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Chen S, Rahn RM, Bice AR, Bice SH, Padawer-Curry JA, Hengen KB, Dougherty JD, Culver JP. Visual Deprivation during Mouse Critical Period Reorganizes Network-Level Functional Connectivity. J Neurosci 2024; 44:e1019232024. [PMID: 38538145 PMCID: PMC11079959 DOI: 10.1523/jneurosci.1019-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 04/09/2024] Open
Abstract
A classic example of experience-dependent plasticity is ocular dominance (OD) shift, in which the responsiveness of neurons in the visual cortex is profoundly altered following monocular deprivation (MD). It has been postulated that OD shifts also modify global neural networks, but such effects have never been demonstrated. Here, we use wide-field fluorescence optical imaging (WFOI) to characterize calcium-based resting-state functional connectivity during acute (3 d) MD in female and male mice with genetically encoded calcium indicators (Thy1-GCaMP6f). We first establish the fundamental performance of WFOI by computing signal to noise properties throughout our data processing pipeline. Following MD, we found that Δ band (0.4-4 Hz) GCaMP6 activity in the deprived visual cortex decreased, suggesting that excitatory activity in this region was reduced by MD. In addition, interhemispheric visual homotopic functional connectivity decreased following MD, which was accompanied by a reduction in parietal and motor homotopic connectivity. Finally, we observed enhanced internetwork connectivity between the visual and parietal cortex that peaked 2 d after MD. Together, these findings support the hypothesis that early MD induces dynamic reorganization of disparate functional networks including the association cortices.
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Affiliation(s)
- Siyu Chen
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
- Genetics, Washington University School of Medicine, St. Louis, Missouri 63110
- Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Rachel M Rahn
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
- Genetics, Washington University School of Medicine, St. Louis, Missouri 63110
- Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Annie R Bice
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Seana H Bice
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Jonah A Padawer-Curry
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Keith B Hengen
- Biology, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Joseph D Dougherty
- Genetics, Washington University School of Medicine, St. Louis, Missouri 63110
- Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, St. Louis, Missouri 63110
| | - Joseph P Culver
- Departments of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110
- Physics, Washington University School of Medicine, St. Louis, Missouri 63110
- Biomedical Engineering, Washington University School of Medicine, St. Louis, Missouri 63110
- Imaging Science PhD Program, Washington University School of Medicine, St. Louis, Missouri 63110
- Biophotonics Research Center, Washington University School of Medicine, St. Louis, Missouri 63110
- Neuroscience, Washington University School of Medicine, St. Louis, Missouri 63110
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2
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Tripathy K, Fogarty M, Svoboda AM, Schroeder ML, Rafferty SM, Richter EJ, Tracy C, Mansfield PK, Booth M, Fishell AK, Sherafati A, Markow ZE, Wheelock MD, Arbeláez AM, Schlaggar BL, Smyser CD, Eggebrecht AT, Culver JP. Mapping brain function in adults and young children during naturalistic viewing with high-density diffuse optical tomography. Hum Brain Mapp 2024; 45:e26684. [PMID: 38703090 PMCID: PMC11069306 DOI: 10.1002/hbm.26684] [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: 08/01/2023] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 05/06/2024] Open
Abstract
Human studies of early brain development have been limited by extant neuroimaging methods. MRI scanners present logistical challenges for imaging young children, while alternative modalities like functional near-infrared spectroscopy have traditionally been limited by image quality due to sparse sampling. In addition, conventional tasks for brain mapping elicit low task engagement, high head motion, and considerable participant attrition in pediatric populations. As a result, typical and atypical developmental trajectories of processes such as language acquisition remain understudied during sensitive periods over the first years of life. We evaluate high-density diffuse optical tomography (HD-DOT) imaging combined with movie stimuli for high resolution optical neuroimaging in awake children ranging from 1 to 7 years of age. We built an HD-DOT system with design features geared towards enhancing both image quality and child comfort. Furthermore, we characterized a library of animated movie clips as a stimulus set for brain mapping and we optimized associated data analysis pipelines. Together, these tools could map cortical responses to movies and contained features such as speech in both adults and awake young children. This study lays the groundwork for future research to investigate response variability in larger pediatric samples and atypical trajectories of early brain development in clinical populations.
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Affiliation(s)
- Kalyan Tripathy
- Division of Biological and Biomedical SciencesWashington University in St. LouisSt. LouisMissouriUSA
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Western Psychiatric HospitalUniversity of Pittsburgh Medical CenterPittsburghPennsylvaniaUSA
| | - Morgan Fogarty
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Imaging Science ProgramWashington University in St. LouisSt. LouisMissouriUSA
| | - Alexandra M. Svoboda
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Mariel L. Schroeder
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Sean M. Rafferty
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Edward J. Richter
- Department of Electrical and Systems EngineeringWashington University in St. LouisSt. LouisMissouriUSA
| | - Christopher Tracy
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Patricia K. Mansfield
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Madison Booth
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Andrew K. Fishell
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Arefeh Sherafati
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of PhysicsWashington University in St. LouisSt. LouisMissouriUSA
| | - Zachary E. Markow
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of Biomedical EngineeringWashington University in St. LouisSt. LouisMissouriUSA
| | - Muriah D. Wheelock
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Ana María Arbeláez
- Department of PediatricsWashington University School of MedicineSt. LouisMissouriUSA
| | - Bradley L. Schlaggar
- Kennedy Krieger InstituteBaltimoreMarylandUSA
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of PediatricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Christopher D. Smyser
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of PediatricsWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Adam T. Eggebrecht
- Division of Biological and Biomedical SciencesWashington University in St. LouisSt. LouisMissouriUSA
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Imaging Science ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Department of Electrical and Systems EngineeringWashington University in St. LouisSt. LouisMissouriUSA
- Department of PhysicsWashington University in St. LouisSt. LouisMissouriUSA
- Department of Biomedical EngineeringWashington University in St. LouisSt. LouisMissouriUSA
| | - Joseph P. Culver
- Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisMissouriUSA
- Imaging Science ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Department of PhysicsWashington University in St. LouisSt. LouisMissouriUSA
- Department of Biomedical EngineeringWashington University in St. LouisSt. LouisMissouriUSA
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3
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Guard M, Labonte AK, Mendoza M, Myers MJ, Duncan M, Drysdale AT, Mukherji E, Rahman T, Tandon M, Kelly JC, Cooke E, Rogers CE, Lenze S, Sylvester CM. Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging. J Clin Psychopharmacol 2024; 44:240-249. [PMID: 38551454 DOI: 10.1097/jcp.0000000000001859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
PURPOSE/BACKGROUND Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown. METHODS/PROCEDURES We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone. FINDINGS/RESULTS The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores. IMPLICATIONS/CONCLUSIONS Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.
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Affiliation(s)
| | - Alyssa K Labonte
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Molly Mendoza
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Michael J Myers
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Maida Duncan
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Andrew T Drysdale
- New York State Psychiatric Institute and the Department of Psychiatry, Columbia University Irving Medical Center, New York, NY
| | - Emily Mukherji
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Tahir Rahman
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Mini Tandon
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
| | - Jeannie C Kelly
- Department of Obstetrics and Gynecology, Washington University in St Louis, St Louis, MO
| | - Emily Cooke
- Department of Pharmacy, Barnes-Jewish Hospital, St Louis, MO
| | | | - Shannon Lenze
- From the Department of Psychiatry, Washington University in St Louis, St Louis, MO
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4
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Clouette J, Potvin-Desrochers A, Seo F, Churchward-Venne TA, Paquette C. Reorganization of Brain Resting-state Functional Connectivity Following 14 Days of Elbow Immobilization in Young Females. Neuroscience 2024; 540:77-86. [PMID: 38246474 DOI: 10.1016/j.neuroscience.2024.01.005] [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: 08/24/2023] [Revised: 12/12/2023] [Accepted: 01/09/2024] [Indexed: 01/23/2024]
Abstract
Limb immobilization is known to cause significant decreases in muscle strength and muscle mass as early as two days following the onset of immobilization. However, the decline in strength surpasses the decline in muscle mass, suggesting that factors in addition to muscle loss, such as neuroplasticity, contribute to the decrease in force production. However, little is known regarding immobilization-induced neural changes, although sensorimotor regions seem to be the most affected. The present study aimed to determine whether brain functional organization is altered following 14 days of unilateral elbow immobilization. Functional organization was quantified using resting-state functional connectivity, a measure of the synchronicity of the spontaneous discharge of different brain regions at rest. Data was obtained from twelve healthy young females before and after completing the immobilization period. A seed-to-voxel analysis was performed using seeds associated with cortical, subcortical, and cerebellar sensorimotor regions of the brain. The results showed changes predominantly involving cerebellar connectivity. For example, the immobilization period caused a decrease in connectivity between the motor cerebellar region of the immobilized arm and the left temporal lobe, and an increase between the same cerebellar region and the supplementary motor area. Overall, changes in connectivity occurred in regions typically associated with error detection and motor learning, suggesting a potential functional reorganization of the brain within 14 days of elbow immobilization.
