.
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Affiliation(s)
- Lyndsey Aponik-Gremillion
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Department of Health Sciences, Dumke College for Health Professionals, Weber State UniversityOgdenUnited States
- Department of Neurosurgery, Baylor College of MedicineHoustonUnited States
| | - Yvonne Y Chen
- Department of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of MedicineHoustonUnited States
| | - Seth R Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hernan G Rey
- Department of Neurosurgery, Baylor College of MedicineHoustonUnited States
- Department of Neurosurgery, Medical College of WisconsinMilwaukeeUnited States
- Joint Department of Biomedical Engineering, Medical College of WisconsinMilwaukeeUnited States
| | - Kevin S Weiner
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
| | - Daniel Yoshor
- Department of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Benjamin Y Hayden
- Department of Neuroscience, University of MinnesotaMinneapolisUnited States
- Center for Magnetic Resonance Research, University of MinnesotaMinneapolisUnited States
- Center for Neural Engineering, University of MinnesotaMinneapolisUnited States
| | - Sameer A Sheth
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Department of Neurosurgery, Baylor College of MedicineHoustonUnited States
| | - Brett L Foster
- Department of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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36
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Willbrand EH, Parker BJ, Voorhies WI, Miller JA, Lyu I, Hallock T, Aponik-Gremillion L, Koslov SR, Bunge SA, Foster BL, Weiner KS. Uncovering a tripartite landmark in posterior cingulate cortex. SCIENCE ADVANCES 2022; 8:eabn9516. [PMID: 36070384 PMCID: PMC9451146 DOI: 10.1126/sciadv.abn9516] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/21/2022] [Indexed: 05/18/2023]
Abstract
Understanding brain structure-function relationships, and their development and evolution, is central to neuroscience research. Here, we show that morphological differences in posterior cingulate cortex (PCC), a hub of functional brain networks, predict individual differences in macroanatomical, microstructural, and functional features of PCC. Manually labeling 4511 sulci in 572 hemispheres, we found a shallow cortical indentation (termed the inframarginal sulcus; ifrms) within PCC that is absent from neuroanatomical atlases yet colocalized with a focal, functional region of the lateral frontoparietal network implicated in cognitive control. This structural-functional coupling generalized to meta-analyses consisting of hundreds of studies and thousands of participants. Additional morphological analyses showed that unique properties of the ifrms differ across the life span and between hominoid species. These findings support a classic theory that shallow, tertiary sulci serve as landmarks in association cortices. They also beg the question: How many other cortical indentations have we missed?
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Affiliation(s)
- Ethan H. Willbrand
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
| | - Benjamin J. Parker
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
| | - Willa I. Voorhies
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
| | - Jacob A. Miller
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Tyler Hallock
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
| | | | - Seth R. Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Silvia A. Bunge
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
| | - Brett L. Foster
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kevin S. Weiner
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720 USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720 USA
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37
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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38
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Alvand A, Kuruvilla-Mathew A, Kirk IJ, Roberts RP, Pedersen M, Purdy SC. Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study. Neuroimage Clin 2022; 35:103139. [PMID: 36002970 PMCID: PMC9421544 DOI: 10.1016/j.nicl.2022.103139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022]
Abstract
Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties -i.e., an average of all brain regions- and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level -i.e., single-brain regions-, we observed decreased participation coefficient (PC - a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.
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Affiliation(s)
- Ashkan Alvand
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Abin Kuruvilla-Mathew
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Ian J Kirk
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Mangor Pedersen
- School of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
| | - Suzanne C Purdy
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
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39
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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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40
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Gordon EM, Laumann TO, Marek S, Newbold DJ, Hampton JM, Seider NA, Montez DF, Nielsen AM, Van AN, Zheng A, Miller R, Siegel JS, Kay BP, Snyder AZ, Greene DJ, Schlaggar BL, Petersen SE, Nelson SM, Dosenbach NUF. Individualized Functional Subnetworks Connect Human Striatum and Frontal Cortex. Cereb Cortex 2022; 32:2868-2884. [PMID: 34718460 PMCID: PMC9247416 DOI: 10.1093/cercor/bhab387] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/14/2022] Open
Abstract
The striatum and cerebral cortex are interconnected via multiple recurrent loops that play a major role in many neuropsychiatric conditions. Primate corticostriatal connections can be precisely mapped using invasive tract-tracing. However, noninvasive human research has not mapped these connections with anatomical precision, limited in part by the practice of averaging neuroimaging data across individuals. Here we utilized highly sampled resting-state functional connectivity MRI for individual-specific precision functional mapping (PFM) of corticostriatal connections. We identified ten individual-specific subnetworks linking cortex-predominately frontal cortex-to striatum, most of which converged with nonhuman primate tract-tracing work. These included separable connections between nucleus accumbens core/shell and orbitofrontal/medial frontal gyrus; between anterior striatum and dorsomedial prefrontal cortex; between dorsal caudate and lateral prefrontal cortex; and between middle/posterior putamen and supplementary motor/primary motor cortex. Two subnetworks that did not converge with nonhuman primates were connected to cortical regions associated with human language function. Thus, precision subnetworks identify detailed, individual-specific, neurobiologically plausible corticostriatal connectivity that includes human-specific language networks.
