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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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2
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Hoang N, Sardaripour N, Ramey GD, Schilling K, Liao E, Chen Y, Park JH, Bledsoe X, Landman BA, Gamazon ER, Benton ML, Capra JA, Rubinov M. Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale. PLoS Biol 2024; 22:e3002782. [PMID: 39269986 DOI: 10.1371/journal.pbio.3002782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 08/01/2024] [Indexed: 09/15/2024] Open
Abstract
An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.
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Affiliation(s)
- Nhung Hoang
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Neda Sardaripour
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Grace D Ramey
- Biological and Medical Informatics Division, University of California, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
| | - Kurt Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Emily Liao
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Yiting Chen
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jee Hyun Park
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Xavier Bledsoe
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mary Lauren Benton
- Department of Computer Science, Baylor University, Waco, Texas, United States of America
| | - John A Capra
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California, United States of America
| | - Mikail Rubinov
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America
- Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, United States of America
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Oishi H, Berezovskii VK, Livingstone MS, Weiner KS, Arcaro MJ. Inferotemporal face patches are histo-architectonically distinct. Cell Rep 2024; 43:114732. [PMID: 39269905 DOI: 10.1016/j.celrep.2024.114732] [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: 06/07/2024] [Revised: 08/05/2024] [Accepted: 08/23/2024] [Indexed: 09/15/2024] Open
Abstract
An interconnected group of cortical regions distributed across the primate inferotemporal cortex forms a network critical for face perception. Understanding the microarchitecture of this face network can refine mechanistic accounts of how individual areas function and interact to support visual perception. To address this, we acquire a unique dataset in macaque monkeys combining fMRI to localize face patches in vivo and then ex vivo histology to resolve their histo-architecture across cortical depths in the same individuals. Our findings reveal that face patches differ based on cytochrome oxidase (CO) and, to a lesser extent, myelin staining, with the middle lateral (ML) face patch exhibiting pronounced CO staining. Histo-architectonic differences are less pronounced when using probabilistic definitions of face patches, underscoring the importance of precision mapping integrating in vivo and ex vivo measurements in the same individuals. This study indicates that the macaque face patch network is composed of architectonically distinct components.
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Affiliation(s)
- Hiroki Oishi
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
| | | | | | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA; Department of Neuroscience, University of California, Berkeley, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Michael J Arcaro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
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Calabro FJ, Parr AC, Sydnor VJ, Hetherington H, Prasad KM, Ibrahim TS, Sarpal DK, Famalette A, Verma P, Luna B. Leveraging ultra-high field (7T) MRI in psychiatric research. Neuropsychopharmacology 2024:10.1038/s41386-024-01980-6. [PMID: 39251774 DOI: 10.1038/s41386-024-01980-6] [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: 04/01/2024] [Revised: 06/21/2024] [Accepted: 07/23/2024] [Indexed: 09/11/2024]
Abstract
Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.
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Affiliation(s)
- Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Valerie J Sydnor
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Tamer S Ibrahim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alyssa Famalette
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Piya Verma
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
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5
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Etkin A, Mathalon DH. Bringing Imaging Biomarkers Into Clinical Reality in Psychiatry. JAMA Psychiatry 2024:2822966. [PMID: 39230917 DOI: 10.1001/jamapsychiatry.2024.2553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Importance Advancing precision psychiatry, where treatments are based on an individual's biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined. Observations Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts. Conclusions and Relevance EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.
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Affiliation(s)
- Amit Etkin
- Alto Neuroscience Inc, Los Altos, California
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
- Veterans Affairs San Francisco Health Care System, San Francisco, California
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Lynch CJ, Elbau IG, Ng T, Ayaz A, Zhu S, Wolk D, Manfredi N, Johnson M, Chang M, Chou J, Summerville I, Ho C, Lueckel M, Bukhari H, Buchanan D, Victoria LW, Solomonov N, Goldwaser E, Moia S, Caballero-Gaudes C, Downar J, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Kay K, Aloysi A, Gordon EM, Bhati MT, Williams N, Power JD, Zebley B, Grosenick L, Gunning FM, Liston C. Frontostriatal salience network expansion in individuals in depression. Nature 2024:10.1038/s41586-024-07805-2. [PMID: 39232159 DOI: 10.1038/s41586-024-07805-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 07/09/2024] [Indexed: 09/06/2024]
Abstract
Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
| | - Immanuel G Elbau
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Tommy Ng
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Aliza Ayaz
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Shasha Zhu
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Danielle Wolk
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Nicola Manfredi
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Megan Johnson
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Megan Chang
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Jolin Chou
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | | | - Claire Ho
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Maximilian Lueckel
- Leibniz Institute for Resilience Research, Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neurosciences (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Hussain Bukhari
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Derrick Buchanan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | | | - Nili Solomonov
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Eric Goldwaser
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Stefano Moia
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Basque Center on Cognition, Brain and Language, Donostia, Spain
| | | | - Jonathan Downar
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Daniel M Blumberger
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Therapeutic Brain Intervention, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Amy Aloysi
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Mahendra T Bhati
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Nolan Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin Zebley
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Logan Grosenick
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
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Petersen SE, Seitzman BA, Nelson SM, Wig GS, Gordon EM. Principles of cortical areas and their implications for neuroimaging. Neuron 2024; 112:2837-2853. [PMID: 38834069 DOI: 10.1016/j.neuron.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.