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Affiliation(s)
- Julien Clouette
- Department of Kinesiology and Physical Education, McGill University, 475 Pine Ave., Montreal, Quebec, Canada; Centre for Interdisciplinary Research in Rehabilitation, 6363 Hudson Road, Montreal, Quebec, Canada
| | - Alexandra Potvin-Desrochers
- Department of Kinesiology and Physical Education, McGill University, 475 Pine Ave., Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, 1033 Pine Ave., Montreal, Quebec, Canada; Centre for Interdisciplinary Research in Rehabilitation, 6363 Hudson Road, Montreal, Quebec, Canada
| | - Freddie Seo
- Department of Kinesiology and Physical Education, McGill University, 475 Pine Ave., Montreal, Quebec, Canada
| | - Tyler A Churchward-Venne
- Department of Kinesiology and Physical Education, McGill University, 475 Pine Ave., Montreal, Quebec, Canada; Division of Geriatric Medicine, McGill University, 1650 Cedar Ave., Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, Canada
| | - Caroline Paquette
- Department of Kinesiology and Physical Education, McGill University, 475 Pine Ave., Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, 1033 Pine Ave., Montreal, Quebec, Canada; Centre for Interdisciplinary Research in Rehabilitation, 6363 Hudson Road, Montreal, Quebec, Canada.
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5
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Ooi LQR, Orban C, Nichols TE, Zhang S, Tan TWK, Kong R, Marek S, Dosenbach NU, Laumann T, Gordon EM, Zhou JH, Bzdok D, Eickhoff SB, Holmes AJ, Yeo BTT. MRI economics: Balancing sample size and scan duration in brain wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.16.580448. [PMID: 38405815 PMCID: PMC10889017 DOI: 10.1101/2024.02.16.580448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.
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Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Trevor Wei Kiat Tan
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
| | - Nico U.F. Dosenbach
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
- Department of Neurology, Washington University, School of Medicine, USA
- Department of Psychiatry, Washington University, School of Medicine, USA
- Deparments of Paediatrics, Biomedical Engineering, and Psychological and Brain Sciences, Washington University, School of Medicine, USA
| | - Timothy Laumann
- Department of Psychiatry, Washington University, School of Medicine, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University, School of Medicine, USA
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada
- Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
| | - B. T. Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
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6
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Chauvin RJ, Newbold DJ, Nielsen AN, Miller RL, Krimmel SR, Metoki A, Wang A, Van AN, Montez DF, Marek S, Suljic V, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Snyder AZ, Kay BP, Raichle ME, Laumann TO, Gordon EM, Dosenbach NU. Disuse-driven plasticity in the human thalamus and putamen. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.07.566031. [PMID: 37987000 PMCID: PMC10659348 DOI: 10.1101/2023.11.07.566031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Motor adaptation in cortico-striato-thalamo-cortical loops has been studied mainly in animals using invasive electrophysiology. Here, we leverage functional neuroimaging in humans to study motor circuit plasticity in the human subcortex. We employed an experimental paradigm that combined two weeks of upper-extremity immobilization with daily resting-state and motor task fMRI before, during, and after the casting period. We previously showed that limb disuse leads to decreased functional connectivity (FC) of the contralateral somatomotor cortex (SM1) with the ipsilateral somatomotor cortex, increased FC with the cingulo-opercular network (CON) as well as the emergence of high amplitude, fMRI signal pulses localized in the contralateral SM1, supplementary motor area and the cerebellum. From our prior observations, it remains unclear whether the disuse plasticity affects the thalamus and striatum. We extended our analysis to include these subcortical regions and found that both exhibit strengthened cortical FC and spontaneous fMRI signal pulses induced by limb disuse. The dorsal posterior putamen and the central thalamus, mainly CM, VLP and VIM nuclei, showed disuse pulses and FC changes that lined up with fmri task activations from the Human connectome project motor system localizer, acquired before casting for each participant. Our findings provide a novel understanding of the role of the cortico-striato-thalamo-cortical loops in human motor plasticity and a potential link with the physiology of sleep regulation. Additionally, similarities with FC observation from Parkinson Disease (PD) questions a pathophysiological link with limb disuse.
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Affiliation(s)
- Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Dillan J. Newbold
- Department of Neurology, New York University Grossman School of Medicine, New York, New York 10016, USA
| | - Ashley N. Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Ryland L. Miller
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
| | - Andrew N. Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Abraham Z. Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Marcus E. Raichle
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- 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
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Nico U.F. Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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7
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Wang M, G'Sell M, Castellano JF, Richardson RM, Ghuman A. A week in the life of the human brain: stable states punctuated by chaotic transitions. RESEARCH SQUARE 2024:rs.3.rs-2752903. [PMID: 37034705 PMCID: PMC10081438 DOI: 10.21203/rs.3.rs-2752903/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Many important neurocognitive states, such as performing natural activities and fluctuations of arousal, shift over minutes-to-hours in the real-world. We harnessed 3-12 days of continuous multi-electrode intracranial recordings in twenty humans during natural behavior (socializing, using digital devices, sleeping, etc.) to study real-world neurodynamics. Applying deep learning with dynamical systems approaches revealed that brain networks formed consistent stable states that predicted behavior and physiology. Changes in behavior were associated with bursts of rapid neural fluctuations where brain networks chaotically explored many configurations before settling into new states. These trajectories traversed an hourglass-shaped structure anchored around a set of networks that slowly tracked levels of outward awareness related to wake-sleep stages, and a central attractor corresponding to default mode network activation. These findings indicate ways our brains use rapid, chaotic transitions that coalesce into neurocognitive states slowly fluctuating around a stabilizing central equilibrium to balance flexibility and stability during real-world behavior.
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8
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Krimmel SR, Laumann TO, Chauvin RJ, Hershey T, Roland JL, Shimony JS, Willie JT, Norris SA, Marek S, Van AN, Monk J, Scheidter KM, Whiting F, Ramirez-Perez N, Metoki A, Wang A, Kay BP, Nahman-Averbuch H, Fair DA, Lynch CJ, Raichle ME, Gordon EM, Dosenbach NUF. The brainstem's red nucleus was evolutionarily upgraded to support goal-directed action. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.30.573730. [PMID: 38260662 PMCID: PMC10802246 DOI: 10.1101/2023.12.30.573730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The red nucleus is a large brainstem structure that coordinates limb movement for locomotion in quadrupedal animals (Basile et al., 2021). The humans red nucleus has a different pattern of anatomical connectivity compared to quadrupeds, suggesting a unique purpose (Hatschek, 1907). Previously the function of the human red nucleus remained unclear at least partly due to methodological limitations with brainstem functional neuroimaging (Sclocco et al., 2018). Here, we used our most advanced resting-state functional connectivity (RSFC) based precision functional mapping (PFM) in highly sampled individuals (n = 5) and large group-averaged datasets (combined N ~ 45,000), to precisely examine red nucleus functional connectivity. Notably, red nucleus functional connectivity to motor-effector networks (somatomotor hand, foot, and mouth) was minimal. Instead, red nucleus functional connectivity along the central sulcus was specific to regions of the recently discovered somato-cognitive action network (SCAN; (Gordon et al., 2023)). Outside of primary motor cortex, red nucleus connectivity was strongest to the cingulo-opercular (CON) and salience networks, involved in action/cognitive control (Dosenbach et al., 2007; Newbold et al., 2021) and reward/motivated behavior (Seeley, 2019), respectively. Functional connectivity to these two networks was organized into discrete dorsal-medial and ventral-lateral zones. Red nucleus functional connectivity to the thalamus recapitulated known structural connectivity of the dento-rubral thalamic tract (DRTT) and could prove clinically useful in functionally targeting the ventral intermediate (VIM) nucleus. In total, our results indicate that far from being a 'motor' structure, the red nucleus is better understood as a brainstem nucleus for implementing goal-directed behavior, integrating behavioral valence and action plans.
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Affiliation(s)
- Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Roselyne J Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Tamara Hershey
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri, USA
| | - Jarod L Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jon T Willie
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Psychiatry, Weill Cornell Medicine, New York, New York, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
| | - Scott A Norris
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Julia Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Forrest Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Nadeshka Ramirez-Perez
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Anxu Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Division of Computation and Data Science, Washington University, St. Louis, Missouri, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Hadas Nahman-Averbuch
- Washington University Pain Center, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota, USA
- Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, USA
| | - Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, New York, USA
| | - Marcus E Raichle
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Psychological & Brain Sciences, Washington University, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
- Program in Occupational Therapy, Washington University, St. Louis, Missouri, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, USA
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9
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Chen S, Rahn RM, Bice AR, Bice SH, Padawer-Curry JA, Hengen KB, Dougherty JD, Culver JP. Visual deprivation during mouse critical period reorganizes network-level functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.30.542957. [PMID: 37398380 PMCID: PMC10312598 DOI: 10.1101/2023.05.30.542957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
A classic example of experience-dependent plasticity is ocular dominance (OD) shift, in which the responsiveness of neurons in the visual cortex is profoundly altered following monocular deprivation (MD). It has been postulated that OD shifts also modify global neural networks, but such effects have never been demonstrated. Here, we used longitudinal wide-field optical calcium imaging to measure resting-state functional connectivity during acute (3-day) MD in mice. First, delta GCaMP6 power in the deprived visual cortex decreased, suggesting that excitatory activity was reduced in the region. In parallel, interhemispheric visual homotopic functional connectivity was rapidly reduced by the disruption of visual drive through MD and was sustained significantly below baseline state. This reduction of visual homotopic connectivity was accompanied by a reduction in parietal and motor homotopic connectivity. Finally, we observed enhanced internetwork connectivity between visual and parietal cortex that peaked at MD2. Together, these findings support the hypothesis that early MD induces dynamic reorganization of disparate functional networks including association cortices.
