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Gao Y, Zhu Z, Fang F, Zhang Y, Meng M. EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion. J Affect Disord 2024; 361:356-366. [PMID: 38885847 DOI: 10.1016/j.jad.2024.06.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.
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Affiliation(s)
- Yunyuan Gao
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Zehao Zhu
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Ming Meng
- College of Automation, Hangzhou Dianzi University, Hangzhou, China.
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2
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Luppi AI, Singleton SP, Hansen JY, Jamison KW, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies. Nat Biomed Eng 2024:10.1038/s41551-024-01242-2. [PMID: 39103509 DOI: 10.1038/s41551-024-01242-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
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Affiliation(s)
- Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - S Parker Singleton
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- MILA, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Richard F Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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3
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Censi ST, Mariani-Costantini R, Granzotto A, Tomassini V, Sensi SL. Endogenous retroviruses in multiple sclerosis: A network-based etiopathogenic model. Ageing Res Rev 2024; 99:102392. [PMID: 38925481 DOI: 10.1016/j.arr.2024.102392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
The present perspective article proposes an etiopathological model for multiple sclerosis pathogenesis and progression associated with the activation of human endogenous retroviruses. We reviewed preclinical, clinical, epidemiological, and evolutionary evidence indicating how the complex, multi-level interplay of genetic traits and environmental factors contributes to multiple sclerosis. We propose that endogenous retroviruses transactivation acts as a critical node in disease development. We also discuss the rationale for combined anti-retroviral therapy in multiple sclerosis as a disease-modifying therapeutic strategy. Finally, we propose that the immuno-pathogenic process triggered by endogenous retrovirus activation can be extended to aging and aging-related neurodegeneration. In this regard, endogenous retroviruses can be envisioned to act as epigenetic noise, favoring the proliferation of disorganized cellular subpopulations and accelerating system-specific "aging". Since inflammation and aging are two sides of the same coin (plastic dis-adaptation to external stimuli with system-specific degree of freedom), the two conditions may be epiphenomenal products of increased epigenomic entropy. Inflammation accelerates organ-specific aging, disrupting communication throughout critical systems of the body and producing symptoms. Overlapping neurological symptoms and syndromes may emerge from the activity of shared molecular networks that respond to endogenous retroviruses' reactivation.
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Affiliation(s)
- Stefano T Censi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University, Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy.
| | - Renato Mariani-Costantini
- Center for Advanced Studies and Technology (CAST), "G. d'Annunzio" University, Chieti-Pescara, Italy
| | - Alberto Granzotto
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University, Chieti-Pescara, Italy; Center for Advanced Studies and Technology (CAST), "G. d'Annunzio" University, Chieti-Pescara, Italy
| | - Valentina Tomassini
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University, Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy; Multiple Sclerosis Centre, Institute of Neurology, SS Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University, Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies (ITAB), "G. d'Annunzio" University, Chieti-Pescara, Italy; Center for Advanced Studies and Technology (CAST), "G. d'Annunzio" University, Chieti-Pescara, Italy; Multiple Sclerosis Centre, Institute of Neurology, SS Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy.
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4
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Chen B, Sun W, Yan C. Controllability in attention deficit hyperactivity disorder brains. Cogn Neurodyn 2024; 18:2003-2013. [PMID: 39104674 PMCID: PMC11297865 DOI: 10.1007/s11571-023-10063-z] [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: 05/09/2023] [Revised: 11/23/2023] [Accepted: 12/19/2023] [Indexed: 08/07/2024] Open
Abstract
The role of network metrics in exploring brain networks of mental illness is crucial. This study focuses on quantifying a node controllability index (CA-scores) and developing a novel framework for studying the dysfunction of attention deficit hyperactivity disorder (ADHD) brains. By analyzing fMRI data from 143 healthy controls and 102 ADHD patients, the controllability metric reveals distinct differences in nodes (brain regions) and subsystems (functional modules). There are significantly atypical CA-scores in the Rolandic operculum, superior medial orbitofrontal cortex, insula, posterior cingulate gyrus, supramarginal gyrus, angular gyrus, precuneus, heschl gyrus, and superior temporal gyrus of ADHD patients. A comparison with measures of connection strength, eigenvector centrality, and topology entropy suggests that the controllability index may be more effective in identifying abnormal regions in ADHD brains. Furthermore, our controllability index could be extended to investigate functional networks associated with other psychiatric disorders. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10063-z.