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Affiliation(s)
- Steven E Petersen
- Mallinckrodt Institute 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 Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin A Seitzman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gagan S Wig
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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Ekhtiari H, Sangchooli A, Carmichael O, Moeller FG, O'Donnell P, Oquendo M, Paulus MP, Pizzagalli DA, Ramey T, Schacht J, Zare-Bidoky M, Childress AR, Brady K. Neuroimaging Biomarkers in Addiction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.02.24312084. [PMID: 39281741 PMCID: PMC11398440 DOI: 10.1101/2024.09.02.24312084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.
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Affiliation(s)
- Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Arshiya Sangchooli
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Owen Carmichael
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - F Gerard Moeller
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Patricio O'Donnell
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Maria Oquendo
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Martin P Paulus
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Diego A Pizzagalli
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Tatiana Ramey
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Joseph Schacht
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Mehran Zare-Bidoky
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Anna Rose Childress
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Kathleen Brady
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
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9
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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. Cereb Cortex 2024; 34:bhae361. [PMID: 39277800 DOI: 10.1093/cercor/bhae361] [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: 03/06/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 09/17/2024] Open
Abstract
Structural connectivity (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 SC 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 min of diffusion-weighted MRI for SC and 360 min 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.
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Affiliation(s)
- Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
| | - Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Dillan J Newbold
- Department of Neurology, New York Langone Medical Center, 550 First Avenue, New York, NY, 10016, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92037, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neurology, New York Langone Medical Center, 550 First Avenue, New York, NY, 10016, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neuroscience, Washington University, 660 S. Euclid Ave.St. Louis, MO 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, 660 S. Euclid Ave.St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO 63130, USA
- Program in Occupational Therapy, Washington University, 4444 Forest Park Ave, St. Louis, MO 63108, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, MO 63110, USA
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10
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Leve LD, Kanamori M, Humphreys KL, Jaffee SR, Nusslock R, Oro V, Hyde LW. The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024:10.1007/s11121-024-01720-8. [PMID: 39225944 DOI: 10.1007/s11121-024-01720-8] [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] [Accepted: 08/11/2024] [Indexed: 09/04/2024]
Abstract
Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.
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Affiliation(s)
- Leslie D Leve
- Prevention Science Institute, University of Oregon, Eugene, USA.
- Department of Counseling Psychology and Human Services, University of Oregon, Eugene, USA.
- Cambridge Public Health, University of Cambridge, Cambridge, UK.
| | - Mariano Kanamori
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, USA
| | - Sara R Jaffee
- Department of Psychology, University of Pennsylvania, Philadelphia, USA
| | - Robin Nusslock
- Department of Psychology & Institute for Policy Research, Northwestern University, Evanston, USA
| | - Veronica Oro
- Prevention Science Institute, University of Oregon, Eugene, USA
| | - Luke W Hyde
- Department of Psychology & Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, USA
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11
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Blanchett R, Chen H, Vlasova RM, Cornea E, Maza M, Davenport M, Reinhartsen D, DeRamus M, Edmondson Pretzel R, Gilmore JH, Hooper SR, Styner MA, Gao W, Knickmeyer RC. White matter microstructure and functional connectivity in the brains of infants with Turner syndrome. Cereb Cortex 2024; 34:bhae351. [PMID: 39256896 PMCID: PMC11387115 DOI: 10.1093/cercor/bhae351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/01/2024] [Accepted: 08/13/2024] [Indexed: 09/12/2024] Open
Abstract
Turner syndrome, caused by complete or partial loss of an X-chromosome, is often accompanied by specific cognitive challenges. Magnetic resonance imaging studies of adults and children with Turner syndrome suggest these deficits reflect differences in anatomical and functional connectivity. However, no imaging studies have explored connectivity in infants with Turner syndrome. Consequently, it is unclear when in development connectivity differences emerge. To address this gap, we compared functional connectivity and white matter microstructure of 1-year-old infants with Turner syndrome to typically developing 1-year-old boys and girls. We examined functional connectivity between the right precentral gyrus and five regions that show reduced volume in 1-year old infants with Turner syndrome compared to controls and found no differences. However, exploratory analyses suggested infants with Turner syndrome have altered connectivity between right supramarginal gyrus and left insula and right putamen. To assess anatomical connectivity, we examined diffusivity indices along the superior longitudinal fasciculus and found no differences. However, an exploratory analysis of 46 additional white matter tracts revealed significant group differences in nine tracts. Results suggest that the first year of life is a window in which interventions might prevent connectivity differences observed at later ages, and by extension, some of the cognitive challenges associated with Turner syndrome.
