1
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), 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 Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), 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 Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), 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 Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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2
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Arafat T, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. Prog Neurobiol 2024; 236:102604. [PMID: 38604584 DOI: 10.1016/j.pneurobio.2024.102604] [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: 06/26/2023] [Revised: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Thaera Arafat
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Department of Neurology, Duke University School of Medicine and Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27705, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3 BG, United Kingdom
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Queretaro, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.
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3
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Mosharov EV, Rosenberg AM, Monzel AS, Osto CA, Stiles L, Rosoklija GB, Dwork AJ, Bindra S, Zhang Y, Fujita M, Mariani MB, Bakalian M, Sulzer D, De Jager PL, Menon V, Shirihai OS, Mann JJ, Underwood M, Boldrini M, Thiebaut de Schotten M, Picard M. A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity. RESEARCH SQUARE 2024:rs.3.rs-4047706. [PMID: 38562777 PMCID: PMC10984021 DOI: 10.21203/rs.3.rs-4047706/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.
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Affiliation(s)
- Eugene V. Mosharov
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Ayelet M Rosenberg
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Anna S Monzel
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Corey A. Osto
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Linsey Stiles
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Gorazd B. Rosoklija
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrew J. Dwork
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Snehal Bindra
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Ya Zhang
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Madeline B Mariani
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mihran Bakalian
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - David Sulzer
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Departments of Neurology and Pharmacology, Columbia University Irving Medical Center, New York, NY, USA; Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Orian S Shirihai
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - J. John Mann
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mark Underwood
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maura Boldrini
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behavior Laboratory, Paris, France; Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, France
| | - Martin Picard
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
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4
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Mosharov EV, Rosenberg AM, Monzel AS, Osto CA, Stiles L, Rosoklija GB, Dwork AJ, Bindra S, Zhang Y, Fujita M, Mariani MB, Bakalian M, Sulzer D, De Jager PL, Menon V, Shirihai OS, Mann JJ, Underwood M, Boldrini M, de Schotten MT, Picard M. A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583623. [PMID: 38496679 PMCID: PMC10942385 DOI: 10.1101/2024.03.05.583623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.
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Affiliation(s)
- Eugene V Mosharov
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Ayelet M Rosenberg
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Anna S Monzel
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Corey A Osto
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Linsey Stiles
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Gorazd B Rosoklija
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrew J Dwork
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Snehal Bindra
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Ya Zhang
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Madeline B Mariani
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mihran Bakalian
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - David Sulzer
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Departments of Neurology and Pharmacology, Columbia University Irving Medical Center, New York, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Orian S Shirihai
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - J John Mann
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mark Underwood
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maura Boldrini
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behavior Laboratory, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, France
| | - Martin Picard
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
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5
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Janson A, Sainburg L, Akbarian B, Johnson GW, Rogers BP, Chang C, Englot DJ, Morgan VL. Indirect structural changes and reduced controllability after temporal lobe epilepsy resection. Epilepsia 2024; 65:675-686. [PMID: 38240699 PMCID: PMC10948308 DOI: 10.1111/epi.17889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 03/06/2024]
Abstract
OBJECTIVE To understand the potential behavioral and cognitive effects of mesial temporal resection for temporal lobe epilepsy (TLE) a method is required to characterize network-wide functional alterations caused by a discrete structural disconnection. The objective of this study was to investigate network-wide alterations in brain dynamics of patients with TLE before and after surgical resection of the seizure focus using average regional controllability (ARC), a measure of the ability of a node to influence network dynamics. METHODS Diffusion-weighted imaging (DWI) data were acquired in 27 patients with drug-resistant unilateral mesial TLE who underwent selective amygdalohippocampectomy. Imaging data were acquired before and after surgery and a presurgical and postsurgical structural connectome was generated from whole-brain tractography. Edge-wise strength, node strength, and node ARC were compared before and after surgery. Direct and indirect edge-wise strength changes were identified using patient-specific simulated resections. Direct edges were defined as primary edges disconnected by the resection zone itself. Indirect edges were secondary measured edge strength changes. Changes in node strength and ARC were then related to both direct and indirect edge changes. RESULTS We found nodes with significant postsurgical changes in both node strength and ARC surrounding the resection zone (paired t tests, p < .05, Bonferroni corrected). ARC identified additional postsurgical changes in nodes outside of the resection zone within the ipsilateral occipital lobe, which were associated with indirect edge-wise strength changes of the postsurgical network (Fisher's exact test, p < .001). These indirect edge-wise changes were facilitated through the "hub" nodes including the thalamus, putamen, insula, and precuneus. SIGNIFICANCE Discrete network disconnection from TLE resection results in widespread structural and functional changes not predicted by disconnection alone. These can be well characterized by dynamic controllability measures such as ARC and may be useful for investigating changes in brain function that may contribute to seizure recurrence and behavioral or cognitive changes after surgery.