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Affiliation(s)
- Bo Chen
- Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, 310018 People’s Republic of China
| | - Weigang Sun
- Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, 310018 People’s Republic of China
| | - Chuankui Yan
- College of Mathematics and Physics, Wenzhou University, Wenzhou, 325024 People’s Republic of China
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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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Polner B, Jamalabadi H, van Kemenade BM, Billino J, Kircher T, Straube B. Speech-Gesture Matching and Schizotypal Traits: A Network Approach. Schizophr Bull 2024:sbae134. [PMID: 39046822 DOI: 10.1093/schbul/sbae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND HYPOTHESIS Impaired speech-gesture matching has repeatedly been shown in patients with schizophrenia spectrum disorders. Here, we tested the hypothesis that schizotypal traits in the general population are related to reduced speech-gesture matching performance and reduced self-reports about gesture perception. We further explored the relationships between facets of schizotypy and gesture processing in a network model. STUDY DESIGN Participants (1094 mainly healthy adults) were presented with concrete or abstract sentences accompanied with videos showing related or unrelated gestures. For each video, participants evaluated the alignment between speech and gesture. They also completed self-rating scales about the perception and production of gestures (Brief Assessment of Gesture scale) and schizotypal traits (Schizotypal Personality Questionnaire-Brief 22-item version). We analyzed bivariate associations and estimated a non-regularized partial Spearman correlation network. We characterized the network by analyzing bridge centrality and controllability metrics of nodes. STUDY RESULTS We found a negative relationship between both concrete and abstract gesture-speech matching performance and overall schizotypy. In the network, disorganization had the highest average controllability and it was negatively related to abstract speech-gesture matching. Bridge centralities indicated that self-reported production of gestures to enhance communication in social interactions connects self-reported gesture perception, schizotypal traits, and gesture processing task performance. CONCLUSION The association between impaired abstract speech-gesture matching and disorganization supports a continuum between schizophrenia and schizotypy. Using gestures to facilitate communication connects subjective and objective aspects of gesture processing and schizotypal traits. Future interventional studies in patients should test the potential causal pathways implied by this network model.
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Affiliation(s)
- Bertalan Polner
- Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Bianca M van Kemenade
- Center for Psychiatry, Justus Liebig University Giessen, Giessen, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg, Marburg, Germany, and Justus Liebig University Giessen, Giessen, Germany
| | - Jutta Billino
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg, Marburg, Germany, and Justus Liebig University Giessen, Giessen, Germany
- Experimental Psychology, Lifespan Neuropsychology, Justus Liebig University Giessen, Giessen, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg, Marburg, Germany, and Justus Liebig University Giessen, Giessen, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg, Marburg, Germany, and Justus Liebig University Giessen, Giessen, Germany
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7
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Li Q, Zhao Y, Hu Y, Liu Y, Wang Y, Zhang Q, Long F, Chen Y, Wang Y, Li H, Poels EMP, Kamperman AM, Sweeney JA, Kuang W, Li F, Gong Q. Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder. EBioMedicine 2024; 106:105255. [PMID: 39032426 DOI: 10.1016/j.ebiom.2024.105255] [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: 12/25/2023] [Revised: 06/14/2024] [Accepted: 07/08/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. METHODS Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. FINDINGS Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. INTERPRETATION These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. FUNDING National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).
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Affiliation(s)
- Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Youjin Zhao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yongbo Hu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yang Liu
- Academy of Mathematics and Systems Science Chinese, Academy of Science, Beijing, China
| | - Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yitian Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Haoran Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Eline M P Poels
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Astrid M Kamperman
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - John A Sweeney
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Department of Psychiatry and Behavioural Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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9
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Tanner J, Faskowitz J, Teixeira AS, Seguin C, Coletta L, Gozzi A, Mišić B, Betzel RF. A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. Nat Commun 2024; 15:5865. [PMID: 38997282 PMCID: PMC11245624 DOI: 10.1038/s41467-024-50248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
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Affiliation(s)
- Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, USA.
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, USA.
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10
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Yin N, Han Y, Wang L, Yang F, Li J, Xu G. Localization of epileptogenic zone based on time-varying effective networks. Epilepsy Res 2024; 205:107409. [PMID: 39002390 DOI: 10.1016/j.eplepsyres.2024.107409] [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: 04/18/2024] [Revised: 06/13/2024] [Accepted: 07/01/2024] [Indexed: 07/15/2024]
Abstract
Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.
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Affiliation(s)
- Ning Yin
- State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
| | - Yamei Han
- State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Le Wang
- Functional Neurosurgery Department of Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Fan Yang
- State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Jicheng Li
- State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligentization of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China
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11
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Chen R, Nie P, Wang J, Wang GZ. Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1865. [PMID: 38972934 DOI: 10.1002/wrna.1865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 07/09/2024]
Abstract
The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Pengxing Nie
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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12
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Li Y, Yang H, Gu S. Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks. Sci Bull (Beijing) 2024; 69:1738-1747. [PMID: 38490889 DOI: 10.1016/j.scib.2024.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/27/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024]
Abstract
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.
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Affiliation(s)
- Yuanning Li
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
| | - Huzheng Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China.
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13
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Stanford W, Mucha PJ, Dayan E. Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks. Commun Biol 2024; 7:701. [PMID: 38849512 PMCID: PMC11161655 DOI: 10.1038/s42003-024-06392-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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Affiliation(s)
- William Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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14
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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 Elucidated via Network Control Theory. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00139-3. [PMID: 38839036 DOI: 10.1016/j.bpsc.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/03/2024] [Accepted: 05/18/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework. METHODS Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances. RESULTS We found 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. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings. CONCLUSIONS 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 heavy alcohol use.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York.