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Affiliation(s)
- Reid Blanchett
- Genetics and Genome Sciences, Michigan State University, Biomedical & Physical Sciences, Room 2165, East Lansing, MI 48824, United States
- Department of Epigenetics, Van Andel Research Institute, 33 Bostwick Ave NE, Grand Rapids, MI 49503, United States
| | - Haitao Chen
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, 8700 Beverly Blvd, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Roza M Vlasova
- Department of Psychiatry, 333 S. Columbia Street, Suite 304 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
| | - Emil Cornea
- Department of Psychiatry, 333 S. Columbia Street, Suite 304 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
| | - Maria Maza
- Department of Psychology and Neuroscience, Campus Box #3270, 235 E. Cameron Avenue, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Marsha Davenport
- Department of Pediatrics, 333 South Columbia Street, Suite 260 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Debra Reinhartsen
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, 101 Renee Lynn Ct, Carrboro, NC 27510, United States
| | - Margaret DeRamus
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, 101 Renee Lynn Ct, Carrboro, NC 27510, United States
| | - Rebecca Edmondson Pretzel
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, 101 Renee Lynn Ct, Carrboro, NC 27510, United States
| | - John H Gilmore
- Department of Psychiatry, 333 S. Columbia Street, Suite 304 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
| | - Stephen R Hooper
- Department of Psychiatry, 333 S. Columbia Street, Suite 304 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
- Department of Health Sciences, Bondurant Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Martin A Styner
- Department of Psychiatry, 333 S. Columbia Street, Suite 304 MacNider Hall, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
- Department of Computer Science, Campus Box 3175, Brooks Computer Science Building, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, 8700 Beverly Blvd, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Life Sciences Bldg. 1355 Bogue, #B240B, Michigan State University, East Lansing, MI 48824, United States
- Institute for Quantitative Health Sciences and Engineering, Room 2114, 775 Woodlot Dr., East Lansing, MI 48824, United States
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12
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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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13
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Marek S, Laumann TO. Replicability and generalizability in population psychiatric neuroimaging. Neuropsychopharmacology 2024:10.1038/s41386-024-01960-w. [PMID: 39215207 DOI: 10.1038/s41386-024-01960-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Studies linking mental health with brain function in cross-sectional population-based association studies have historically relied on small, underpowered samples. Given the small effect sizes typical of such brain-wide associations, studies require samples into the thousands to achieve the statistical power necessary for replicability. Here, we detail how small sample sizes have hampered replicability and provide sample size targets given established association strength benchmarks. Critically, while replicability will improve with larger samples, it is not guaranteed that observed effects will meaningfully apply to target populations of interest (i.e., be generalizable). We discuss important considerations related to generalizability in psychiatric neuroimaging and provide an example of generalizability failure due to "shortcut learning" in brain-based predictions of mental health phenotypes. Shortcut learning is a phenomenon whereby machine learning models learn an association between the brain and an unmeasured construct (the shortcut), rather than the intended target of mental health. Given the complex nature of brain-behavior interactions, the future of epidemiological approaches to brain-based studies of mental health will require large, diverse samples with comprehensive assessment.
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Affiliation(s)
- Scott Marek
- Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Neuroimaging Labs Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- AI Institute for Health, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
- Neuroimaging Labs Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
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14
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Dong HM, Zhang XH, Labache L, Zhang S, Ooi LQR, Yeo BTT, Margulies DS, Holmes AJ, Zuo XN. Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood. Nat Neurosci 2024:10.1038/s41593-024-01736-x. [PMID: 39179884 DOI: 10.1038/s41593-024-01736-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/18/2024] [Indexed: 08/26/2024]
Abstract
The human brain experiences functional changes through childhood and adolescence, shifting from an organizational framework anchored within sensorimotor and visual regions into one that is balanced through interactions with later-maturing aspects of association cortex. Here, we link this profile of functional reorganization to the development of ventral attention network connectivity across independent datasets. We demonstrate that maturational changes in cortical organization link preferentially to within-network connectivity and heightened degree centrality in the ventral attention network, whereas connectivity within network-linked vertices predicts cognitive ability. This connectivity is associated closely with maturational refinement of cortical organization. Children with low ventral attention network connectivity exhibit adolescent-like topographical profiles, suggesting that attentional systems may be relevant in understanding how brain functions are refined across development. These data suggest a role for attention networks in supporting age-dependent shifts in cortical organization and cognition across childhood and adolescence.
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Affiliation(s)
- Hao-Ming Dong
- Department of Psychology, Yale University, New Haven, CT, USA.
| | - Xi-Han Zhang
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Loïc Labache
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Shaoshi Zhang
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- National Basic Science Data Center, Beijing, China.
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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15
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Sun W, Billot A, Du J, Wei X, Lemley RA, Daneshzand M, Nummenmaa A, Buckner RL, Eldaief MC. Precision Network Modeling of TMS Across Individuals Suggests Therapeutic Targets and Potential for Improvement. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.15.24311994. [PMID: 39185539 PMCID: PMC11343249 DOI: 10.1101/2024.08.15.24311994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuro-psychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and recent advances in network-level TMS targeting, we demonstrate that clinical TMS approaches target different functional networks between individuals. Homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. We also modeled the impact of targeting the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC and found that the individual-specific anticorrelated region variably targets a network coupled to reward circuitry. Combining individualized, precision network mapping and electric field (E-field) modeling, we further illustrate how modeling can be deployed to prospectively target distinct closely localized association networks in the dlPFC with meaningful spatial selectivity and E-field intensity. Lastly, our findings emphasize differences between selectivity and maximal intensity, highlighting the need to consider both metrics in precision TMS efforts.
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16
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Ramduny J, Kelly C. Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction. Neuropsychopharmacology 2024:10.1038/s41386-024-01962-8. [PMID: 39147868 DOI: 10.1038/s41386-024-01962-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 08/02/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.
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Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
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17
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Trevino G, Lee JJ, Shimony JS, Luckett PH, Leuthardt EC. Complexity organization of resting-state functional-MRI networks. Hum Brain Mapp 2024; 45:e26809. [PMID: 39185729 PMCID: PMC11345701 DOI: 10.1002/hbm.26809] [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: 01/12/2024] [Revised: 05/28/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.