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Affiliation(s)
- Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ashley M Nielsen
- Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Andrew N Van
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychological & Brain Sciences, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55454, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
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41
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Siddiqi SH, Kording KP, Parvizi J, Fox MD. Causal mapping of human brain function. Nat Rev Neurosci 2022; 23:361-375. [PMID: 35444305 PMCID: PMC9387758 DOI: 10.1038/s41583-022-00583-8] [Citation(s) in RCA: 108] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/14/2022] [Indexed: 12/11/2022]
Abstract
Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.
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Affiliation(s)
- Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Konrad P Kording
- Department of Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Josef Parvizi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
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42
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Jones JS, Astle DE. Segregation and integration of the functional connectome in neurodevelopmentally 'at risk' children. Dev Sci 2022; 25:e13209. [PMID: 34873798 PMCID: PMC7613070 DOI: 10.1111/desc.13209] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and is thought to support high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive development? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally 'at-risk' children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks alongside resting-state functional magnetic resonance imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.
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Affiliation(s)
- Jonathan S Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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43
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Rivière D, Leprince Y, Labra N, Vindas N, Foubet O, Cagna B, Loh KK, Hopkins W, Balzeau A, Mancip M, Lebenberg J, Cointepas Y, Coulon O, Mangin JF. Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy. Front Neuroinform 2022; 16:803934. [PMID: 35311005 PMCID: PMC8928460 DOI: 10.3389/fninf.2022.803934] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.
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Affiliation(s)
- Denis Rivière
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Nicole Labra
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
| | - Nabil Vindas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Ophélie Foubet
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Bastien Cagna
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Kep Kee Loh
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - William Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
| | - Antoine Balzeau
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
| | - Martial Mancip
- Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Yann Cointepas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Olivier Coulon
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
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Hollunder B, Rajamani N, Siddiqi SH, Finke C, Kühn AA, Mayberg HS, Fox MD, Neudorfer C, Horn A. Toward personalized medicine in connectomic deep brain stimulation. Prog Neurobiol 2022; 210:102211. [PMID: 34958874 DOI: 10.1016/j.pneurobio.2021.102211] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/15/2021] [Accepted: 12/22/2021] [Indexed: 02/08/2023]
Abstract
At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.
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Affiliation(s)
- Barbara Hollunder
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Nanditha Rajamani
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Carsten Finke
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA
| | - Clemens Neudorfer
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andreas Horn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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45
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Gupta S, Lim M, Rajapakse JC. Decoding task specific and task general functional architectures of the brain. Hum Brain Mapp 2022; 43:2801-2816. [PMID: 35224817 PMCID: PMC9120557 DOI: 10.1002/hbm.25817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/01/2022] [Accepted: 02/13/2022] [Indexed: 11/06/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference‐based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Marcus Lim
- School of Computer Science and Engineering Nanyang Technological University Singapore
| | - Jagath C. Rajapakse
- School of Computer Science and Engineering Nanyang Technological University Singapore
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46
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Fallahi A, Pooyan M, Habibabadi JM, Hashemi-Fesharaki SS, Tabatabaei NH, Ay M, Nazem-Zadeh MR. A novel approach for extracting functional brain networks involved in mesial temporal lobe epilepsy based on self organizing maps. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Ward HB, Beermann A, Nawaz U, Halko MA, Janes AC, Moran LV, Brady RO. Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence. Front Psychiatry 2022; 13:804055. [PMID: 35153877 PMCID: PMC8829345 DOI: 10.3389/fpsyt.2022.804055] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/03/2022] [Indexed: 12/30/2022] Open
Abstract
Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = -0.50; 95% CI -0.75 to -0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.