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York
| | - Louisa Schilling
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
| | - Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, New Jersey
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
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15
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Forbes E, Hassien A, Tan RJ, Wang D, Lega B. Modulation of hippocampal theta oscillations via deep brain stimulation of the parietal cortex depends on cognitive state. Cortex 2024; 175:28-40. [PMID: 38691923 PMCID: PMC11221570 DOI: 10.1016/j.cortex.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/07/2023] [Accepted: 03/24/2024] [Indexed: 05/03/2024]
Abstract
The angular gyrus (AG) and posterior cingulate cortex (PCC) demonstrate extensive structural and functional connectivity with the hippocampus and other core recollection network regions. Consequently, recent studies have explored neuromodulation targeting these and other regions as a potential strategy for restoring function in memory disorders such as Alzheimer's Disease. However, determining the optimal approach for neuromodulatory devices requires understanding how parameters like selected stimulation site, cognitive state during modulation, and stimulation duration influence the effects of deep brain stimulation (DBS) on electrophysiological features relevant to episodic memory. We report experimental data examining the effects of high-frequency stimulation delivered to the AG or PCC on hippocampal theta oscillations during the memory encoding (study) or retrieval (test) phases of an episodic memory task. Results showed selective enhancement of anterior hippocampal slow theta oscillations with stimulation of the AG preferentially during memory retrieval. Conversely, stimulation of the PCC attenuated slow theta oscillations. We did not observe significant behavioral effects in this (open-loop) stimulation experiment, suggesting that neuromodulation strategies targeting episodic memory performance may require more temporally precise stimulation approaches.
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Affiliation(s)
- Eugenio Forbes
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Alexa Hassien
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Ryan Joseph Tan
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - David Wang
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Bradley Lega
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
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16
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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [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: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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17
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Heshmati S, Westhoff M, Hofmann SG. Novel Approaches Toward Studying Change: Implications for Understanding and Treating Psychopathology. Psychiatr Clin North Am 2024; 47:287-300. [PMID: 38724120 DOI: 10.1016/j.psc.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In this article, the authors critically evaluate contemporary models of psychopathology and therapies, underscoring the limitations of traditional symptom-based classification approaches in mental health. The authors introduce a paradigm shift in the field, toward a process-oriented and dynamic systems approach to psychotherapy that offers deeper insights into the complex interplay of symptoms and individual experiences in psychopathology. These approaches offer a more personalized and effective understanding and treatment of mental health issues, moving beyond static and 1-dimensional views. The authors discuss the implications for clinical practice, emphasizing improved assessment, diagnosis, and tailored treatment strategies.
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Affiliation(s)
- Saida Heshmati
- Department of Psychology, Claremont Graduate University, 150 E. 10th Street, Claremont, CA 91711, USA.
| | - Marlon Westhoff
- Department of Psychology, Philipps-University of Marburg, Translational Clinical Psychology Group, Schulstraße 12, Marburg D-35032, Germany
| | - Stefan G Hofmann
- Department of Psychology, Philipps-University of Marburg, Translational Clinical Psychology Group, Schulstraße 12, Marburg D-35032, Germany
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18
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Pan C, Ma Y, Wang L, Zhang Y, Wang F, Zhang X. From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder. Brain Sci 2024; 14:509. [PMID: 38790487 PMCID: PMC11119370 DOI: 10.3390/brainsci14050509] [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: 04/17/2024] [Revised: 05/02/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024] Open
Abstract
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.
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Affiliation(s)
- Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China;
| | - Ying Ma
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210033, China; (Y.M.); (Y.Z.)
| | - Lifei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210024, China
| | - Yan Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210033, China; (Y.M.); (Y.Z.)
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210024, China
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
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19
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Ruiz T, Brown S, Farivar R. Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI. Vision (Basel) 2024; 8:33. [PMID: 38804354 PMCID: PMC11130927 DOI: 10.3390/vision8020033] [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: 02/02/2024] [Revised: 04/03/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024] Open
Abstract
Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain's processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.
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Affiliation(s)
- Tatiana Ruiz
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
| | - Shael Brown
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
| | - Reza Farivar
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
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20
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Zhang Y, Chen Y, Zhang J, Luo X, Zhang M, Qu H, Yi Z. Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7072-7086. [PMID: 36279337 DOI: 10.1109/tnnls.2022.3213688] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SNN), a new bionic spiking temporal memory (BSTM) model is proposed to explore the encoding, formation, and retrieval of episodic memory. For encoding episodic memory, the spike-timing-dependent-plasticity (STDP) learning algorithm and a proposed minicolumn selection algorithm are used to encode each input item into several active minicolumns. For the formation of episodic memory, a sequential memory algorithm is proposed to store the contexts between items. For retrieval of episodic memory, the local retrieval algorithm and the global retrieval algorithm are proposed to retrieve sequence information, achieving multisentence prediction and multitime step prediction. All functions of BSTM are based on bionic spiking neurons, which have biological characteristics including columnar and dendritic structures, firing and receiving spikes, and delaying transmission. To test the performance of the BSTM model, the Children's Book Test (CBT) data set was used to conduct a series of experiments under different settings, including changing the number of minicolumns, neurons and sequences, modifying sequence items, etc. Compared to other sequence memory algorithms, the experimental results show that the proposed BSTM achieves higher accuracy and better robustness.
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21
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Jamison KW, Gu Z, Wang Q, Sabuncu MR, Kuceyeski A. Release the Krakencoder: A unified brain connectome translation and fusion tool. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589274. [PMID: 38659856 PMCID: PMC11042193 DOI: 10.1101/2024.04.12.589274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.