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Affiliation(s)
- Gabriel Trevino
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
| | - John J. Lee
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Joshua S. Shimony
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Patrick H. Luckett
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Eric C. Leuthardt
- Department of Neurological SurgeryWashington University School of MedicineSt. LouisMissouriUSA
- Center for Innovation in Neuroscience and TechnologyWashington University School of MedicineSt. LouisMissouriUSA
- Division of NeurotechnologyWashington University School of MedicineSt. LouisMissouriUSA
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18
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Gell M, Noble S, Laumann TO, Nelson SM, Tervo-Clemmens B. Psychiatric neuroimaging designs for individualised, cohort, and population studies. Neuropsychopharmacology 2024:10.1038/s41386-024-01918-y. [PMID: 39143320 DOI: 10.1038/s41386-024-01918-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.
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Affiliation(s)
- Martin Gell
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany.
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Stephanie Noble
- Psychology Department, Northeastern University, Boston, MA, USA
- Bioengineering Department, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN, USA.
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19
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Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01938-8. [PMID: 39117903 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
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Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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20
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Derman D, Pham DD, Mejia AF, Ferradal SL. Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.24.550218. [PMID: 39149306 PMCID: PMC11326129 DOI: 10.1101/2023.07.24.550218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.
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Affiliation(s)
- Diego Derman
- Department of Intelligent Systems Engineering, Indiana University, USA
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21
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Herzberg MP, Nielsen AN, Luby J, Sylvester CM. Measuring neuroplasticity in human development: the potential to inform the type and timing of mental health interventions. Neuropsychopharmacology 2024:10.1038/s41386-024-01947-7. [PMID: 39103496 DOI: 10.1038/s41386-024-01947-7] [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: 04/01/2024] [Revised: 06/17/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
Neuroplasticity during sensitive periods, the molecular and cellular process of enduring neural change in response to external stimuli during windows of high environmental sensitivity, is crucial for adaptation to expected environments and has implications for psychiatry. Animal research has characterized the developmental sequence and neurobiological mechanisms that govern neuroplasticity, yet gaps in our ability to measure neuroplasticity in humans limit the clinical translation of these principles. Here, we present a roadmap for the development and validation of neuroimaging and electrophysiology measures that index neuroplasticity to begin to address these gaps. We argue that validation of measures to track neuroplasticity in humans will elucidate the etiology of mental illness and inform the type and timing of mental health interventions to optimize effectiveness. We outline criteria for evaluating putative neuroimaging measures of plasticity in humans including links to neurobiological mechanisms shown to govern plasticity in animal models, developmental change that reflects heightened early life plasticity, and prediction of neural and/or behavior change. These criteria are applied to three putative measures of neuroplasticity using electroencephalography (gamma oscillations, aperiodic exponent of power/frequency) or functional magnetic resonance imaging (amplitude of low frequency fluctuations). We discuss the use of these markers in psychiatry, envision future uses for clinical and developmental translation, and suggest steps to address the limitations of the current putative neuroimaging measures of plasticity. With additional work, we expect these markers will significantly impact mental health and be used to characterize mechanisms, devise new interventions, and optimize developmental trajectories to reduce psychopathology risk.
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Affiliation(s)
- Max P Herzberg
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA.
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Joan Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO, USA
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22
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Li X, Bianchini Esper N, Ai L, Giavasis S, Jin H, Feczko E, Xu T, Clucas J, Franco A, Sólon Heinsfeld A, Adebimpe A, Vogelstein JT, Yan CG, Esteban O, Poldrack RA, Craddock C, Fair D, Satterthwaite T, Kiar G, Milham MP. Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nat Hum Behav 2024:10.1038/s41562-024-01942-4. [PMID: 39103610 DOI: 10.1038/s41562-024-01942-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.
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Affiliation(s)
- Xinhui Li
- Child Mind Institute, New York, NY, USA
| | | | - Lei Ai
- Child Mind Institute, New York, NY, USA
| | | | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | | | - Alexandre Franco
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | | | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, Stanford University, San Francisco, CA, USA
| | | | - Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Theodore Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Michael P Milham
- Child Mind Institute, New York, NY, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
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23
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Siegel JS. Precision functional mapping for target engagement in drug development. Neuropsychopharmacology 2024:10.1038/s41386-024-01945-9. [PMID: 39103494 DOI: 10.1038/s41386-024-01945-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, NYU Langone Center for Psychedelic Medicine, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
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24
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Khanra P, Nakuci J, Muldoon S, Watanabe T, Masuda N. Reliability of energy landscape analysis of resting-state functional MRI data. Eur J Neurosci 2024; 60:4265-4290. [PMID: 38837814 DOI: 10.1111/ejn.16390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 06/07/2024]
Abstract
Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
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Affiliation(s)
- Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Sarah Muldoon
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
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25
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Tang X, Turesky TK, Escalante ES, Loh MY, Xia M, Yu X, Gaab N. Longitudinal associations between language network characteristics in the infant brain and school-age reading abilities are mediated by early-developing phonological skills. Dev Cogn Neurosci 2024; 68:101405. [PMID: 38875769 PMCID: PMC11225703 DOI: 10.1016/j.dcn.2024.101405] [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: 11/23/2023] [Revised: 04/30/2024] [Accepted: 06/06/2024] [Indexed: 06/16/2024] Open
Abstract
Reading acquisition is a prolonged learning process relying on language development starting in utero. Behavioral longitudinal studies reveal prospective associations between infant language abilities and preschool/kindergarten phonological development that relates to subsequent reading performance. While recent pediatric neuroimaging work has begun to characterize the neural network underlying language development in infants, how this neural network scaffolds long-term language and reading acquisition remains unknown. We addressed this question in a 7-year longitudinal study from infancy to school-age. Seventy-six infants completed resting-state fMRI scanning, and underwent standardized language assessments in kindergarten. Of this larger cohort, forty-one were further assessed on their emergent word reading abilities after receiving formal reading instructions. Hierarchical clustering analyses identified a modular infant language network in which functional connectivity (FC) of the inferior frontal module prospectively correlated with kindergarten-age phonological skills and emergent word reading abilities. These correlations were obtained when controlling for infant age at scan, nonverbal IQ and parental education. Furthermore, kindergarten-age phonological skills mediated the relationship between infant FC and school-age reading abilities, implying a critical mid-way milestone for long-term reading development from infancy. Overall, our findings illuminate the neurobiological mechanisms by which infant language capacities could scaffold long-term reading acquisition.
