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Kim JZ, Larsen B, Parkes L. Shaping dynamical neural computations using spatiotemporal constraints. Biochem Biophys Res Commun 2024; 728:150302. [PMID: 38968771 DOI: 10.1016/j.bbrc.2024.150302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 07/07/2024]
Abstract
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
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Affiliation(s)
- Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, 14853, USA.
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, USA
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, 08854, USA.
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2
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Nau M, Schmid AC, Kaplan SM, Baker CI, Kravitz DJ. Centering cognitive neuroscience on task demands and generalization. Nat Neurosci 2024; 27:1656-1667. [PMID: 39075326 DOI: 10.1038/s41593-024-01711-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/17/2024] [Indexed: 07/31/2024]
Abstract
Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.
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Affiliation(s)
- Matthias Nau
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Alexandra C Schmid
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA
| | - Simon M Kaplan
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, MD, USA.
| | - Dwight J Kravitz
- Department of Psychological & Brain Sciences, The George Washington University, Washington, DC, USA.
- Division of Behavioral and Cognitive Sciences, Directorate for Social, Behavioral, and Economic Sciences, US National Science Foundation, Arlington, VA, USA.
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3
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Costa C, Pezzetta R, Masina F, Lago S, Gastaldon S, Frangi C, Genon S, Arcara G, Scarpazza C. Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach. Hum Brain Mapp 2024; 45:e26817. [PMID: 39169641 PMCID: PMC11339134 DOI: 10.1002/hbm.26817] [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: 02/21/2024] [Revised: 07/15/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.
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Affiliation(s)
| | | | | | - Sara Lago
- Padova Neuroscience CenterPaduaItaly
- IRCCS Ospedale San CamilloVeniceItaly
| | - Simone Gastaldon
- Padova Neuroscience CenterPaduaItaly
- Dipartimento di Psicologia dello Sviluppo e della SocializzazioneUniversità degli Studi di PadovaPaduaItaly
| | - Camilla Frangi
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
| | - Sarah Genon
- Institute for Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | | | - Cristina Scarpazza
- IRCCS Ospedale San CamilloVeniceItaly
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
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Gadassi-Polack R, Paganini G, Winschel J, Benisty H, Joormann J, Kober H, Mishne G. Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment. Soc Neurosci 2024:1-17. [PMID: 39129327 DOI: 10.1080/17470919.2024.2382771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/11/2024] [Indexed: 08/13/2024]
Abstract
Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.
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Affiliation(s)
- Reuma Gadassi-Polack
- Psychiatry, Yale University, New Haven, CT, USA
- School of Behavioral Sciences, Tel-Aviv Yaffo Academic College, Tel Aviv, Israel
| | | | | | - Hadas Benisty
- Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | | | - Hedy Kober
- Psychiatry, Yale University, New Haven, CT, USA
| | - Gal Mishne
- Faculty of Medicine, University of California, San Diego, CA,USA
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Ding C, Li A, Xie S, Tian X, Li K, Fan L, Yan H, Chen J, Chen Y, Wang H, Guo H, Yang Y, Lv L, Wang H, Zhang H, Lu L, Zhang D, Zhang Z, Wang M, Jiang T, Liu B. Mapping Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400929. [PMID: 38900070 PMCID: PMC11348140 DOI: 10.1002/advs.202400929] [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: 01/25/2024] [Revised: 05/22/2024] [Indexed: 06/21/2024]
Abstract
To elucidate the brain-wide information interactions that vary and contribute to individual differences in schizophrenia (SCZ), an information-resolved method is employed to construct individual synergistic and redundant interaction matrices based on regional pairwise BOLD time-series from 538 SCZ and 540 normal controls (NC). This analysis reveals a stable pattern of regionally-specific synergy dysfunction in SCZ. Furthermore, a hierarchical Bayesian model is applied to deconstruct the patterns of whole-brain synergy dysfunction into three latent factors that explain symptom heterogeneity in SCZ. Factor 1 exhibits a significant positive correlation with Positive and Negative Syndrome Scale (PANSS) positive scores, while factor 3 demonstrates significant negative correlations with PANSS negative and general scores. By integrating the neuroimaging data with normative gene expression information, this study identifies that each of these three factors corresponded to a subset of the SCZ risk gene set. Finally, by combining data from NeuroSynth and open molecular imaging sources, along with a spatially heterogeneous mean-field model, this study delineates three SCZ synergy factors corresponding to distinct symptom profiles and implicating unique cognitive, neurodynamic, and neurobiological mechanisms.