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Affiliation(s)
- Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zijin Gu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Qinxin Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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22
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Wang X, Lin J, Lu H, Xiong Y, Duan C, Zhang D, Huang J, Deng L, Li C, Li R, Zhang D, Bian X, Zhou J, Pan L, Lou X. Alteration of White Matter Connectivity for MR-Guided Focused Ultrasound in the Treatment of Essential Tremor. J Magn Reson Imaging 2024; 59:1358-1370. [PMID: 37491872 DOI: 10.1002/jmri.28896] [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] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy has been implemented as a therapeutic alternative for the treatment of drug-refractory essential tremor (ET). However, its impact on the brain structural network is still unclear. PURPOSE To investigate both global and local alterations of the white matter (WM) connectivity network in ET after MRgFUS thalamotomy. STUDY TYPE Retrospective. SUBJECTS Twenty-seven ET patients (61 ± 11 years, 19 males) with MRgFUS thalamotomy and 28 healthy controls (HC) (61 ± 11 years, 20 males) were recruited for comparison. FIELD STRENGTH/SEQUENCE A 3 T/single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging, three-dimensional T1 weighted imaging by using gradient-echo-based sequence. ASSESSMENT Patients were undergoing MRgFUS thalamotomy and their clinical data were collected from pre-operation to 6-month post-operation. Network topological metrics, including rich-club organization, small-world, and efficiency properties were calculated. Correlation between the topological metrics and tremor scores in ET groups was also calculated to assess the role of neural remodeling in the brain. STATISTICAL TESTS Two-sample independent t-tests, chi-squared test, ANOVA, Bonferroni test, and Spearman's correlation. Statistical significance was set at P < 0.05. RESULTS For ET patients, the strength of rich-club connection and clustering coefficient significantly increased vs. characteristic path length decreased at 6-month post-operation compared with pre-operation. The distribution pattern of rich-club regions was different in ET groups. Specifically, the order of the rich-club regions was changed according to the network degree value after MRgFUS thalamotomy. Moreover, the altered nodal efficiency in the right temporal pole of the superior temporal gyrus (R = 0.434-0.596) and right putamen (R = 0.413-0.436) was positively correlated with different tremor improvement. DATA CONCLUSION These findings might improve understanding of treatment-induced modulation from a network perspective and may work as an objective marker in the assessment of ET tremor control with MRgFUS thalamotomy. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Xiaoyu Wang
- School of Medicine, Nankai University, Tianjin, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jiaji Lin
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Haoxuan Lu
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Yongqin Xiong
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Caohui Duan
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Dong Zhang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jiayu Huang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Linlin Deng
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Chenxi Li
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Runze Li
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Dekang Zhang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xiangbing Bian
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jiayou Zhou
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Longsheng Pan
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Xin Lou
- School of Medicine, Nankai University, Tianjin, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
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23
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair DA, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush‐Goebel S, Shinohara RT, Shou H, Tisdall MD, Vettel JM, Grafton ST, Cieslak M, Satterthwaite TD. A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging. Hum Brain Mapp 2024; 45:e26580. [PMID: 38520359 PMCID: PMC10960521 DOI: 10.1002/hbm.26580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Bioengineering, School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip A. Cook
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Barry Giesbrecht
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sage Rush‐Goebel
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jean M. Vettel
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
- U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenMarylandUSA
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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24
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Alizadeh Darbandi SS, Fornito A, Ghasemi A. The impact of input node placement in the controllability of structural brain networks. Sci Rep 2024; 14:6902. [PMID: 38519624 PMCID: PMC10960045 DOI: 10.1038/s41598-024-57181-0] [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: 08/09/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.
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Affiliation(s)
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Abdorasoul Ghasemi
- Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
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25
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Cash RFH, Zalesky A. Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities. Biol Psychiatry 2024; 95:510-522. [PMID: 38040047 DOI: 10.1016/j.biopsych.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023]
Abstract
The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.
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Affiliation(s)
- Robin F H Cash
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
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26
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Borzou A, Miller SN, Hommel JD, Schwarz JM. Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex. PNAS NEXUS 2024; 3:pgae092. [PMID: 38476665 PMCID: PMC10929585 DOI: 10.1093/pnasnexus/pgae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024]
Abstract
We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-excitatory and functional-inhibitory neurons being affected. Using conventional network analysis, we find that the diameter of the graph, or the shortest length between the two most distant nodes, increases with cocaine, suggesting that the neuronal network is less robust. We also find that the betweenness centrality scores for several of the functional-excitatory and functional-inhibitory neurons decrease significantly, while other scores remain essentially unchanged, to also suggest that the neuronal network is less robust. Finally, we study the distribution of neuronal activity and relate it to energy to find that cocaine drives the neuronal network towards destabilization in the energy landscape of neuronal activation. While this destabilization is presumably temporary given one administration of cocaine, perhaps this initial destabilization indicates a transition towards a new stable state with repeated cocaine administration. However, such analyses are useful more generally to understand how neuronal networks respond to perturbations.
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Affiliation(s)
- Ahmad Borzou
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- CompuFlair, Houston, TX 77064, USA
| | - Sierra N Miller
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jonathan D Hommel
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - J M Schwarz
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- Indian Creek Farm, Ithaca, NY 14850, USA
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27
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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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28
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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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29
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Vohryzek J, Cabral J, Lord LD, Fernandes HM, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML. Brain dynamics predictive of response to psilocybin for treatment-resistant depression. Brain Commun 2024; 6:fcae049. [PMID: 38515439 PMCID: PMC10957168 DOI: 10.1093/braincomms/fcae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/16/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024] Open
Abstract
Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here, we leveraged the differential outcome in responders and non-responders to psilocybin (10 and 25 mg, 7 days apart) therapy for depression-to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a healthy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-hydroxytryptamine 2a and 5-hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression, and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.