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Affiliation(s)
- Xinyi Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Ted K Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA
| | - Elizabeth S Escalante
- Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA; Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Megan Yf Loh
- Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xi Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, MA 02138, USA
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26
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Korponay C, Janes AC, Frederick BB. Brain-wide functional connectivity artifactually inflates throughout functional magnetic resonance imaging scans. Nat Hum Behav 2024; 8:1568-1580. [PMID: 38898230 DOI: 10.1038/s41562-024-01908-6] [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: 09/13/2023] [Accepted: 05/03/2024] [Indexed: 06/21/2024]
Abstract
Functional magnetic resonance imaging (fMRI) is a central tool for investigating human brain function, organization and disease. Here, we show that fMRI-based estimates of functional brain connectivity artifactually inflate at spatially heterogeneous rates during resting-state and task-based scans. This produces false positive connection strength changes and spatial distortion of brain connectivity maps. We demonstrate that this artefact is driven by temporal inflation of the non-neuronal, systemic low-frequency oscillation (sLFO) blood flow signal during fMRI scanning and is not addressed by standard denoising procedures. We provide evidence that sLFO inflation reflects perturbations in cerebral blood flow by respiration and heart rate changes that accompany diminishing arousal during scanning, although the mechanisms of this pathway are uncertain. Finally, we show that adding a specialized sLFO denoising procedure to fMRI processing pipelines mitigates the artifactual inflation of functional connectivity, enhancing the validity and within-scan reliability of fMRI findings.
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Affiliation(s)
- Cole Korponay
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA.
- McLean Hospital Brain Imaging Center, Belmont, MA, USA.
| | - Amy C Janes
- Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Blaise B Frederick
- Department of Psychiatry, Harvard University Medical School, Boston, MA, USA
- McLean Hospital Brain Imaging Center, Belmont, MA, USA
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27
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Siegel JS, Subramanian S, Perry D, Kay BP, Gordon EM, Laumann TO, Reneau TR, Metcalf NV, Chacko RV, Gratton C, Horan C, Krimmel SR, Shimony JS, Schweiger JA, Wong DF, Bender DA, Scheidter KM, Whiting FI, Padawer-Curry JA, Shinohara RT, Chen Y, Moser J, Yacoub E, Nelson SM, Vizioli L, Fair DA, Lenze EJ, Carhart-Harris R, Raison CL, Raichle ME, Snyder AZ, Nicol GE, Dosenbach NUF. Psilocybin desynchronizes the human brain. Nature 2024; 632:131-138. [PMID: 39020167 PMCID: PMC11291293 DOI: 10.1038/s41586-024-07624-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 05/29/2024] [Indexed: 07/19/2024]
Abstract
A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.
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Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Subha Subramanian
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Demetrius Perry
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - T Rick Reneau
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Ravi V Chacko
- Department of Emergency Medicine, Advocate Christ Health Care, Oak Lawn, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | | | - Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Julie A Schweiger
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - David A Bender
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Forrest I Whiting
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Jonah A Padawer-Curry
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Robin Carhart-Harris
- Department of Neurology, University of California, San Francisco, CA, USA
- Centre for Psychedelic Research, Imperial College London, London, UK
| | - Charles L Raison
- Usona Institute, Fitchburg, WI, USA
- Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Marcus E Raichle
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Ginger E Nicol
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
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28
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Wang X, Krieger-Redwood K, Cui Y, Smallwood J, Du Y, Jefferies E. Macroscale brain states support the control of semantic cognition. Commun Biol 2024; 7:926. [PMID: 39090387 PMCID: PMC11294576 DOI: 10.1038/s42003-024-06630-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty - global association and feature matching judgments - which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain's dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
| | | | - Yanni Cui
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | | | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
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29
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Hunter MD, Fisher ZF, Geier CF. What ergodicity means for you. Dev Cogn Neurosci 2024; 68:101406. [PMID: 38909566 PMCID: PMC11254532 DOI: 10.1016/j.dcn.2024.101406] [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: 12/05/2023] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/25/2024] Open
Abstract
This paper explores the relation between within-person and between-person research designs using the concept of ergodicity from statistical mechanics in physics. We demonstrate the consequences of ergodicity using several real data examples from previously published studies. We then create several simulated examples that illustrate the independence of within-person processes from between-person differences, and pair these examples with analytic results that reinforce our conclusions. Finally, we discuss the plausibility of ergodicity being the general rule rather than the exception for social and behavioral processes, address common arguments against heeding the implications of ergodicity for behavioral research, and offer several possible solutions.