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Affiliation(s)
- Siyu Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rachel M. Rahn
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Annie R. Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Seana H. Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jonah A. Padawer-Curry
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Keith B. Hengen
- Department of Biology, Washington University, St. Louis, MO 63130, USA
| | - Joseph D. Dougherty
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University, St. Louis, MO 63130, USA
| | - Joseph P. Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Physics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, USA
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10
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Casartelli L, Maronati C, Cavallo A. From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability. Phys Life Rev 2023; 47:245-263. [PMID: 37976727 DOI: 10.1016/j.plrev.2023.10.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/19/2023]
Abstract
In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.
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Affiliation(s)
- Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E. MEDEA, Italy
| | - Camilla Maronati
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy
| | - Andrea Cavallo
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy; C'MoN Unit, Fondazione Istituto Italiano di Tecnologia, Genova, Italy.
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11
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Hotta J, Saari J, Harno H, Kalso E, Forss N, Hari R. Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1. Hum Brain Mapp 2023; 44:6258-6274. [PMID: 37837646 PMCID: PMC10619416 DOI: 10.1002/hbm.26513] [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: 01/15/2023] [Revised: 06/16/2023] [Accepted: 09/17/2023] [Indexed: 10/16/2023] Open
Abstract
In complex regional pain syndrome (CRPS), the representation area of the affected limb in the primary sensorimotor cortex (SM1) reacts abnormally during sensory stimulation and motor actions. We recorded 3T functional magnetic resonance imaging resting-state data from 17 upper-limb CRPS type 1 patients and 19 healthy control subjects to identify alterations of patients' SM1 function during spontaneous pain and to find out how the spatial distribution of these alterations were related to peripheral symptoms. Seed-based correlations and independent component analyses indicated that patients' upper-limb SM1 representation areas display (i) reduced interhemispheric connectivity, associated with the combined effect of intensity and spatial extent of limb pain, (ii) increased connectivity with the right anterior insula that positively correlated with the duration of CRPS, (iii) increased connectivity with periaqueductal gray matter, and (iv) disengagement from the other parts of the SM1 network. These findings, now reported for the first time in CRPS, parallel the alterations found in patients suffering from other chronic pain conditions or from limb denervation; they also agree with findings in healthy persons who are exposed to experimental pain or have used their limbs asymmetrically. Our results suggest that CRPS is associated with a sustained and somatotopically specific alteration of SM1 function, that has correspondence to the spatial distribution of the peripheral manifestations and to the duration of the syndrome.
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Affiliation(s)
- Jaakko Hotta
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Aalto NeuroImagingAalto UniversityEspooFinland
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
| | - Jukka Saari
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Aalto NeuroImagingAalto UniversityEspooFinland
- Department of Applied PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Hanna Harno
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
- Department of Anaesthesiology, Intensive Care and Pain MedicineUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Eija Kalso
- Department of Anaesthesiology, Intensive Care and Pain MedicineUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Nina Forss
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
| | - Riitta Hari
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Department of Art and MediaAalto University School of Arts, Design and ArchitectureHelsinkiFinland
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12
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Van AN, Montez DF, Laumann TO, Suljic V, Madison T, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Chauvin RJ, Krimmel SR, Metoki A, Rajesh A, Roland JL, Salo T, Wang A, Weldon KB, Sotiras A, Shimony JS, Kay BP, Nelson SM, Tervo-Clemmens B, Marek SA, Vizioli L, Yacoub E, Satterthwaite TD, Gordon EM, Fair DA, Tisdall MD, Dosenbach NU. Framewise multi-echo distortion correction for superior functional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.568744. [PMID: 38077010 PMCID: PMC10705259 DOI: 10.1101/2023.11.28.568744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.
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Affiliation(s)
- Andrew N. Van
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Thomas Madison
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Jarod L. Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - Kimberly B. Weldon
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63130
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M. Nelson
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Scott A. Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Nico U.F. Dosenbach
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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13
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Moser J, Koirala S, Madison T, Labonte AK, Carrasco CM, Feczko E, Moore LA, Ahmed W, Myers MJ, Yacoub E, Trevo-Clemmens B, Larsen B, Laumann TO, Nelson SM, Vizioli L, Sylvester CM, Fair DA. Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564416. [PMID: 37961636 PMCID: PMC10634909 DOI: 10.1101/2023.10.27.564416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.
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Affiliation(s)
- Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Madison
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Weli Ahmed
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Brenden Trevo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Bart Larsen
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Taylor Family Institute for Innovative Research, Washington University in St. Louis, St. Louis, Missouri, 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
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14
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D'Andrea CB, Laumann TO, Newbold DJ, Nelson SM, Nielsen AN, Chauvin R, Marek S, Greene DJ, Dosenbach NUF, Gordon EM. Substructure of the brain's Cingulo-Opercular network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561772. [PMID: 37873065 PMCID: PMC10592749 DOI: 10.1101/2023.10.10.561772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.
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Affiliation(s)
- Carolina Badke D'Andrea
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO 63310, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Dillan J Newbold
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota 55455, USA
| | - Ashley N Nielsen
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Roselyne Chauvin
- Department of Neurology, New York University Medical Center, New York, New York 10016, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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15
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Rajesh A, Seider NA, Newbold DJ, Adeyemo B, Marek S, Greene DJ, Snyder AZ, Shimony JS, Laumann TO, Dosenbach NUF, Gordon EM. Structure-Function Coupling in Highly Sampled Individual Brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.04.560909. [PMID: 37873167 PMCID: PMC10592963 DOI: 10.1101/2023.10.04.560909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Structural connections (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to structural connections may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 minutes of diffusion-weighted MRI for SC and 360 minutes of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas. SIGNIFICANCE STATEMENT Structural connections between distant regions of the human brain support networked function that enables cognition and behavior. Improving our understanding of how structure enables function could allow better insight into how brain disconnection injuries impair brain function.Previous work using neuroimaging suggested that structure-function relationships vary systematically across the brain, with structure better explaining function in basic visual/motor areas than in higher-order areas. However, this work was conducted in group-averaged data, which may obscure details of individual-specific structure-function relationships.Using individual-specific densely sampled neuroimaging data, we found that in addition to visual/motor regions, structure strongly predicts function in specific circuits of the higher-order cingulate gyrus. The cingulate's structure-function relationship suggests that its organization may be unique among higher-order cortical regions.
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16
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Larsen B, Sydnor VJ, Keller AS, Yeo BTT, Satterthwaite TD. A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment. Trends Neurosci 2023; 46:847-862. [PMID: 37643932 PMCID: PMC10530452 DOI: 10.1016/j.tins.2023.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/23/2023] [Accepted: 07/25/2023] [Indexed: 08/31/2023]
Abstract
To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC), and Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Kraus B, Zinbarg R, Braga RM, Nusslock R, Mittal VA, Gratton C. Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neurosci Biobehav Rev 2023; 152:105259. [PMID: 37268180 PMCID: PMC10527506 DOI: 10.1016/j.neubiorev.2023.105259] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.