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Affiliation(s)
- Heather Burrell Ward
- Beth Israel Deaconess Medical Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Adam Beermann
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Uzma Nawaz
- Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Mark A Halko
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Amy C Janes
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Lauren V Moran
- Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
| | - Roscoe O Brady
- Beth Israel Deaconess Medical Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,McLean Hospital, Belmont, MA, United States
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48
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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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Phipps CJ, Murman DL, Warren DE. Stimulating Memory: Reviewing Interventions Using Repetitive Transcranial Magnetic Stimulation to Enhance or Restore Memory Abilities. Brain Sci 2021; 11:1283. [PMID: 34679348 PMCID: PMC8533697 DOI: 10.3390/brainsci11101283] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/24/2021] [Accepted: 09/25/2021] [Indexed: 12/18/2022] Open
Abstract
Human memory systems are imperfect recording devices that are affected by age and disease, but recent findings suggest that the functionality of these systems may be modifiable through interventions using non-invasive brain stimulation such as repetitive transcranial magnetic stimulation (rTMS). The translational potential of these rTMS interventions is clear: memory problems are the most common cognitive complaint associated with healthy aging, while pathological conditions such as Alzheimer's disease are often associated with severe deficits in memory. Therapies to improve memory or treat memory loss could enhance independence while reducing costs for public health systems. Despite this promise, several important factors limit the generalizability and translational potential of rTMS interventions for memory. Heterogeneity of protocol design, rTMS parameters, and outcome measures present significant challenges to interpretation and reproducibility. However, recent advances in cognitive neuroscience, including rTMS approaches and recent insights regarding functional brain networks, may offer methodological tools necessary to design new interventional studies with enhanced experimental rigor, improved reproducibility, and greater likelihood of successful translation to clinical settings. In this review, we first discuss the current state of the literature on memory modulation with rTMS, then offer a commentary on developments in cognitive neuroscience that are relevant to rTMS interventions, and finally close by offering several recommendations for the design of future investigations using rTMS to modulate human memory performance.
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Affiliation(s)
| | | | - David E. Warren
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE 68198, USA; (C.J.P.); (D.L.M.)
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50
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Perino MT, Myers MJ, Wheelock MD, Yu Q, Harper JC, Manhart MF, Gordon EM, Eggebrecht AT, Pine DS, Barch DM, Luby JL, Sylvester CM. Whole-Brain Resting-State Functional Connectivity Patterns Associated With Pediatric Anxiety and Involuntary Attention Capture. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:229-238. [PMID: 36033105 PMCID: PMC9417088 DOI: 10.1016/j.bpsgos.2021.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/22/2021] [Accepted: 05/24/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Pediatric anxiety disorders are linked to dysfunction in multiple functional brain networks, as well as to alterations in the allocation of spatial attention. We used network-level analyses to characterize resting-state functional connectivity (rs-fc) alterations associated with 1) symptoms of anxiety and 2) alterations in stimulus-driven attention associated with pediatric anxiety disorders. We hypothesized that anxiety was related to altered connectivity of the frontoparietal, default mode, cingulo-opercular, and ventral attention networks and that anxiety-related connectivity alterations that include the ventral attention network would simultaneously be related to deviations in stimulus-driven attention. METHODS A sample of children (n = 61; mean = 10.6 years of age), approximately half of whom met criteria for a current anxiety disorder, completed a clinical assay, an attention task, and rs-fc magnetic resonance imaging scans. Network-level analyses examined whole-brain rs-fc patterns associated with clinician-rated anxiety and with involuntary capture of attention. Post hoc analyses controlled for comorbid symptoms. RESULTS Elevated clinician-rated anxiety was associated with altered connectivity within the cingulo-opercular network, as well as between the cingulo-opercular network and the ventral attention, default mode, and visual networks. Connectivity between the ventral attention and cingulo-opercular networks was associated with variation in both anxiety and stimulus-driven attention. CONCLUSIONS Pediatric anxiety is related to aberrant connectivity patterns among several networks, most of which include the cingulo-opercular network. These results help clarify the within- and between-network interactions associated with pediatric anxiety and its association with altered attention, suggesting that specific network connections could be targeted to improve specific altered processes associated with anxiety.
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Affiliation(s)
- Michael T. Perino
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Michael J. Myers
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Muriah D. Wheelock
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Qiongru Yu
- Department of Psychology, San Diego State University, San Diego, California
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Jennifer C. Harper
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Megan F. Manhart
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Evan M. Gordon
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Adam T. Eggebrecht
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Daniel S. Pine
- Development & Emotion Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Deanna M. Barch
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Joan L. Luby
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Chad M. Sylvester
- School of Medicine, Washington University in St. Louis, St. Louis, Missouri
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