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Affiliation(s)
- Chaoyue Ding
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Ang Li
- State Key Laboratory of Brain and Cognitive ScienceInstitute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Sangma Xie
- School of AutomationHangzhou Dianzi UniversityHangzhou310018China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Kunchi Li
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Lingzhong Fan
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Hao Yan
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Jun Chen
- Department of RadiologyRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Yunchun Chen
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Huaning Wang
- Department of PsychiatryXijing HospitalThe Fourth Military Medical UniversityXi'an710032China
| | - Hua Guo
- Zhumadian Psychiatric HospitalZhumadian463000China
| | - Yongfeng Yang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Luxian Lv
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Huiling Wang
- Department of PsychiatryRenmin Hospital of Wuhan UniversityWuhan430060China
| | - Hongxing Zhang
- Department of PsychiatryHenan Mental HospitalThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiang453002China
| | - Lin Lu
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Dai Zhang
- Institute of Mental HealthPeking University Sixth HospitalBeijing100191China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
| | - Tianzi Jiang
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Brainnetome CenterInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
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Wang X, Krieger-Redwood K, Cui Y, Smallwood J, Du Y, Jefferies E. Macroscale brain states support the control of semantic cognition. Commun Biol 2024; 7:926. [PMID: 39090387 PMCID: PMC11294576 DOI: 10.1038/s42003-024-06630-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty - global association and feature matching judgments - which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain's dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
| | | | - Yanni Cui
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | | | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
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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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de Ávila C, Gugula A, Trenk A, Intorcia AJ, Suazo C, Nolz J, Plamondon J, Khatri D, Tallant L, Caron A, Blasiak A, Serrano GE, Beach TG, Gundlach AL, Mastroeni DF. Unveiling a novel memory center in human brain: neurochemical identification of the nucleus incertus, a key pontine locus implicated in stress and neuropathology. Biol Res 2024; 57:46. [PMID: 39014514 PMCID: PMC11253401 DOI: 10.1186/s40659-024-00523-z] [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: 10/13/2023] [Accepted: 06/07/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND The nucleus incertus (NI) was originally described by Streeter in 1903, as a midline region in the floor of the fourth ventricle of the human brain with an 'unknown' function. More than a century later, the neuroanatomy of the NI has been described in lower vertebrates, but not in humans. Therefore, we examined the neurochemical anatomy of the human NI using markers, including the neuropeptide, relaxin-3 (RLN3), and began to explore the distribution of the NI-related RLN3 innervation of the hippocampus. METHODS Histochemical staining of serial, coronal sections of control human postmortem pons was conducted to reveal the presence of the NI by detection of immunoreactivity (IR) for the neuronal markers, microtubule-associated protein-2 (MAP2), glutamic acid dehydrogenase (GAD)-65/67 and corticotrophin-releasing hormone receptor 1 (CRHR1), and RLN3, which is highly expressed in NI neurons in diverse species. RLN3 and vesicular GABA transporter 1 (vGAT1) mRNA were detected by fluorescent in situ hybridization. Pons sections containing the NI from an AD case were immunostained for phosphorylated-tau, to explore potential relevance to neurodegenerative diseases. Lastly, sections of the human hippocampus were stained to detect RLN3-IR and somatostatin (SST)-IR. RESULTS In the dorsal, anterior-medial region of the human pons, neurons containing RLN3- and MAP2-IR, and RLN3/vGAT1 mRNA-positive neurons were observed in an anatomical pattern consistent with that of the NI in other species. GAD65/67- and CRHR1-immunopositive neurons were also detected within this area. Furthermore, RLN3- and AT8-IR were co-localized within NI neurons of an AD subject. Lastly, RLN3-IR was detected in neurons within the CA1, CA2, CA3 and DG areas of the hippocampus, in the absence of RLN3 mRNA. In the DG, RLN3- and SST-IR were co-localized in a small population of neurons. CONCLUSIONS Aspects of the anatomy of the human NI are shared across species, including a population of stress-responsive, RLN3-expressing neurons and a RLN3 innervation of the hippocampus. Accumulation of phosphorylated-tau in the NI suggests its possible involvement in AD pathology. Further characterization of the neurochemistry of the human NI will increase our understanding of its functional role in health and disease.