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Affiliation(s)
- Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s—PT Government Associate Laboratory, Braga/Guimarães, University of Minho, Portugal
| | - Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Henrique M Fernandes
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
- Psychedelics Division, Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
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30
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Nakuci J, Yeon J, Haddara N, Kim JH, Kim SP, Rahnev D. Multiple brain activation patterns for the same task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.08.536107. [PMID: 37066155 PMCID: PMC10104176 DOI: 10.1101/2023.04.08.536107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. Finally, in a fourth experiment, we tested whether multiple activation patterns would also appear for a qualitatively different, working memory task. We again found multiple subtypes of trials with differential activation in frontoparietal control, dorsal attention, and ventral attention networks. Overall, these findings demonstrate that the same cognitive tasks are accomplished through multiple brain activation patterns.
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Affiliation(s)
- Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
| | - Jiwon Yeon
- Department of Psychology, Stanford University, Stanford, California, 94305, USA
| | - Nadia Haddara
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
| | - Ji-Hyun Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
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Sinisalo H, Rissanen I, Kahilakoski OP, Souza VH, Tommila T, Laine M, Nyrhinen M, Ukharova E, Granö I, Soto AM, Matsuda RH, Rantala R, Guidotti R, Kičić D, Lioumis P, Mutanen T, Pizzella V, Marzetti L, Roine T, Stenroos M, Ziemann U, Romani GL, Ilmoniemi RJ. Modulating brain networks in space and time: Multi-locus transcranial magnetic stimulation. Clin Neurophysiol 2024; 158:218-224. [PMID: 38184469 DOI: 10.1016/j.clinph.2023.12.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/17/2023] [Accepted: 12/15/2023] [Indexed: 01/08/2024]
Affiliation(s)
- Heikki Sinisalo
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Ilkka Rissanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | | | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Timo Tommila
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikael Laine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikko Nyrhinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; AMI Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| | - Elena Ukharova
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ida Granö
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ana M Soto
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Renan H Matsuda
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Robin Rantala
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
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32
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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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Fang F, Teixeira AL, Li R, Zou L, Zhang Y. The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis. Cereb Cortex 2024; 34:bhad500. [PMID: 38216523 DOI: 10.1093/cercor/bhad500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/14/2024] Open
Abstract
Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.
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Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Antonio L Teixeira
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Rihui Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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34
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Bolton TAW, Van De Ville D, Régis J, Witjas T, Girard N, Levivier M, Tuleasca C. Dynamic functional changes upon thalamotomy in essential tremor depend on baseline brain morphometry. Sci Rep 2024; 14:2605. [PMID: 38297028 PMCID: PMC10831051 DOI: 10.1038/s41598-024-52410-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024] Open
Abstract
Patients with drug-resistant essential tremor (ET) may undergo Gamma Knife stereotactic radiosurgical thalamotomy (SRS-T), where the ventro-intermediate nucleus of the thalamus (Vim) is lesioned by focused beams of gamma radiations to induce clinical improvement. Here, we studied SRS-T impacts on left Vim dynamic functional connectivity (dFC, n = 23 ET patients scanned before and 1 year after intervention), and on surface-based morphometric brain features (n = 34 patients, including those from dFC analysis). In matched healthy controls (HCs), three dFC states were extracted from resting-state functional MRI data. In ET patients, state 1 spatial stability increased upon SRS-T (F1,22 = 19.13, p = 0.004). More frequent expression of state 3 over state 1 before SRS-T correlated with greater clinical recovery in a way that depended on the MR signature volume (t6 = 4.6, p = 0.004). Lower pre-intervention spatial variability in state 3 expression also did (t6 = - 4.24, p = 0.005) and interacted with the presence of familial ET so that these patients improved less (t6 = 4.14, p = 0.006). ET morphometric profiles showed significantly lower similarity to HCs in 13 regions upon SRS-T (z ≤ - 3.66, p ≤ 0.022), and a joint analysis revealed that before thalamotomy, morphometric similarity and states 2/3 mean spatial similarity to HCs were anticorrelated, a relationship that disappeared upon SRS-T (z ≥ 4.39, p < 0.001). Our results show that left Vim functional dynamics directly relates to upper limb tremor lowering upon intervention, while morphometry instead has a supporting role in reshaping such dynamics.
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Affiliation(s)
- Thomas A W Bolton
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, 1011, Lausanne, Switzerland.
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), 1011, Lausanne, Switzerland.