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Affiliation(s)
- Brian Kraus
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Richard Zinbarg
- Department of Psychology, Northwestern University, Evanston, IL, USA; The Family Institute at Northwestern University, Evanston, IL, USA
| | - Rodrigo M Braga
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA; Institute for Policy Research, Northwestern University, Evanston, IL, USA; Institute for Innovations in Developmental Sciences (DevSci), Northwestern University, Chicago, IL, USA; Northwestern University, Department of Psychiatry, Chicago, IL, USA; Northwestern University, Medical Social Sciences, Chicago, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychology, Florida State University, Tallahassee, FL, USA; Program in Neuroscience, Florida State University, Tallahassee, FL, USA
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18
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Li J, Yu X, Zou Y, Leng Y, Yang F, Liu B, Fan W. Altered static and dynamic intrinsic brain activity in unilateral sudden sensorineural hearing loss. Front Neurosci 2023; 17:1257729. [PMID: 37719156 PMCID: PMC10500124 DOI: 10.3389/fnins.2023.1257729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/09/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Sudden sensorineural hearing loss (SSHL) is a critical otologic emergency characterized by a rapid decline of at least 30 dB across three consecutive frequencies in the pure-tone audiogram within a 72-hour period. This audiological condition has been associated with alterations in brain cortical and subcortical structures, as well as changes in brain functional activities involving multiple networks. However, the extent of cerebral intrinsic brain activity disruption in SSHL remains poorly understood. The aimed of this study is to investigate intrinsic brain activity alterations in SSHL using static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) analysis. Methods Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a cohort of SSHL patients (unilateral, n = 102) and healthy controls (n = 73). Static and dynamic fALFF methods were employed to analyze the acquired fMRI data, enabling a comprehensive examination of intrinsic brain activity changes in SSHL. Results Our analysis revealed significant differences in static fALFF patterns between SSHL patients and healthy controls. SSHL patients exhibited decreased fALFF in the left fusiform gyrus, left precentral gyrus, and right inferior frontal gyrus, alongside increased fALFF in the left inferior frontal gyrus, left superior frontal gyrus, and right middle temporal gyrus. Additionally, dynamic fALFF analysis demonstrated elevated fALFF in the right superior frontal gyrus and right middle frontal gyrus among SSHL patients. Intriguingly, we observed a positive correlation between static fALFF in the left fusiform gyrus and the duration of hearing loss, shedding light on potential temporal dynamics associated with intrinsic brain activity changes. Discussion The observed disruptions in intrinsic brain activity and temporal dynamics among SSHL patients provide valuable insights into the functional reorganization and potential compensatory mechanisms linked to hearing loss. These findings underscore the importance of understanding the underlying neural alterations in SSHL, which could pave the way for the development of targeted interventions and rehabilitation strategies aimed at optimizing SSHL management.
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Affiliation(s)
- Jing Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaocheng Yu
- Department of Thyroid and Breast Surgery, Wuhan No. 1 Hospital, Wuhan, China
| | - Yan Zou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yangming Leng
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Bo Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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19
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Siegel JS, Subramanian S, Perry D, Kay B, Gordon E, Laumann T, Reneau R, Gratton C, Horan C, Metcalf N, Chacko R, Schweiger J, Wong D, Bender D, Padawer-Curry J, Raison C, Raichle M, Lenze EJ, Snyder AZ, Dosenbach NUF, Nicol G. Psilocybin desynchronizes brain networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.22.23294131. [PMID: 37701731 PMCID: PMC10493007 DOI: 10.1101/2023.08.22.23294131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
1The relationship between the acute effects of psychedelics and their persisting neurobiological and psychological effects is poorly understood. Here, we tracked brain changes with longitudinal precision functional mapping in healthy adults before, during, and for up to 3 weeks after oral psilocybin and methylphenidate (17 MRI visits per participant) and again 6+ months later. Psilocybin disrupted connectivity across cortical networks and subcortical structures, producing more than 3-fold greater acute changes in functional networks than methylphenidate. These changes were driven by desynchronization of brain activity across spatial scales (area, network, whole brain). Psilocybin-driven desynchronization was observed across association cortex but strongest in the default mode network (DMN), which is connected to the anterior hippocampus and thought to create our sense of self. Performing a perceptual task reduced psilocybin-induced network changes, suggesting a neurobiological basis for grounding, connecting with physical reality during psychedelic therapy. The acute brain effects of psilocybin are consistent with distortions of space-time and the self. Psilocybin induced persistent decrease in functional connectivity between the anterior hippocampus and cortex (and DMN in particular), lasting for weeks but normalizing after 6 months. Persistent suppression of hippocampal-DMN connectivity represents a candidate neuroanatomical and mechanistic correlate for psilocybin's pro-plasticity and anti-depressant effects.
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20
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Kwon Y, Salvo JJ, Anderson N, Holubecki AM, Lakshman M, Yoo K, Kay K, Gratton C, Braga RM. Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553585. [PMID: 37645962 PMCID: PMC10462098 DOI: 10.1101/2023.08.16.553585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.
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Affiliation(s)
- Y Kwon
- Northwestern University Department of Neurology
| | - J J Salvo
- Northwestern University Department of Neurology
| | - N Anderson
- Northwestern University Department of Neurology
| | | | - M Lakshman
- Northwestern University Department of Neurology
| | - K Yoo
- Yale University Department of Psychology
| | - K Kay
- University of Minnesota Department of Radiology
| | - C Gratton
- Florida State University Department of Psychology
| | - R M Braga
- Northwestern University Department of Neurology
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21
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Lynch CJ, Elbau I, Ng T, Ayaz A, Zhu S, Manfredi N, Johnson M, Wolk D, Power JD, Gordon EM, Kay K, Aloysi A, Moia S, Caballero-Gaudes C, Victoria LW, Solomonov N, Goldwaser E, Zebley B, Grosenick L, Downar J, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Williams N, Gunning FM, Liston C. Expansion of a frontostriatal salience network in individuals with depression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.09.551651. [PMID: 37645792 PMCID: PMC10461904 DOI: 10.1101/2023.08.09.551651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable 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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22
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Abstract
This Viewpoint describes how precision functional mapping may be helpful for associating neuroanatomical regions with specific psychiatric disorders.
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Affiliation(s)
- Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
| | - Charles F Zorumski
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
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23
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Patel GH, Gruskin DC, Arkin SC, Jamerson EC, Ruiz-Betancourt DR, Klim CC, Sanchez-Peña JP, Bartel LP, Lee JK, Grinband J, Martinez A, Berman RA, Ochsner KN, Leopold DA, Javitt DC. The Road Not Taken: Disconnection of a Human-Unique Cortical Pathway Underlying Naturalistic Social Perception in Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:398-408. [PMID: 37519457 PMCID: PMC10382708 DOI: 10.1016/j.bpsgos.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/02/2022] [Accepted: 03/06/2022] [Indexed: 11/18/2022] Open
Abstract
Background Efficient processing of complex and dynamic social scenes relies on intact connectivity of many underlying cortical areas and networks, but how connectivity anomalies affect the neural substrates of social perception remains unknown. Here we measured these relationships using functionally based localization of social perception areas, resting-state functional connectivity, and movie-watching data. Methods In 42 participants with schizophrenia (SzPs) and 41 healthy control subjects, we measured the functional connectivity of areas localized by face-emotion processing, theory-of-mind (ToM), and attention tasks. We quantified the weighted shortest path length between visual and medial prefrontal ToM areas in both populations to assess the impact of these changes in functional connectivity on network structure. We then correlated connectivity along the shortest path in each group with movie-evoked activity in a key node of the ToM network (posterior temporoparietal junction [TPJp]). Results SzPs had pronounced decreases in connectivity in TPJ/posterior superior temporal sulcus (TPJ-pSTS) areas involved in face-emotion processing (t81 = 4.4, p = .00002). In healthy control subjects, the shortest path connecting visual and medial prefrontal ToM areas passed through TPJ-pSTS, whereas in SzPs, the shortest path passed through the prefrontal cortex. While movie-evoked TPJp activity correlated with connectivity along the TPJ-pSTS pathway in both groups (r = 0.43, p = .002), it additionally correlated with connectivity along the prefrontal cortex pathway only in SzPs (rSzP = 0.56, p = .003). Conclusions These results suggest that connectivity along the human-unique TPJ-pSTS pathway affects both the network architecture and functioning of areas involved in processing complex dynamic social scenes. These results demonstrate how focal connectivity anomalies can have widespread impacts across the cortex.
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Affiliation(s)
- Gaurav H. Patel
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - David C. Gruskin
- Department of Psychiatry, Columbia University, New York, New York
| | - Sophie C. Arkin
- University of California, Los Angeles, Los Angeles, California
| | | | | | | | - Juan P. Sanchez-Peña
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Laura P. Bartel
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Jessica K. Lee
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Jack Grinband
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
| | - Antígona Martinez
- Department of Psychiatry, Columbia University, New York, New York
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Rebecca A. Berman
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, Maryland
| | - Kevin N. Ochsner
- Department of Psychiatry, Columbia University, New York, New York
| | - David A. Leopold
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, Maryland
| | - Daniel C. Javitt
- Department of Psychiatry, Columbia University, New York, New York
- Experimental Therapeutics, New York State Psychiatric Institute, New York, New York
- Schizophrenia Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
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24
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Gordon EM, Chauvin RJ, Van AN, Rajesh A, Nielsen A, Newbold DJ, Lynch CJ, Seider NA, Krimmel SR, Scheidter KM, Monk J, Miller RL, Metoki A, Montez DF, Zheng A, Elbau I, Madison T, Nishino T, Myers MJ, Kaplan S, Badke D'Andrea C, Demeter DV, Feigelis M, Ramirez JSB, Xu T, Barch DM, Smyser CD, Rogers CE, Zimmermann J, Botteron KN, Pruett JR, Willie JT, Brunner P, Shimony JS, Kay BP, Marek S, Norris SA, Gratton C, Sylvester CM, Power JD, Liston C, Greene DJ, Roland JL, Petersen SE, Raichle ME, Laumann TO, Fair DA, Dosenbach NUF. A somato-cognitive action network alternates with effector regions in motor cortex. Nature 2023; 617:351-359. [PMID: 37076628 PMCID: PMC10172144 DOI: 10.1038/s41586-023-05964-2] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.