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Affiliation(s)
- Michael D Hunter
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Zachary F Fisher
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
| | - Charles F Geier
- Department of Human Development and Family Science, University of Georgia, Athens, GA 30602, USA
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30
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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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31
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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32
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Peng L, Su J, Hu D, Yu Y, Wei H, Li M. Measuring functional connectivity in frequency-domain helps to better characterize brain function. Hum Brain Mapp 2024; 45:e26726. [PMID: 38949487 PMCID: PMC11215841 DOI: 10.1002/hbm.26726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 03/25/2024] [Accepted: 05/09/2024] [Indexed: 07/02/2024] Open
Abstract
Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Jianpo Su
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Dewen Hu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Yang Yu
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
| | - Huilin Wei
- Systems Engineering InstituteAcademy of Military SciencesBeijingChina
| | - Ming Li
- College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina
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33
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Tripathi V, Rigolo L, Bracken BK, Galvin CP, Golby AJ, Tie Y, Somers DC. Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study. Hum Brain Mapp 2024; 45:e26764. [PMID: 38994667 PMCID: PMC11240144 DOI: 10.1002/hbm.26764] [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: 02/24/2024] [Revised: 05/09/2024] [Accepted: 06/09/2024] [Indexed: 07/13/2024] Open
Abstract
Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.
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Affiliation(s)
- Vaibhav Tripathi
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | - Laura Rigolo
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bethany K Bracken
- Sensing, Processing, and Applied Robotics (SPAR), Charles River Analytics, Cambridge, Massachusetts, USA
| | - Colin P Galvin
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yanmei Tie
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David C Somers
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
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34
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Ren J, Loughnan R, Xu B, Thompson WK, Fan CC. Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes. Commun Biol 2024; 7:836. [PMID: 38982203 PMCID: PMC11233705 DOI: 10.1038/s42003-024-06504-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.
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Affiliation(s)
- Junting Ren
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Street, La Jolla, 92093, CA, USA.
| | - Robert Loughnan
- Center for Human Development, University of California San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Bohan Xu
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Wesley K Thompson
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Street, La Jolla, 92093, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, 74136, OK, USA.
- Department of Radiology, School of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.
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35
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Bagheri S, Yu JC, Gallucci J, Tan V, Oliver LD, Dickie EW, Rashidi AG, Foussias G, Lai MC, Buchanan RW, Malhotra AK, Voineskos AN, Ameis SH, Hawco C. Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601737. [PMID: 39005278 PMCID: PMC11245004 DOI: 10.1101/2024.07.02.601737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (ASDs and SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), ASD and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition. fALFF from 495 participants (185 TDC, 68 ASD, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participant's average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps. Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and ASD participants compared with TDCs. Limited differences were observed between ASD and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the ASD and SSD groups was also significantly higher compared with TDC. Similar patterns of fALFF and individual variability in ASD and SSD suggest some common neurobiological deficits across these related heterogeneous conditions.
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Affiliation(s)
- Soroush Bagheri
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Ju-Chi Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Vinh Tan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Lindsay D. Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Erin W. Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ayesha G. Rashidi
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Research Institute, and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Robert W. Buchanan
- Maryland Psychiatric Research Centre, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Anil K. Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, NY, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, NY, USA
- Centre for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Stephanie H. Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Research Institute, and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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36
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Das A, Menon V. Frequency-specific directed connectivity between the hippocampus and parietal cortex during verbal and spatial episodic memory: an intracranial EEG replication. Cereb Cortex 2024; 34:bhae287. [PMID: 39042030 PMCID: PMC11264422 DOI: 10.1093/cercor/bhae287] [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: 03/14/2024] [Revised: 05/23/2024] [Indexed: 07/24/2024] Open
Abstract
Hippocampus-parietal cortex circuits are thought to play a crucial role in memory and attention, but their neural basis remains poorly understood. We employed intracranial intracranial electroencephalography (iEEG) to investigate the neurophysiological underpinning of these circuits across three memory tasks spanning verbal and spatial domains. We uncovered a consistent pattern of higher causal directed connectivity from the hippocampus to both lateral parietal cortex (supramarginal and angular gyrus) and medial parietal cortex (posterior cingulate cortex) in the delta-theta band during memory encoding and recall. This connectivity was independent of activation or suppression states in the hippocampus or parietal cortex. Crucially, directed connectivity from the supramarginal gyrus to the hippocampus was enhanced in participants with higher memory recall, highlighting its behavioral significance. Our findings align with the attention-to-memory model, which posits that attention directs cognitive resources toward pertinent information during memory formation. The robustness of these results was demonstrated through Bayesian replication analysis of the memory encoding and recall periods across the three tasks. Our study sheds light on the neural basis of casual signaling within hippocampus-parietal circuits, broadening our understanding of their critical roles in human cognition.
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Affiliation(s)
- Anup Das
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305
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37
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Wang G, Datta A, Lindquist MA. Improved fMRI-based pain prediction using Bayesian group-wise functional registration. Biostatistics 2024; 25:885-903. [PMID: 37805937 DOI: 10.1093/biostatistics/kxad026] [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: 03/13/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 10/10/2023] Open
Abstract
In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.
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Affiliation(s)
- Guoqing Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
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38
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [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: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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39
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Bhandari A, Keglovits H, Badre D. Task structure tailors the geometry of neural representations in human lateral prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583429. [PMID: 38496680 PMCID: PMC10942429 DOI: 10.1101/2024.03.06.583429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.
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Affiliation(s)
- Apoorva Bhandari
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
| | - Haley Keglovits
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
- Robert J & Nancy D Carney Institute for Brain Science, Brown University, 164 Angell St, Providence, RI 02912, USA
| | - David Badre
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
- Robert J & Nancy D Carney Institute for Brain Science, Brown University, 164 Angell St, Providence, RI 02912, USA
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40
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Nick Q, Gale DJ, Areshenkoff C, De Brouwer A, Nashed J, Wammes J, Zhu T, Flanagan R, Smallwood J, Gallivan J. Reconfigurations of cortical manifold structure during reward-based motor learning. eLife 2024; 12:RP91928. [PMID: 38916598 PMCID: PMC11198988 DOI: 10.7554/elife.91928] [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] [Indexed: 06/26/2024] Open
Abstract
Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.