| | - Dimitri Van De Ville
- Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1202, Geneva, Switzerland
| | - Jean Régis
- Stereotactic and Functional Neurosurgery Service and Gamma Knife Unit, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, 13005, Marseille, France
| | - Tatiana Witjas
- Neurology Department, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, 13005, Marseille, France
| | - Nadine Girard
- Department of Diagnostic and Interventional Neuroradiology, Centre de Résonance Magnétique Biologique et Médicale, Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, 13005, Marseille, France
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, 1011, Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), 1015, Lausanne, Switzerland
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, 1011, Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), 1015, Lausanne, Switzerland
- Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
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35
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Luppi AI, Girn M, Rosas FE, Timmermann C, Roseman L, Erritzoe D, Nutt DJ, Stamatakis EA, Spreng RN, Xing L, Huttner WB, Carhart-Harris RL. A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex. Brain 2024; 147:56-80. [PMID: 37703310 DOI: 10.1093/brain/awad311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London, SW7 2AZ, UK
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David Erritzoe
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
| | - Lei Xing
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Wieland B Huttner
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
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Han K, Liu G, Liu N, Li J, Li J, Cui L, Cheng M, Long J, Liao X, Tang Z, Liu Y, Liu J, Chen J, Lu H, Zhang H. Effects of Mobile Intelligent Cognitive Training for Patients with Post-Stroke Cognitive Impairment: A 12-Week, Multicenter, Randomized Controlled Study. J Alzheimers Dis 2024; 100:999-1015. [PMID: 38968051 DOI: 10.3233/jad-240356] [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] [Indexed: 07/07/2024]
Abstract
Background The current application effects of computerized cognitive intervention are inconsistent and limited to hospital rehabilitation settings. Objective To investigate the effect of mobile intelligent cognitive training (MICT) on patients with post-stroke cognitive impairment (PSCI). Methods This study was a multicenter, prospective, open-label, blinded endpoint, cluster-randomized controlled trial (RCT). 518 PSCI patients were stratified and assigned to four rehabilitation settings, and then patients were randomized into experimental and control groups in each rehabilitation setting through cluster randomization. All patients received comprehensive management for PSCI, while the experimental group additionally received MICT intervention. Treatment was 30 minutes daily, 5 days per week, for 12 weeks. Cognitive function, activities of daily living (ADL), and quality of life (QOL) were assessed before the treatment, at weeks 6 and 12 post-treatment, and a 16-week follow-up. Results Linear Mixed Effects Models showed patients with PSCI were better off than pre-treatment patients on each outcome measure (p < 0.05). Additionally, the improvement of these outcomes in the experimental group was significantly better than in the control group at week 6 post-treatment and 16-week follow-up (p < 0.05). The rehabilitation setting also affected the cognitive efficacy of MICT intervention in improving PSCI patients, and the degree of improvement in each outcome was found to be highest in hospital, followed by community, nursing home, and home settings. Conclusions Long-term MICT intervention can improve cognition, ADL, and QOL in patients with PSCI, with sustained effects for at least one month. Notably, different rehabilitation settings affect the cognitive intervention efficacy of MICT on PSCI patients. However, this still needs to be further determined in future studies.
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Affiliation(s)
- Kaiyue Han
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | | | - Nan Liu
- Beijing Puren Hospital, Beijing, China
| | - Jiangyi Li
- Beijing Dongcheng District Kangfu One Two Three Health Training Center, Beijing, China
| | - Jianfeng Li
- Beijing Yangfangdian Hospital, Beijing, China
| | - Lihua Cui
- Beijing Fengtai District Jiaxiang Nursing-Home for the Elderly, Beijing, China
- Beijing Fengtai You Anmen Hospital, Beijing, China
| | - Ming Cheng
- Beijing Haidian District Guolilai Elderly Care Center, Beijing, China
| | - Junzi Long
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
- Changping Laboratory, Beijing, China
| | - Xingxing Liao
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
- Changping Laboratory, Beijing, China
| | - Zhiqing Tang
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Ying Liu
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Jiajie Liu
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Jiarou Chen
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
- The Second School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Haitao Lu
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Hao Zhang
- School of Rehabilitation, Capital Medical University, Beijing, China
- Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
- University of Health and Rehabilitation Sciences, Qingdao, China
- Cheeloo College of Medicine, Shandong University, Jinan, China
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Boyle R, Klinger HM, Shirzadi Z, Coughlan GT, Seto M, Properzi MJ, Townsend DL, Yuan Z, Scanlon C, Jutten RJ, Papp KV, Amariglio RE, Rentz DM, Chhatwal JP, Donohue MC, Sperling RA, Schultz AP, Buckley RF. Left Frontoparietal Control Network Connectivity Moderates the Effect of Amyloid on Cognitive Decline in Preclinical Alzheimer's Disease: The A4 Study. J Prev Alzheimers Dis 2024; 11:881-888. [PMID: 39044497 PMCID: PMC11266218 DOI: 10.14283/jpad.2024.140] [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: 05/21/2024] [Accepted: 06/13/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND Stronger resting-state functional connectivity of the default mode and frontoparietal control networks has been associated with cognitive resilience to Alzheimer's disease related pathology and neurodegeneration in smaller cohort studies. OBJECTIVES We investigated whether these networks are associated with longitudinal CR to AD biomarkers of beta-amyloid (Aβ). DESIGN Longitudinal mixed. SETTING The Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study and its natural history observation arm, the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) study. PARTICIPANTS A sample of 1,021 cognitively unimpaired older adults (mean age = 71.2 years [SD = 4.7 years], 61% women, 42% APOEε4 carriers, 52% Aβ positive). MEASUREMENTS Global cognitive performance (Preclinical Alzheimer's Cognitive Composite) was assessed over an average 5.4 year follow-up period (SD = 2 years). Cortical Aβ and functional connectivity (left and right frontoparietal control and default mode networks) were estimated from fMRI and PET, respectively, at baseline. Covariates included baseline age, APOEε4 carrier status, years of education, adjusted gray matter volume, head motion, study group, cumulative treatment exposure, and cognitive test version. RESULTS Mixed effects models revealed that functional connectivity of the left frontoparietal control network moderated the negative effect of Aβ on cognitive change (p = .025) such that stronger connectivity was associated with reduced Aβ-related cognitive decline. CONCLUSIONS Our results demonstrate a potential protective effect of functional connectivity in preclinical AD, such that stronger connectivity in this network is associated with slower Aβ-related cognitive decline.