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Affiliation(s)
- Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
| | - Roselyne J Chauvin
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
| | - Aishwarya Rajesh
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Ashley Nielsen
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, New York University Langone Medical Center, New York, NY, USA
| | - Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Julia Monk
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Ryland L Miller
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Immanuel Elbau
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Thomas Madison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Carolina Badke D'Andrea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Matthew Feigelis
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Julian S B Ramirez
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Deanna M Barch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
| | - Christopher D Smyser
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Kelly N Botteron
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - John R Pruett
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jon T Willie
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Peter Brunner
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
- Department of Neurosurgery, 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
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Scott A Norris
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Jarod L Roland
- Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of 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
| | - Marcus E Raichle
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of 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
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, 55455, United States
| | - Nico U F Dosenbach
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA.
- Department of 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.
- Program in Occupational Therapy, Washington University in St. Louis, St Louis, MO, USA.
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25
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McGowan AL, Sayed F, Boyd ZM, Jovanova M, Kang Y, Speer ME, Cosme D, Mucha PJ, Ochsner KN, Bassett DS, Falk EB, Lydon-Staley DM. Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones. Biol Psychiatry 2023; 93:681-689. [PMID: 36797176 PMCID: PMC10038886 DOI: 10.1016/j.biopsych.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.
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Affiliation(s)
- Amanda L McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Concordia University, Montréal, Québec, Canada
| | - Farah Sayed
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zachary M Boyd
- Department of Mathematics, Brigham Young University, Provo, Utah
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Megan E Speer
- Department of Psychology, Columbia University, New York, New York
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, New York
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; Operations, Information and Decisions, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.
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26
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Tambini A, Miller J, Ehlert L, Kiyonaga A, D’Esposito M. Structured memory representations develop at multiple time scales in hippocampal-cortical networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.06.535935. [PMID: 37066263 PMCID: PMC10104124 DOI: 10.1101/2023.04.06.535935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.
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Affiliation(s)
- Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY
| | - Jacob Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT
| | - Luke Ehlert
- Department of Neurobiology and Behavior, University of California. Irvine, CA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA
| | - Mark D’Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA
- Department of Psychology, University of California, Berkeley, CA
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27
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Sydnor VJ, Larsen B, Seidlitz J, Adebimpe A, Alexander-Bloch AF, Bassett DS, Bertolero MA, Cieslak M, Covitz S, Fan Y, Gur RE, Gur RC, Mackey AP, Moore TM, Roalf DR, Shinohara RT, Satterthwaite TD. Intrinsic activity development unfolds along a sensorimotor-association cortical axis in youth. Nat Neurosci 2023; 26:638-649. [PMID: 36973514 PMCID: PMC10406167 DOI: 10.1038/s41593-023-01282-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023]
Abstract
Animal studies of neurodevelopment have shown that recordings of intrinsic cortical activity evolve from synchronized and high amplitude to sparse and low amplitude as plasticity declines and the cortex matures. Leveraging resting-state functional MRI (fMRI) data from 1,033 youths (ages 8-23 years), we find that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental change. Declines in the amplitude of intrinsic fMRI activity were initiated heterochronously across regions and were coupled to the maturation of intracortical myelin, a developmental plasticity regulator. Spatiotemporal variability in regional developmental trajectories was organized along a hierarchical, sensorimotor-association cortical axis from ages 8 to 18. The sensorimotor-association axis furthermore captured variation in associations between youths' neighborhood environments and intrinsic fMRI activity; associations suggest that the effects of environmental disadvantage on the maturing brain diverge most across this axis during midadolescence. These results uncover a hierarchical neurodevelopmental axis and offer insight into the progression of cortical plasticity in humans.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), 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
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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28
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Taylor NL, Shine JM. A whole new world: embracing the systems-level to understand the indirect impact of pathology in neurodegenerative disorders. J Neurol 2023; 270:1969-1975. [PMID: 36577819 DOI: 10.1007/s00415-022-11550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022]
Abstract
The direct link between neuropathology and the symptoms that emerge from damage to the brain is often difficult to discern. In this perspective, we argue that a satisfying account of neurodegenerative symptoms most naturally emerges from the consideration of the brain from the systems-level. Specifically, we will highlight the role of the neuromodulatory arousal system, which is uniquely positioned to coordinate the brain's ability to flexibly integrate the otherwise segregated structures required to support higher cognitive functions. Importantly, the neuromodulatory arousal system is highly heterogeneous, encompassing structures that are common sites of neurodegeneration across Alzheimer's and Parkinson's disease. We will review studies that implicate the dysfunctional interactions amongst distributed brain regions as a side-effect of pathological involvement of the neuromodulatory arousal system in these neurodegenerative disorders. From this perspective, we will argue that future work in clinical neuroscience should attempt to consider the inherent complexity in the brain and employ analytic techniques that do not solely focus on regional functional impairments, but rather captures the brain as an inherently dynamic, distributed, multi-scale system. Through this lens, we hope that we will devise new and improved diagnostic markers and interventional approaches to aid in the treatment of neurodegenerative disorders.
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Affiliation(s)
- Natasha L Taylor
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - James M Shine
- Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
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29
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Montez DF, Van AN, Miller RL, Seider NA, Marek S, Zheng A, Newbold DJ, Scheidter K, Feczko E, Perrone AJ, Miranda-Dominguez O, Earl EA, Kay BP, Jha AK, Sotiras A, Laumann TO, Greene DJ, Gordon EM, Tisdall MD, van der Kouwe A, Fair DA, Dosenbach NUF. Using synthetic MR images for distortion correction. Dev Cogn Neurosci 2023; 60:101234. [PMID: 37023632 PMCID: PMC10106483 DOI: 10.1016/j.dcn.2023.101234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 04/07/2023] Open
Abstract
Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion and differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) images makes their alignment a challenge. Typically, field map data are used to correct EPI distortions. Alignments achieved with field maps can vary greatly and depends on the quality of field map data. However, many public datasets lack field map data entirely. Additionally, reliable field map data is often difficult to acquire in high-motion pediatric or developmental cohorts. To address this, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image with similar contrast properties to EPI data. This synthetic image acts as an effective reference for individual-specific distortion correction. Using pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club; HCP: Human Connectome Project) data, we demonstrate that Synth performs comparably to field map distortion correction approaches, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.
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Affiliation(s)
- David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America.
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland L Miller
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Kristen Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Eric A Earl
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abhinav K Jha
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla CA 92093, United States of America
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Andre van der Kouwe
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, United States of America; Department of Radiology, Harvard Medical School, Boston, MA 02115, United States of America
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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30
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Nielsen AN, Kaplan S, Meyer D, Alexopoulos D, Kenley JK, Smyser TA, Wakschlag LS, Norton ES, Raghuraman N, Warner BB, Shimony JS, Luby JL, Neil JJ, Petersen SE, Barch DM, Rogers CE, Sylvester CM, Smyser CD. Maturation of large-scale brain systems over the first month of life. Cereb Cortex 2023; 33:2788-2803. [PMID: 35750056 PMCID: PMC10016041 DOI: 10.1093/cercor/bhac242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/29/2022] [Accepted: 05/23/2022] [Indexed: 01/14/2023] Open
Abstract
The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.
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Affiliation(s)
- Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Dominique Meyer
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Tara A Smyser
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Lauren S Wakschlag
- Institute for Innovations and Developmental Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Medical Social Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Feinberg School of Medicine, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
| | - Elizabeth S Norton
- Institute for Innovations and Developmental Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Medical Social Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Communication Sciences and Disorders, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Jeffery J Neil
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Steven E Petersen
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Cynthia E Rogers
- Department of Communication Sciences and Disorders, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Pediatrics, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
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Elbau IG, Lynch CJ, Downar J, Vila-Rodriguez F, Power JD, Solomonov N, Daskalakis ZJ, Blumberger DM, Liston C. Functional Connectivity Mapping for rTMS Target Selection in Depression. Am J Psychiatry 2023; 180:230-240. [PMID: 36855880 DOI: 10.1176/appi.ajp.20220306] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
OBJECTIVE Repetitive transcranial magnetic stimulation (rTMS) protocols increasingly use subgenual anterior cingulate cortex (sgACC) functional connectivity to individualize treatment targets. However, the efficacy of this approach is unclear, with conflicting findings and varying effect sizes across studies. Here, the authors investigated the effect of the stimulation site's functional connectivity with the sgACC (sgACC-StimFC) on treatment outcome to rTMS in 295 patients with major depression. METHODS The reliability and accuracy of estimating sgACC functional connectivity were validated with data from individuals who underwent extensive functional MRI testing. Electric field modeling was used to analyze associations between sgACC-StimFC and clinical improvement using standardized assessments and to evaluate sources of heterogeneity. RESULTS An imputation-based method provided reliable and accurate sgACC functional connectivity estimates. Treatment responses weakly but robustly correlated with sgACC-StimFC (r=-0.16), but only when the stimulated cortex was identified using electric field modeling. Surprisingly, this association was driven by patients with strong global signal fluctuations stemming from a specific periodic respiratory pattern (r=-0.49). CONCLUSIONS Functional connectivity between the sgACC and the stimulated cortex was correlated with individual differences in treatment outcomes, but the association was weaker than those observed in previous studies and was accentuated in a subgroup of patients with distinct, respiration-related signal patterns in their scans. These findings indicate that in a large representative sample of patients with major depressive disorder, individual differences in sgACC-StimFC explained only ∼3% of the variance in outcomes, which may limit the utility of existing sgACC-based targeting protocols. However, these data also provide strong evidence for a true-albeit small-effect and highlight opportunities for incorporating additional functional connectivity measures to generate models of rTMS response with enhanced predictive power.