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Affiliation(s)
- Qasem Nick
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Daniel J Gale
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Corson Areshenkoff
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Anouk De Brouwer
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Joseph Nashed
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Medicine, Queen's UniversityKingstonCanada
| | - Jeffrey Wammes
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Tianyao Zhu
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Randy Flanagan
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Jonny Smallwood
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Jason Gallivan
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
- Department of Biomedical and Molecular Sciences, Queen’s UniversityKingstonCanada
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41
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McAvoy MP, Liu L, Zhou R, Philip BA. Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.19.558290. [PMID: 37781580 PMCID: PMC10541115 DOI: 10.1101/2023.09.19.558290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Volumetric analysis methods continue to enjoy great popularity in the analysis of task-related functional MRI (fMRI) data. Among these methods, the software package FSL (FMRIB, Oxford, UK) is omnipresent throughout the field. However, it remains unknown what advantages might be gained by integrating FSL with alternative preprocessing tools. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to apply FreeSurfer brain extraction for simultaneous registration and motion correction ("one-step resampling"), for FSL volumetric analysis of fMRI data. We compared three preprocessing approaches (OGRE, FSL, and fMRIPrep) in a dataset wherein adult human volunteers (N=39) performed a precision drawing task during fMRI scanning. The three approaches produced grossly similar results, but OGRE's preprocessing led to lower inter-individual variability across the brain and greater detected activation in primary motor cortex contralateral to movement. This demonstrates that FreeSurfer tools and one-step resampling can improve FSL's volumetric analysis of fMRI data. The OGRE pipeline provides an off-the-shelf method to apply FreeSurfer-based brain extraction and one-step resampling of motion correction and registration for FSL analysis of task fMRI data.
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Affiliation(s)
- Mark P. McAvoy
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
| | - Lei Liu
- Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Ruiwen Zhou
- Department of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Benjamin A. Philip
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
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42
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Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [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: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
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43
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Xu X, Yang H, Cong J, Sydnor V, Cui Z. Structural connectivity matures along a sensorimotor-association connectional axis in youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599267. [PMID: 38948845 PMCID: PMC11212872 DOI: 10.1101/2024.06.17.599267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Childhood and adolescence are associated with protracted developmental remodeling of cortico-cortical structural connectivity. However, how heterochronous development in white matter structural connectivity spatially and temporally unfolds across the macroscale human connectome remains unknown. Leveraging non-invasive diffusion MRI data from both cross-sectional (N = 590) and longitudinal (baseline: N = 3,949; two-year follow-up: N = 3,155) developmental datasets, we found that structural connectivity development diverges along a pre-defined sensorimotor-association (S-A) connectional axis from ages 8.1 to 21.9 years. Specifically, we observed a continuum of developmental profiles that spans from an early childhood increase in connectivity strength in sensorimotor-sensorimotor connections to a late adolescent increase in association-association connectional strength. The S-A connectional axis also captured spatial variations in associations between structural connectivity and both higher-order cognition and general psychopathology. Together, our findings reveal a hierarchical axis in the development of structural connectivity across the human connectome.
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Affiliation(s)
- Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University; Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Hang Yang
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Jing Cong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University; Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Valerie Sydnor
- Department of Psychiatry, University of Pittsburgh Medical Center; Pittsburgh, PA, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
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44
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Functional connectome through the human life span. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.12.557193. [PMID: 37745373 PMCID: PMC10515818 DOI: 10.1101/2023.09.12.557193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji’nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | | | | | | | | | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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45
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Tang X, Turesky TK, Escalante ES, Loh MY, Xia M, Yu X, Gaab N. Longitudinal associations between language network characteristics in the infant brain and school-age reading abilities are mediated by early-developing phonological skills. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.22.546194. [PMID: 38895379 PMCID: PMC11185523 DOI: 10.1101/2023.06.22.546194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Reading acquisition is a prolonged learning process relying on language development starting in utero. Behavioral longitudinal studies reveal prospective associations between infant language abilities and preschool/kindergarten phonological development that relates to subsequent reading performance. While recent pediatric neuroimaging work has begun to characterize the neural network underlying language development in infants, how this neural network scaffolds long-term language and reading acquisition remains unknown. We addressed this question in a 7-year longitudinal study from infancy to school-age. Seventy-six infants completed resting-state fMRI scanning, and underwent standardized language assessments in kindergarten. Of this larger cohort, forty-one were further assessed on their emergent word reading abilities after receiving formal reading instructions. Hierarchical clustering analyses identified a modular infant language network in which functional connectivity (FC) of the inferior frontal module prospectively correlated with kindergarten-age phonological skills and emergent word reading abilities. These correlations were obtained when controlling for infant age at scan, nonverbal IQ and parental education. Furthermore, kindergarten-age phonological skills mediated the relationship between infant FC and school-age reading abilities, implying a critical mid-way milestone for long-term reading development from infancy. Overall, our findings illuminate the neurobiological mechanisms by which infant language capacities could scaffold long-term reading acquisition.