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Affiliation(s)
- R Boyle
- Rachel F Buckley, Department of Neurology, Harvard Aging Brain Study, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA,
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38
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Kong Q, Li T, Reddy S, Hodges S, Kong J. Brain stimulation targets for chronic pain: Insights from meta-analysis, functional connectivity and literature review. Neurotherapeutics 2024; 21:e00297. [PMID: 38237403 PMCID: PMC10903102 DOI: 10.1016/j.neurot.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 02/16/2024] Open
Abstract
Noninvasive brain stimulation (NIBS) techniques have demonstrated their potential for chronic pain management, yet their efficacy exhibits variability across studies. Refining stimulation targets and exploring additional targets offer a possible solution to this challenge. This study aimed to identify potential brain surface targets for NIBS in treating chronic pain disorders by integrating literature review, neuroimaging meta-analysis, and functional connectivity analysis on 90 chronic low back pain patients. Our results showed that the primary motor cortex (M1) (C3/C4, 10-20 EEG system) and prefrontal cortex (F3/F4/Fz) were the most used brain stimulation targets for chronic pain treatment according to the literature review. The bilateral precentral gyrus (M1), supplementary motor area, Rolandic operculum, and temporoparietal junction, were all identified as common potential NIBS targets through both a meta-analysis sourced from Neurosynth and functional connectivity analysis. This study presents a comprehensive summary of the current literature and refines the existing NIBS targets through a combination of imaging meta-analysis and functional connectivity analysis for chronic pain conditions. The derived coordinates (with integration of the international electroencephalography (EEG) 10/20 electrode placement system) within the above brain regions may further facilitate the localization of these targets for NIBS application. Our findings may have the potential to expand NIBS target selection beyond current clinical trials and improve chronic pain treatment.
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Affiliation(s)
- Qiao Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Tingting Li
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Sveta Reddy
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Sierra Hodges
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
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Nozari E, Bertolero MA, Stiso J, Caciagli L, Cornblath EJ, He X, Mahadevan AS, Pappas GJ, Bassett DS. Macroscopic resting-state brain dynamics are best described by linear models. Nat Biomed Eng 2024; 8:68-84. [PMID: 38082179 DOI: 10.1038/s41551-023-01117-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/26/2023] [Indexed: 12/22/2023]
Abstract
It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroencephalography and of whole-brain blood-oxygen-level-dependent brain activity via functional magnetic resonance imaging. We used state-of-the-art linear and nonlinear families of models to describe spontaneous resting-state activity of 700 participants in the Human Connectome Project and 122 participants in the Restoring Active Memory project. We found that linear autoregressive models provide the best fit across both data types and three performance metrics: predictive power, computational complexity and the extent of the residual dynamics unexplained by the model. To explain this observation, we show that microscopic nonlinear dynamics can be counteracted or masked by four factors associated with macroscopic dynamics: averaging over space and over time, which are inherent to aggregated macroscopic brain activity, and observation noise and limited data samples, which stem from technological limitations. We therefore argue that easier-to-interpret linear models can faithfully describe macroscopic brain dynamics during resting-state conditions.
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Affiliation(s)
- Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
- Department of Bioengineering, University of California, Riverside, CA, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli J Cornblath
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaosong He
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Arun S Mahadevan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - George J Pappas
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, 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.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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Guo X, Zhai G, Liu J, Zhang X, Zhang T, Cui D, Zhou R, Gao L. Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder. Autism Res 2023; 16:2275-2290. [PMID: 37815146 DOI: 10.1002/aur.3037] [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: 05/17/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023]
Abstract
Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital Sichuan University, Chengdu, China
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Rongjuan Zhou
- Finance Department, Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
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Li Q, Berner R, Wang X. Decoding brain complexity: Structural-functional interplay in neural networks: Comment on "Structure and function in artificial, zebrafish and human neural networks" by Peng Ji et al. Phys Life Rev 2023; 47:87-89. [PMID: 37776745 DOI: 10.1016/j.plrev.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 10/02/2023]
Affiliation(s)
- Qiang Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Rico Berner
- Department of Physics, Humboldt Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Xiaofan Wang
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
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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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43
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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Zheng X, Zhang X, Chen TT, Watanabe I. Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302530. [PMID: 37332101 DOI: 10.1002/adma.202302530] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/27/2023] [Indexed: 06/20/2023]
Abstract
Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.
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Affiliation(s)
- Xiaoyang Zheng
- Center for Basic Research on Materials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
| | - Xubo Zhang
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
| | - Ta-Te Chen
- Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
- National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
| | - Ikumu Watanabe
- Center for Basic Research on Materials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, 305-0047, Japan
- Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8573, Japan
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de Cates AN, Martens MAG, Wright LC, Gibson D, Spitz G, Gould van Praag CD, Suri S, Cowen PJ, Murphy SE, Harmer CJ. 5-HT 4 Receptor Agonist Effects on Functional Connectivity in the Human Brain: Implications for Procognitive Action. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1124-1134. [PMID: 37098409 PMCID: PMC10914664 DOI: 10.1016/j.bpsc.2023.03.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/02/2023] [Accepted: 03/31/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Cognitive deficits are often comorbid with mood disorders and can cause significant functional impairment even after resolution of the primary mood symptoms. We do not currently have pharmacological treatments that adequately address these deficits. 5-HT4 receptor agonists show promise as potential procognitive agents in animal and early human translational studies. Optimal cognitive performance in humans is directly associated with appropriate functional connectivity between specific resting-state neural networks. However, so far the effect of 5-HT4 receptor agonism on resting-state functional connectivity (rsFC) in the brain in humans is unknown. METHODS We collected resting-state functional magnetic resonance imaging scans from 50 healthy volunteers, of whom 25 received 6 days × 1 mg prucalopride (a highly selective 5-HT4 receptor agonist) and 25 received placebo in a randomized double-blind design. RESULTS Network analyses identified that participants in the prucalopride group had enhanced rsFC between the central executive network and the posterior/anterior cingulate cortex. Seed analyses also showed greater rsFC between the left and right rostral anterior cingulate cortex and the left lateral occipital cortex, and reduced rsFC between the hippocampus and other default mode network regions. CONCLUSIONS Similar to other potentially procognitive medications, low-dose prucalopride in healthy volunteers appeared to enhance rsFC between regions involved in cognitive networks and reduce rsFC within the default mode network. This suggests a mechanism for the behavioral cognitive enhancement previously seen with 5-HT4 receptor agonists in humans and supports the potential for 5-HT4 receptor agonists to be used in clinical psychiatric populations.