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Affiliation(s)
- Immanuel G Elbau
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Jonathan Downar
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Fidel Vila-Rodriguez
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Jonathan D Power
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Nili Solomonov
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Zafiris J Daskalakis
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Daniel M Blumberger
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York (Elbau, Lynch, Power, Solomonov, Liston); Department of Psychiatry and Institute of Medical Science, Faculty of Medicine, University of Toronto, and Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto (Downar, Blumberger); Non-Invasive Neurostimulation Therapies Lab and Department of Psychiatry, University of British Columbia, Vancouver (Vila-Rodriguez); Department of Psychiatry, University of California, San Diego (Daskalakis)
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Krohn S, von Schwanenflug N, Waschke L, Romanello A, Gell M, Garrett DD, Finke C. A spatiotemporal complexity architecture of human brain activity. SCIENCE ADVANCES 2023; 9:eabq3851. [PMID: 36724223 PMCID: PMC9891702 DOI: 10.1126/sciadv.abq3851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.
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Affiliation(s)
- Stephan Krohn
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Corresponding author. (S.K.); (C.F.)
| | - Nina von Schwanenflug
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leonhard Waschke
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Amy Romanello
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Gell
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - Douglas D. Garrett
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Carsten Finke
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Corresponding author. (S.K.); (C.F.)
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Palenciano AF, Senoussi M, Formica S, González-García C. Canonical template tracking: Measuring the activation state of specific neural representations. FRONTIERS IN NEUROIMAGING 2023; 1:974927. [PMID: 37555182 PMCID: PMC10406196 DOI: 10.3389/fnimg.2022.974927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/13/2022] [Indexed: 08/10/2023]
Abstract
Multivariate analyses of neural data have become increasingly influential in cognitive neuroscience since they allow to address questions about the representational signatures of neurocognitive phenomena. Here, we describe Canonical Template Tracking: a multivariate approach that employs independent localizer tasks to assess the activation state of specific representations during the execution of cognitive paradigms. We illustrate the benefits of this methodology in characterizing the particular content and format of task-induced representations, comparing it with standard (cross-)decoding and representational similarity analyses. Then, we discuss relevant design decisions for experiments using this analysis approach, focusing on the nature of the localizer tasks from which the canonical templates are derived. We further provide a step-by-step tutorial of this method, stressing the relevant analysis choices for functional magnetic resonance imaging and magneto/electroencephalography data. Importantly, we point out the potential pitfalls linked to canonical template tracking implementation and interpretation of the results, together with recommendations to mitigate them. To conclude, we provide some examples from previous literature that highlight the potential of this analysis to address relevant theoretical questions in cognitive neuroscience.
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Affiliation(s)
- Ana F. Palenciano
- Mind, Brain, and Behavior Research Center, University of Granada, Granada, Spain
| | - Mehdi Senoussi
- CLLE Lab, CNRS UMR 5263, University of Toulouse, Toulouse, France
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Silvia Formica
- Department of Psychology, Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany
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Siegel JS, Nicol GE. Plasticity markers in the human brain associated with rapid antidepressants. Neuropsychopharmacology 2023; 48:223-224. [PMID: 35927506 PMCID: PMC9700761 DOI: 10.1038/s41386-022-01400-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, Washington University in Saint Louis, 600 South Taylor Avenue, Saint Louis, MO, 63110, USA.
| | - Ginger E Nicol
- Department of Psychiatry, Washington University in Saint Louis, 600 South Taylor Avenue, Saint Louis, MO, 63110, USA
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35
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Sirago G, Pellegrino MA, Bottinelli R, Franchi MV, Narici MV. Loss of neuromuscular junction integrity and muscle atrophy in skeletal muscle disuse. Ageing Res Rev 2023; 83:101810. [PMID: 36471545 DOI: 10.1016/j.arr.2022.101810] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 11/27/2022]
Abstract
Physical inactivity (PI) is a major risk factor of chronic diseases. A major aspect of PI is loss of muscle mass and strength. The latter phenomenon significantly impacts daily life and represent a major issue for global health. Understandably, skeletal muscle itself has been the major focus of studies aimed at understanding the mechanisms underlying loss of mass and strength. Relatively lesser attention has been given to the contribution of alterations in somatomotor control, despite the fact that these changes can start very early and can occur at multiple levels, from the cortex down to the neuromuscular junction (NMJ). It is well known that exposure to chronic inactivity or immobilization causes a disproportionate loss of force compared to muscle mass, i.e. a loss of specific or intrinsic whole muscle force. The latter phenomenon may be partially explained by the loss of specific force of individual muscle fibres, but several other players are very likely to contribute to such detrimental phenomenon. Irrespective of the length of the disuse period, the loss of force is, in fact, more than two-fold greater than that of muscle size. It is very likely that somatomotor alterations may contribute to this loss in intrinsic muscle force. Here we review evidence that alterations of one component of somatomotor control, namely the neuromuscular junction, occur in disuse. We also discuss some of the novel players in NMJ stability (e.g., homer, bassoon, pannexin) and the importance of new established and emerging molecular markers of neurodegenerative processes in humans such as agrin, neural-cell adhesion molecule and light-chain neurofilaments.
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Affiliation(s)
- Giuseppe Sirago
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy.
| | - Maria A Pellegrino
- Department of Molecular Medicine, University of Pavia, Pavia 27100, Italy
| | - Roberto Bottinelli
- Department of Molecular Medicine, University of Pavia, Pavia 27100, Italy; IRCCS Mondino Foundation, Pavia 27100, Italy
| | - Martino V Franchi
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | - Marco V Narici
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy; CIR-MYO Myology Center, University of Padova, Padova 35131, Italy.
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36
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Miller JA, Tambini A, Kiyonaga A, D'Esposito M. Long-term learning transforms prefrontal cortex representations during working memory. Neuron 2022; 110:3805-3819.e6. [PMID: 36240768 PMCID: PMC9768795 DOI: 10.1016/j.neuron.2022.09.019] [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: 02/23/2022] [Revised: 06/28/2022] [Accepted: 09/14/2022] [Indexed: 11/06/2022]
Abstract
The role of the lateral prefrontal cortex (lPFC) in working memory (WM) is debated. Non-human primate (NHP) electrophysiology shows that the lPFC stores WM representations, but human neuroimaging suggests that the lPFC controls WM content in sensory cortices. These accounts are confounded by differences in task training and stimulus exposure. We tested whether long-term training alters lPFC function by densely sampling WM activity using functional MRI. Over 3 months, participants trained on both a WM and serial reaction time (SRT) task, wherein fractal stimuli were embedded within sequences. WM performance improved for trained (but not novel) fractals and, neurally, delay activity increased in distributed lPFC voxels across learning. Item-level WM representations became detectable within lPFC patterns, and lPFC activity reflected sequence relationships from the SRT task. These findings demonstrate that human lPFC develops stimulus-selective responses with learning, and WM representations are shaped by long-term experience, which could reconcile competing accounts of WM functioning.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT, USA.
| | - Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, CA, USA
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Bosak N, Branco P, Kuperman P, Buxbaum C, Cohen RM, Fadel S, Zubeidat R, Hadad R, Lawen A, Saadon‐Grosman N, Sterling M, Granovsky Y, Apkarian AV, Yarnitsky D, Kahn I. Brain Connectivity Predicts Chronic Pain in Acute Mild Traumatic Brain Injury. Ann Neurol 2022; 92:819-833. [PMID: 36082761 PMCID: PMC9826527 DOI: 10.1002/ana.26463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVES Previous studies have established the role of the cortico-mesolimbic and descending pain modulation systems in chronic pain prediction. Mild traumatic brain injury (mTBI) is an acute pain model where chronic pain is prevalent and complicated for prediction. In this study, we set out to study whether functional connectivity (FC) of the nucleus accumbens (NAc) and the periaqueductal gray matter (PAG) is predictive of pain chronification in early-acute mTBI. METHODS To estimate FC, resting-state functional magnetic resonance imaging (fMRI) of 105 participants with mTBI following a motor vehicle collision was acquired within 72 hours post-accident. Participants were classified according to pain ratings provided at 12-months post-collision into chronic pain (head/neck pain ≥30/100, n = 44) and recovery (n = 61) groups, and their FC maps were compared. RESULTS The chronic pain group exhibited reduced negative FC between NAc and a region within the primary motor cortex corresponding with the expected representation of the area of injury. A complementary pattern was also demonstrated between PAG and the primary somatosensory cortex. PAG and NAc also shared increased FC to the rostral anterior cingulate cortex (rACC) within the recovery group. Brain connectivity further shows high classification accuracy (area under the curve [AUC] = .86) for future chronic pain, when combined with an acute pain intensity report. INTERPRETATION FC features obtained shortly after mTBI predict its transition to long-term chronic pain, and may reflect an underlying interaction of injury-related primary sensorimotor cortical areas with the mesolimbic and pain modulation systems. Our findings indicate a potential predictive biomarker and highlight targets for future early preventive interventions. ANN NEUROL 2022;92:819-833.