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Affiliation(s)
- Xinyi Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Ted K. Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, Massachusetts, USA, 02138
| | - Elizabeth S. Escalante
- Harvard Graduate School of Education, Harvard University, Cambridge, Massachusetts, USA, 02138
| | - Megan Yf Loh
- Harvard Graduate School of Education, Harvard University, Cambridge, Massachusetts, USA, 02138
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Xi Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 100875
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, Massachusetts, USA, 02138
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46
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Nenning KH, Xu T, Tambini A, Franco AR, Margulies DS, Colcombe SJ, Milham MP. Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs. Commun Biol 2024; 7:697. [PMID: 38844612 PMCID: PMC11156950 DOI: 10.1038/s42003-024-06401-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 05/30/2024] [Indexed: 06/09/2024] Open
Abstract
Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization.
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Affiliation(s)
- Karl-Heinz Nenning
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | - Arielle Tambini
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- New York University, New York, NY, USA
| | - Alexandre R Franco
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Child Mind Institute, New York, NY, USA
- New York University, New York, NY, USA
| | | | - Stanley J Colcombe
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Child Mind Institute, New York, NY, USA
- New York University, New York, NY, USA
| | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Child Mind Institute, New York, NY, USA
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47
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Pinho AL, Richard H, Ponce AF, Eickenberg M, Amadon A, Dohmatob E, Denghien I, Torre JJ, Shankar S, Aggarwal H, Thual A, Chapalain T, Ginisty C, Becuwe-Desmidt S, Roger S, Lecomte Y, Berland V, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Varoquaux G, Dehaene S, Hertz-Pannier L, Thirion B. Individual Brain Charting dataset extension, third release for movie watching and retinotopy data. Sci Data 2024; 11:590. [PMID: 38839770 PMCID: PMC11153490 DOI: 10.1038/s41597-024-03390-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.
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Affiliation(s)
- Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France.
- Department of Computer Science, Western University, London, Ontario, Canada.
- Western Centre for Brain and Mind, Western University, London, Ontario, Canada.
| | - Hugo Richard
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Criteo AI Labs, Paris, France
- FAIRPLAY - IA coopérative: équité, vie privée, incitations, Paris, France
| | | | - Michael Eickenberg
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Flatiron Institute, New York, USA
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, 91191, Gif-sur-Yvette, France
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Meta FAIR, Paris, France
| | - Isabelle Denghien
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
| | | | - Swetha Shankar
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | | | - Alexis Thual
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | | | | | | | | | - Yann Lecomte
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
| | | | | | | | | | | | | | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
- UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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48
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Reddy NA, Clements RG, Brooks JCW, Bright MG. Simultaneous cortical, subcortical, and brainstem mapping of sensory activation. Cereb Cortex 2024; 34:bhae273. [PMID: 38940832 PMCID: PMC11212354 DOI: 10.1093/cercor/bhae273] [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: 04/15/2024] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024] Open
Abstract
Nonpainful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging studies have highlighted the value of whole-brain, systems-level investigation for examining sensory processing. However, whole-brain functional magnetic resonance imaging studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high-resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo functional magnetic resonance imaging acquisition at 3T with multi-echo independent component analysis denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to nonpainful brushing of the right hand, left hand, and right foot (n = 10 per location), and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we differentiated the adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.
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Affiliation(s)
- Neha A Reddy
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, United States
| | - Rebecca G Clements
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, United States
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
| | - Molly G Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL 60208, United States
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49
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Basile GA, Tatti E, Bertino S, Milardi D, Genovese G, Bruno A, Muscatello MRA, Ciurleo R, Cerasa A, Quartarone A, Cacciola A. Neuroanatomical correlates of peripersonal space: bridging the gap between perception, action, emotion and social cognition. Brain Struct Funct 2024; 229:1047-1072. [PMID: 38683211 PMCID: PMC11147881 DOI: 10.1007/s00429-024-02781-9] [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] [Accepted: 02/22/2024] [Indexed: 05/01/2024]
Abstract
Peripersonal space (PPS) is a construct referring to the portion of space immediately surrounding our bodies, where most of the interactions between the subject and the environment, including other individuals, take place. Decades of animal and human neuroscience research have revealed that the brain holds a separate representation of this region of space: this distinct spatial representation has evolved to ensure proper relevance to stimuli that are close to the body and prompt an appropriate behavioral response. The neural underpinnings of such construct have been thoroughly investigated by different generations of studies involving anatomical and electrophysiological investigations in animal models, and, recently, neuroimaging experiments in human subjects. Here, we provide a comprehensive anatomical overview of the anatomical circuitry underlying PPS representation in the human brain. Gathering evidence from multiple areas of research, we identified cortical and subcortical regions that are involved in specific aspects of PPS encoding.We show how these regions are part of segregated, yet integrated functional networks within the brain, which are in turn involved in higher-order integration of information. This wide-scale circuitry accounts for the relevance of PPS encoding in multiple brain functions, including not only motor planning and visuospatial attention but also emotional and social cognitive aspects. A complete characterization of these circuits may clarify the derangements of PPS representation observed in different neurological and neuropsychiatric diseases.
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Affiliation(s)
- Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy.
| | - Elisa Tatti
- Department of Molecular, Cellular & Biomedical Sciences, CUNY, School of Medicine, New York, NY, 10031, USA
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | | | - Antonio Bruno
- Psychiatry Unit, University Hospital "G. Martino", Messina, Italy
- Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Maria Rosaria Anna Muscatello
- Psychiatry Unit, University Hospital "G. Martino", Messina, Italy
- Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | | | - Antonio Cerasa
- S. Anna Institute, Crotone, Italy
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, Messina, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Rende, Italy
| | | | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy.
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50
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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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