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Affiliation(s)
- Angharad N de Cates
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom.
| | - Marieke A G Martens
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Lucy C Wright
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Daisy Gibson
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Gershon Spitz
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia; Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Cassandra D Gould van Praag
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Centre for Human Brain Activity and Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Centre for Human Brain Activity and Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Philip J Cowen
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Susannah E Murphy
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Catherine J Harmer
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom; Oxford Centre for Human Brain Activity and Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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Hossein S, Cooper JA, DeVries BAM, Nuutinen MR, Hahn EC, Kragel PA, Treadway MT. Effects of acute stress and depression on functional connectivity between prefrontal cortex and the amygdala. Mol Psychiatry 2023; 28:4602-4612. [PMID: 37076616 DOI: 10.1038/s41380-023-02056-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023]
Abstract
Stress is known to be a significant risk factor for the development of Major Depressive Disorder (MDD), yet the neural mechanisms that underlie this risk are poorly understood. Prior work has heavily implicated the corticolimbic system in the pathophysiology of MDD. In particular, the prefrontal cortex (PFC) and amygdala play a central role in regulating the response to stress, with dorsal PFC and ventral PFC exhibiting reciprocal excitatory and inhibitory influences on amygdala subregions. However, it remains unclear how best to disentangle the impact of stress from the impact of current MDD symptoms on this system. Here, we examined stress-induced changes in resting state functional connectivity (rsFC) within an a priori corticolimbic network in MDD patients and healthy controls (total n = 80) before and after an acute stressor or a "no stress" control condition. Using graph theoretic analysis, we found that connectivity between basolateral amygdala and dorsal prefrontal nodes of the corticolimbic network had a negative association with individual differences in chronic perceived stress at baseline. Following the acute stressor, healthy individuals showed a reduction of the amygdala node strength, while MDD patients exhibited little change. Finally, dorsal PFC-particularly dorsomedial PFC- connectivity to the basolateral amygdala was associated with the strength of the basolateral amygdala responses to loss feedback during a reinforcement learning task. These findings highlight attenuated connectivity between basolateral amygdala and prefrontal cortex in patients with MDD. In healthy individuals, acute stress exposure was found to push the corticolimbic network to a "stress-phenotype" that may be chronically present in patients with current depression and high levels of perceived stress. In sum, these results help to identify circuit mechanisms underlying the effects of acute stress and their role in mood disorders.
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Affiliation(s)
- Shabnam Hossein
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jessica A Cooper
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | | | | | - Emma C Hahn
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Michael T Treadway
- Department of Psychology, Emory University, Atlanta, GA, USA.
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.
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Jamalabadi H, Hahn T, Winter NR, Nozari E, Ernsting J, Meinert S, Leehr EJ, Dohm K, Bauer J, Pfarr JK, Stein F, Thomas-Odenthal F, Brosch K, Mauritz M, Gruber M, Repple J, Kaufmann T, Krug A, Nenadić I, Kircher T, Dannlowski U, Derntl B. Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers. Front Aging Neurosci 2023; 15:1085153. [PMID: 37920384 PMCID: PMC10618679 DOI: 10.3389/fnagi.2023.1085153] [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: 10/31/2022] [Accepted: 09/25/2023] [Indexed: 11/04/2023] Open
Abstract
Background Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging. Methods In a sample of 814 healthy individuals (aged 33.9 ± 12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability). Results and conclusion In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.
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Affiliation(s)
- Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R. Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
- Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, United States
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Marco Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
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Wilcox RR, Barbey AK. Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks. Cereb Cortex 2023; 33:10322-10331. [PMID: 37526284 DOI: 10.1093/cercor/bhad284] [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: 01/14/2023] [Revised: 06/21/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.
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Affiliation(s)
- Ramsey R Wilcox
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, NE 68501, United States
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Nebraska-Lincoln, NE 68501, United States
- Department of Psychology, University of Illinois, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, United States
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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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Brynildsen JK, Rajan K, Henderson MX, Bassett DS. Network models to enhance the translational impact of cross-species studies. Nat Rev Neurosci 2023; 24:575-588. [PMID: 37524935 PMCID: PMC10634203 DOI: 10.1038/s41583-023-00720-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/17/2023] [Indexed: 08/02/2023]
Abstract
Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.
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Affiliation(s)
- Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kanaka Rajan
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael X Henderson
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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