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Affiliation(s)
- Noam Bosak
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael,Department of NeurologyRambam Health Care CampusHaifaIsrael
| | - Paulo Branco
- Department of NeuroscienceNorthwestern University Medical SchoolChicagoIL
| | - Pora Kuperman
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
| | - Chen Buxbaum
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael,Department of NeurologyRambam Health Care CampusHaifaIsrael
| | - Ruth Manor Cohen
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
| | - Shiri Fadel
- Department of NeurologyRambam Health Care CampusHaifaIsrael
| | - Rabab Zubeidat
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
| | - Rafi Hadad
- Department of NeurologyRambam Health Care CampusHaifaIsrael
| | - Amir Lawen
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
| | - Noam Saadon‐Grosman
- Department of Medical Neurobiology, Faculty of MedicineThe Hebrew UniversityJerusalemIsrael
| | - Michele Sterling
- RECOVER Injury Research Centre, NHMRC Centre of Research Excellence in Road Traffic Injury RecoveryThe University of QueenslandBrisbaneAustralia
| | - Yelena Granovsky
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
| | | | - David Yarnitsky
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael,Department of NeurologyRambam Health Care CampusHaifaIsrael
| | - Itamar Kahn
- Rappaport Faculty of MedicineTechnion – Israel Institute of TechnologyHaifaIsrael
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Ladwig Z, Seitzman BA, Dworetsky A, Yu Y, Adeyemo B, Smith DM, Petersen SE, Gratton C. BOLD cofluctuation 'events' are predicted from static functional connectivity. Neuroimage 2022; 260:119476. [PMID: 35842100 PMCID: PMC9428936 DOI: 10.1016/j.neuroimage.2022.119476] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/09/2022] [Accepted: 07/12/2022] [Indexed: 11/17/2022] Open
Abstract
Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure.
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Affiliation(s)
- Zach Ladwig
- Interdepartmental Neuroscience Program, Northwestern University
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University St. Louis School of Medicine
| | | | - Yuhua Yu
- Department of Psychology, Northwestern University
| | - Babatunde Adeyemo
- Department of Neurology, Washington University St. Louis School of Medicine
| | - Derek M Smith
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine
| | - Steven E Petersen
- Department of Radiology, Washington University St. Louis School of Medicine; Department of Neurology, Washington University St. Louis School of Medicine; Department of Psychological and Brain Sciences, Washington University St. Louis School of Medicine; Department of Neuroscience, Washington University St. Louis School of Medicine; Department of Biomedical Engineering, Washington University St. Louis School of Medicine
| | - Caterina Gratton
- Interdepartmental Neuroscience Program, Northwestern University; Department of Psychology, Northwestern University; Department of Neurology, Northwestern University.
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Krane NA, Loyo M, Pollock J, Hill M, Johnson CZ, Stevens AA. Exploratory Study of the Brain Response in Facial Synkinesis after Bell Palsy with Systematic Review and Meta-analysis of the Literature. AJNR Am J Neuroradiol 2022; 43:1470-1475. [PMID: 36574328 PMCID: PMC9575525 DOI: 10.3174/ajnr.a7619] [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: 01/24/2022] [Accepted: 06/28/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Facial synkinesis, characterized by unintentional facial movements paired with intentional movements, is a debilitating sequela of Bell palsy. PURPOSE Our aim was to determine whether persistent peripheral nerve changes arising from Bell palsy result in persistent altered brain function in motor pathways in synkinesis. DATA SOURCES A literature search using terms related to facial paralysis, Bell palsy, synkinesis, and fMRI through May 2021 was conducted in MEDLINE and EMBASE. Additionally, an fMRI study examined lip and eyeblink movements in 2 groups: individuals who fully recovered following Bell palsy and individuals who developed synkinesis. STUDY SELECTION Task-based data of the whole brain that required lip movements in healthy controls were extracted from 7 publications. Three studies contributed similar whole-brain analyses in acute Bell palsy. DATA ANALYSIS The meta-analysis of fMRI in healthy control and Bell palsy groups determined common clusters of activation within each group using activation likelihood estimates. A separate fMRI study used multivariate general linear modeling to identify changes associated with synkinesis in smiling and blinking tasks. DATA SYNTHESIS A region of the precentral gyrus contralateral to the paretic side of the face was hypoactive in synkinesis during lip movements compared with controls. This region was centered in a cluster of activation identified in the meta-analysis of the healthy controls but absent from individuals with Bell palsy. LIMITATIONS The meta-analysis relied on a small set of studies. The small sample of subjects with synkinesis limited the power of the fMRI analysis. CONCLUSIONS Premotor pathways show persistent functional changes in synkinesis first identifiable in acute Bell palsy.
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Affiliation(s)
- N A Krane
- From the Division of Facial Plastic and Reconstructive Surgery (N.A.K., M.L., C.Z.J.), Department of Otolaryngology-Head and Neck Surgery
| | - M Loyo
- From the Division of Facial Plastic and Reconstructive Surgery (N.A.K., M.L., C.Z.J.), Department of Otolaryngology-Head and Neck Surgery
| | - J Pollock
- Division of Neuroradiology (J.P.), Department of Diagnostic Radiology
| | - M Hill
- Department of Otolaryngology-Head and Neck Surgery (M.H.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - C Z Johnson
- From the Division of Facial Plastic and Reconstructive Surgery (N.A.K., M.L., C.Z.J.), Department of Otolaryngology-Head and Neck Surgery
| | - A A Stevens
- Advanced Imaging Research Center (A.A.S.), Oregon Health & Science University, Portland, Oregon
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40
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Skwara AC, King BG, Zanesco AP, Saron CD. Shifting Baselines: Longitudinal Reductions in EEG Beta Band Power Characterize Resting Brain Activity with Intensive Meditation. Mindfulness (N Y) 2022; 13:2488-2506. [PMID: 36258902 PMCID: PMC9568471 DOI: 10.1007/s12671-022-01974-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 11/18/2022]
Abstract
Objectives A core assumption of meditation training is that cognitive capacities developed during formal practice will transfer to other contexts or activities as expertise develops over time. This implies that meditation training might influence domain-general neurocognitive systems, the spontaneous activity of which should be reflected in the dynamics of the resting brain. Previous research has demonstrated that 3 months of meditation training led to reductions in EEG beta band power during mindfulness of breathing practice. The current study extends these findings to ask whether concomitant shifts in power are observed during 2 min of eyes closed rest, when participants are not explicitly engaged in formal meditation. Methods Experienced meditation practitioners were randomly assigned to practice 3 months of focused attention meditation in a residential retreat, or to serve as waitlist controls. The waitlist controls later completed their own 3-month retreat. Permutation-based cluster analysis of 88-channel resting EEG data was used to test for spectral changes in spontaneous brain activity over the course of the retreats. Results Longitudinal reductions in EEG power in the beta frequency range were identified and replicated across the two independent training periods. Less robust reductions were also observed in the high alpha frequency range, and in individual peak alpha frequency. These changes closely mirror those previously observed during formal mindfulness of breathing meditation practice. Conclusions These findings suggest that the neurocognitive effects of meditation training can extend beyond the bounds of formal practice, influencing the spontaneous activity of the resting brain. Rather than serving as an invariant baseline, resting states might carry meaningful training-related effects, blurring the line between state and trait change. Supplementary Information The online version contains supplementary material available at 10.1007/s12671-022-01974-9.
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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Mill RD, Hamilton JL, Winfield EC, Lalta N, Chen RH, Cole MW. Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior. PLoS Biol 2022; 20:e3001686. [PMID: 35980898 PMCID: PMC9387855 DOI: 10.1371/journal.pbio.3001686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/24/2022] [Indexed: 11/21/2022] Open
Abstract
How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena. How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.