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Affiliation(s)
- Andrew Janson
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lucas Sainburg
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Behnaz Akbarian
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Graham W Johnson
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Baxter P Rogers
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Catie Chang
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Dario J Englot
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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6
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Betzel R, Puxeddu MG, Seguin C, Bazinet V, Luppi A, Podschun A, Singleton SP, Faskowitz J, Parakkattu V, Misic B, Markett S, Kuceyeski A, Parkes L. Controlling the human connectome with spatially diffuse input signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.581006. [PMID: 38463980 PMCID: PMC10925126 DOI: 10.1101/2024.02.27.581006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
- Program in Neuroscience, Indiana University, Bloomington IN 47401
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Andrea Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | | | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
| | - Vibin Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47401
- Cognitive Science Program, Indiana University, Bloomington IN 47401
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY
- Department of Computational Biology, Cornell University, Ithaca, NY
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
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7
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Singleton SP, Velidi P, Schilling L, Luppi AI, Jamison K, Parkes L, Kuceyeski A. Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568762. [PMID: 38077021 PMCID: PMC10705230 DOI: 10.1101/2023.11.27.568762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, U.S.A
| | - Louisa Schilling
- Montreal Neurological Institute, McGill Univeristy, Montreal, CA
| | - Andrea I Luppi
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
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8
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Sun H, Jiang R, Dai W, Dufford AJ, Noble S, Spann MN, Gu S, Scheinost D. Network controllability of structural connectomes in the neonatal brain. Nat Commun 2023; 14:5820. [PMID: 37726267 PMCID: PMC10509217 DOI: 10.1038/s41467-023-41499-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/06/2023] [Indexed: 09/21/2023] Open
Abstract
White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.
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Affiliation(s)
- Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Marisa N Spann
- Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA.
- Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT, 06510, USA.
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9
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. Using network control theory to study the dynamics of the structural connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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10
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Wilmskoetter J, Busby N, He X, Caciagli L, Roth R, Kristinsson S, Davis KA, Rorden C, Bassett DS, Fridriksson J, Bonilha L. Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity. Commun Biol 2023; 6:727. [PMID: 37452209 PMCID: PMC10349039 DOI: 10.1038/s42003-023-05119-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.
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Affiliation(s)
- Janina Wilmskoetter
- Department of Health and Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA.
| | - Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Beijing, China
| | - Lorenzo Caciagli
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Sigfus Kristinsson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, New Mexico, NM, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
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11
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Singleton SP, Timmermann C, Luppi AI, Eckernäs E, Roseman L, Carhart-Harris RL, Kuceyeski A. Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540409. [PMID: 37214949 PMCID: PMC10197635 DOI: 10.1101/2023.05.11.540409] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Psychedelics offer a profound window into the functioning of the human brain and mind through their robust acute effects on perception, subjective experience, and brain activity patterns. In recent work using a receptor-informed network control theory framework, we demonstrated that the serotonergic psychedelics lysergic acid diethylamide (LSD) and psilocybin flatten the brain's control energy landscape in a manner that covaries with more dynamic and entropic brain activity. Contrary to LSD and psilocybin, whose effects last for hours, the serotonergic psychedelic N,N-dimethyltryptamine (DMT) rapidly induces a profoundly immersive altered state of consciousness lasting less than 20 minutes, allowing for the entirety of the drug experience to be captured during a single resting-state fMRI scan. Using network control theory, which quantifies the amount of input necessary to drive transitions between functional brain states, we integrate brain structure and function to map the energy trajectories of 14 individuals undergoing fMRI during DMT and placebo. Consistent with previous work, we find that global control energy is reduced following injection with DMT compared to placebo. We additionally show longitudinal trajectories of global control energy correlate with longitudinal trajectories of EEG signal diversity (a measure of entropy) and subjective ratings of drug intensity. We interrogate these same relationships on a regional level and find that the spatial patterns of DMT's effects on these metrics are correlated with serotonin 2a receptor density (obtained from separately acquired PET data). Using receptor distribution and pharmacokinetic information, we were able to successfully recapitulate the effects of DMT on global control energy trajectories, demonstrating a proof-of-concept for the use of control models in predicting pharmacological intervention effects on brain dynamics.