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Affiliation(s)
- Ravi D. Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- * E-mail:
| | - Julia L. Hamilton
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Emily C. Winfield
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Nicole Lalta
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
| | - Richard H. Chen
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
- Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, New Jersey, United States of America
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America
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Bandettini PA, Gonzalez-Castillo J, Handwerker D, Taylor P, Chen G, Thomas A. The challenge of BWAs: Unknown unknowns in feature space and variance. MED 2022; 3:526-531. [PMID: 35963233 DOI: 10.1016/j.medj.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The recent paper by Marek et al.1 has shown that, to capture brain-wide associations using fMRI and MRI measures, thousands of individuals are required. These results can be potentially misunderstood to imply that MRI or fMRI lack sensitivity or specificity. This commentary discusses the demonstrated sensitivity of fMRI and focuses on methodology that may allow improvements in BWA studies. While individual variation may be an ultimate constraint, refinements in acquisition, population selection, and processing may bring about higher correlations.
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Affiliation(s)
- Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA; Functional MRI Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Dan Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Paul Taylor
- Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Gang Chen
- Scientific and Statistical Computing Core Facility, National Institute of Mental Health, Bethesda, MD 20817, USA
| | - Adam Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20817, USA
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Xia CH, Barnett I, Tapera TM, Adebimpe A, Baker JT, Bassett DS, Brotman MA, Calkins ME, Cui Z, Leibenluft E, Linguiti S, Lydon-Staley DM, Martin ML, Moore TM, Murtha K, Piiwaa K, Pines A, Roalf DR, Rush-Goebel S, Wolf DH, Ungar LH, Satterthwaite TD. Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity. Neuropsychopharmacology 2022; 47:1662-1671. [PMID: 35660803 PMCID: PMC9163291 DOI: 10.1038/s41386-022-01351-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 05/18/2022] [Accepted: 05/23/2022] [Indexed: 11/09/2022]
Abstract
Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.
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Affiliation(s)
- Cedric Huchuan Xia
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ian Barnett
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Justin T Baker
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA
| | - Danielle S Bassett
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Santa Fe Institute, Santa Fe, NM, 87501, USA
| | - Melissa A Brotman
- National Institute of Mental Health, Intramural Research Program, Bethesda, MD, 20892, USA
| | - Monica E Calkins
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ellen Leibenluft
- National Institute of Mental Health, Intramural Research Program, Bethesda, MD, 20892, USA
| | - Sophia Linguiti
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David M Lydon-Staley
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Melissa Lynne Martin
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tyler M Moore
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kristin Murtha
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kayla Piiwaa
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sage Rush-Goebel
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel H Wolf
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lyle H Ungar
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Operations, Information and Decisions, Wharton School, Philadelphia, PA, 19104, USA.,Department of Psychology, School of Arts and Sciences, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Mapping correlated neurological deficits after stroke to distributed brain networks. Brain Struct Funct 2022; 227:3173-3187. [PMID: 35881254 DOI: 10.1007/s00429-022-02525-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 06/12/2022] [Indexed: 11/02/2022]
Abstract
Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.
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Seider NA, Adeyemo B, Miller R, Newbold DJ, Hampton JM, Scheidter KM, Rutlin J, Laumann TO, Roland JL, Montez DF, Van AN, Zheng A, Marek S, Kay BP, Bretthorst GL, Schlaggar BL, Greene DJ, Wang Y, Petersen SE, Barch DM, Gordon EM, Snyder AZ, Shimony JS, Dosenbach NUF. Accuracy and reliability of diffusion imaging models. Neuroimage 2022; 254:119138. [PMID: 35339687 PMCID: PMC9841915 DOI: 10.1016/j.neuroimage.2022.119138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/01/2022] [Accepted: 03/22/2022] [Indexed: 01/19/2023] Open
Abstract
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
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Affiliation(s)
- Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Kristen M Scheidter
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jarod L Roland
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO 63110 United States of America
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - G Larry Bretthorst
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Chemistry, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, United States of America; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States of America
| | - Yong Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Steven E Petersen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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How to establish robust brain-behavior relationships without thousands of individuals. Nat Neurosci 2022; 25:835-837. [PMID: 35710985 DOI: 10.1038/s41593-022-01110-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Szaflarski JP, Allendorfer JB, Goodman AM, Byington CG, Philip NS, Correia S, LaFrance WC. Diagnostic delay in functional seizures is associated with abnormal processing of facial emotions. Epilepsy Behav 2022; 131:108712. [PMID: 35526462 DOI: 10.1016/j.yebeh.2022.108712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/10/2022] [Accepted: 04/16/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE In patients with functional seizures (FS), delay in diagnosis (DD) may negatively affect outcomes. Altered brain responses to emotional stimuli have been shown in adults with FS. We hypothesized that DD would be associated with differential fMRI activation in emotion processing circuits. METHODS Fifty-two adults (38 females) with video-EEG confirmed FS prospectively completed assessments related to symptoms of depression (BDI-II), anxiety (BAI), post-traumatic stress disorder (PCL-S), a measure of how their symptoms affect day-to-day life (GAF), and fMRI at 3T with emotional faces task (EFT). During fMRI, subjects indicated "male" or "female" via button press while implicitly processing happy, sad, fearful, and neutral faces. Functional magnetic resonance imaging (FMRI) response to each emotion was modeled and group analyses were performed in AFNI within pre-specified regions-of-interest involved in emotion processing. A median split (507 days) defined short- (s-DD) and long-delay diagnosis (l-DD) groups. Voxelwise regression analyses were also performed to examine linear relationship between DD and emotion processing. FMRI signal was extracted from clusters showing group differences and Spearman's correlations assessed relationships with symptom scores. RESULTS Groups did not differ in FS age of onset, sex distribution, years of education, TBI characteristics, EFT in-scanner or post-test performance, or scores on the GAF, BDI-II, BAI, and PCL-S measures. The s-DD group was younger than l-DD (mean age 32.6 vs. 40.1; p = 0.022) at the time of study participation. After correcting for age, compared to s-DD, the l-DD group showed greater fMRI activation to sad faces in the bilateral posterior cingulate cortex (PCC) and to neutral faces in the right anterior insula. Within-group linear regression revealed that with increasing DD, there was increased fMRI activation to sad faces in the PCC and to happy faces in the right anterior insula/inferior frontal gyrus (AI/IFG). There were positive correlations between PCC response to sad faces and BDI-II scores in the l-DD group (rho = 0.48, p = 0.012) and the combined sample (rho = 0.30, p = 0.029). Increased PCC activation to sad faces in those in the l-DD group was associated with worse symptoms of depression (i.e. higher BDI-II score). CONCLUSIONS Delay in FS diagnosis is associated with fMRI changes in PCC and AI/IFG. As part of the default mode network, PCC is implicated in mood control, self-referencing, and other emotion-relevant processes. In our study, PCC changes are linked to depression. Future studies should assess the effects of interventions on these abnormalities.
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Affiliation(s)
- Jerzy P Szaflarski
- Department of Neurology, University of Alabama at Birmingham (UAB), UAB Epilepsy Center, Birmingham, AL, USA.
| | - Jane B Allendorfer
- Department of Neurology, University of Alabama at Birmingham (UAB), UAB Epilepsy Center, Birmingham, AL, USA
| | - Adam M Goodman
- Department of Neurology, University of Alabama at Birmingham (UAB), UAB Epilepsy Center, Birmingham, AL, USA
| | - Caroline G Byington
- Department of Neurology, University of Alabama at Birmingham (UAB), UAB Epilepsy Center, Birmingham, AL, USA
| | - Noah S Philip
- VA RR&D Center for Neurorestoration & Neurotechnology, VA Providence Healthcare System, Providence, RI, USA; Dept of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Stephen Correia
- VA RR&D Center for Neurorestoration & Neurotechnology, VA Providence Healthcare System, Providence, RI, USA
| | - W Curt LaFrance
- VA RR&D Center for Neurorestoration & Neurotechnology, VA Providence Healthcare System, Providence, RI, USA; Dept of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
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49
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Abstract
In a recent issue of Nature, Marek et al. (2022) demonstrate that cross-sectional brain-behavior correlations are often small and unreliable without large samples. This observation pushes human neuroscience toward study designs that either maximize sample sizes to detect small effects or maximize effect sizes using focused investigations.
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Affiliation(s)
- Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA; Department of Neurology, Northwestern University, Evanston, IL, USA.
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
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50
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Baumel WT, Lu L, Huang X, Drysdale AT, Sweeny JA, Gong Q, Sylvester CM, Strawn JR. Neurocircuitry of Treatment in Anxiety Disorders. Biomark Neuropsychiatry 2022; 6. [PMID: 35756886 PMCID: PMC9222661 DOI: 10.1016/j.bionps.2022.100052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background: Methods: Results: Conclusions:
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Affiliation(s)
- W. Tommy Baumel
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Correspondence to: University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267, USA. (W.T. Baumel)
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Andrew T. Drysdale
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - John A. Sweeny
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Chad M. Sylvester
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
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