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Affiliation(s)
| | - Christopher Timmermann
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
| | | | - Emma Eckernäs
- Unit for Pharmacokinetics and Drug Metabolism, Department of Pharmacology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
| | - Robin L. Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Psychedelics Division, Neuroscape, University of California San Francisco, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, USA
- Department of Radiology, Weill Cornell Medicine, New York, USA
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12
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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13
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Luppi AI, Singleton SP, Hansen JY, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532981. [PMID: 36993597 PMCID: PMC10055141 DOI: 10.1101/2023.03.16.532981] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain's network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and disease-related cortical abnormality maps (11 neurodegenerative, psychiatric and neurodevelopmental diseases; N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by pharmacological or pathological perturbation. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying novel ways to promote selective transitions between desired cognitive topographies.
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Affiliation(s)
- Andrea I. Luppi
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Danilo Bzdok
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- MILA, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, U.S.A
| | - Richard F. Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, U.S.A
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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14
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Hahn T, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Fisch L, Leenings R, Sarink K, Blanke J, Holstein V, Emden D, Beisemann M, Opel N, Grotegerd D, Meinert S, Heindel W, Witt S, Rietschel M, Nöthen MM, Forstner AJ, Kircher T, Nenadic I, Jansen A, Müller-Myhsok B, Andlauer TFM, Walter M, van den Heuvel MP, Jamalabadi H, Dannlowski U, Repple J. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder. Mol Psychiatry 2023; 28:1057-1063. [PMID: 36639510 PMCID: PMC10005934 DOI: 10.1038/s41380-022-01936-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023]
Abstract
Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
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Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vincent Holstein
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marie Beisemann
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Interdisciplinary Centre for Clinical Research IZKF, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, Münster, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany.,Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | | | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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15
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Stocker JE, Nozari E, van Vugt M, Jansen A, Jamalabadi H. Network controllability measures of subnetworks: implications for neurosciences. J Neural Eng 2023; 20. [PMID: 36633267 DOI: 10.1088/1741-2552/acb256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Objective:Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted.Approach:In this paper, we use a host of simulations based on synthetic as well as structural magnetic resonance imaging (MRI) data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density.Main results:We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density.Significance:Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.
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Affiliation(s)
- Julia Elina Stocker
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, United States of America.,Department of Electrical and Computer Engineering, University of California, Riverside, United States of America.,Department of Bioengineering, University of California, Riverside, United States of America
| | - Marieke van Vugt
- Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.,Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
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16
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Hahn T, Jamalabadi H, Nozari E, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Grumbach P, Fisch L, Leenings R, Sarink K, Blanke J, Vennekate LK, Emden D, Opel N, Grotegerd D, Enneking V, Meinert S, Borgers T, Klug M, Leehr EJ, Dohm K, Heindel W, Gross J, Dannlowski U, Redlich R, Repple J. Towards a network control theory of electroconvulsive therapy response. PNAS NEXUS 2023; 2:pgad032. [PMID: 36874281 PMCID: PMC9982063 DOI: 10.1093/pnasnexus/pgad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/09/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023]
Abstract
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.
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Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, 92521 Riverside, USA
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Leon Kleine Vennekate
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Institute for Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Melissa Klug
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University Hospital Münster, 48149 Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Department of Psychology, University of Halle, 06099 Halle (Saale), Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
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