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Affiliation(s)
- Camila de Ávila
- Arizona State University-Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA.
| | - Anna Gugula
- Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Jagiellonian University, Krakow, Poland
| | - Aleksandra Trenk
- Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Jagiellonian University, Krakow, Poland
| | - Anthony J Intorcia
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Crystal Suazo
- Arizona State University-Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA
| | - Jennifer Nolz
- Arizona State University-Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA
| | | | - Divyanshi Khatri
- Arizona State University-Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA
| | - Lauren Tallant
- Department of Neuroscience, Mayo Clinic, Scottsdale, AZ, USA
| | - Alexandre Caron
- Quebec Heart and Lung Institute, Quebec City, QC, Canada
- Faculty of Pharmacy, Université Laval, Quebec City, QC, Canada
| | - Anna Blasiak
- Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Jagiellonian University, Krakow, Poland
| | - Geidy E Serrano
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Thomas G Beach
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Andrew L Gundlach
- Florey Department of Neuroscience and Mental Health and Department of Anatomy and Physiology and The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Diego F Mastroeni
- Arizona State University-Banner Neurodegenerative Disease Research Center, Tempe, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
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9
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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [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: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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10
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Sun S, Yan C, Qu S, Luo G, Liu X, Tian F, Dong Q, Li X, Hu B. Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111076. [PMID: 38972502 DOI: 10.1016/j.pnpbp.2024.111076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/02/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
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Affiliation(s)
- Shuting Sun
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Fuze Tian
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
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11
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Fan B, Zhou X, Pang L, Long Q, Lv C, Zheng J. Aberrant functional hubs and related networks attributed to cognitive impairment in patients with anti‑N‑methyl‑D‑aspartate receptor encephalitis. Biomed Rep 2024; 21:104. [PMID: 38827495 PMCID: PMC11140295 DOI: 10.3892/br.2024.1792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/06/2024] [Indexed: 06/04/2024] Open
Abstract
Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis results in severe neuropsychiatric symptoms and persistent cognitive impairment; however, the underlying mechanism is still not fully understood. The present study utilized the degree centrality (DC), functional connectivity (FC) and multivariate pattern analysis (MVPA) to further explore neurofunctional symptoms in patients with anti-NMDAR encephalitis. A total of 29 patients with anti-NMDAR encephalitis and 26 healthy controls (HCs) were enrolled for neuropsychological assessment and resting-state functional MRI (rs-fMRI) scans. DC, FC and MVPA were examined to investigate cerebral functional activity and distinguish neuroimaging characteristics between the patient and HC groups based on the rs-fMRI data. Compared with the HCs, the patients exhibited cognitive deficits, anxiety and depression. In the DC analysis, the patients exhibited significantly decreased DC strength in the left rectus gyrus, left caudate nucleus (LCN) and bilateral superior medial frontal gyrus, as well as increased DC strength in the cerebellar anterior lobe, compared with the HCs. In the subsequent FC analysis, the LCN showed decreased FC strength in the bilateral middle frontal gyrus and right precuneus. Furthermore, correlation analysis indicated that disrupted cerebral functional activity was significantly correlated with the alerting effect and Hamilton Depression Scale score. Using DC maps and receiver operating characteristic curve analysis, the MVPA classifier exhibited an area under curve of 0.79, and the accuracy classification rate was 76.36%, with a sensitivity of 79.31% and a specificity of 78.18%. The present study revealed that the disrupted functional activity of hub and related networks in the cerebellum, including the default mode network and executive control network, contributed to deficits in cognition and emotion in patients with anti-NMDAR encephalitis. In conclusion, the present study provided imaging evidence and primary diagnostic markers for pathological and compensatory mechanisms of anti-NMDAR encephalitis, with the aim of improving the understanding of this disease.
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Affiliation(s)
- Binglin Fan
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Linlin Pang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Qijia Long
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Caitiao Lv
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
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12
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Nick Q, Gale DJ, Areshenkoff C, De Brouwer A, Nashed J, Wammes J, Zhu T, Flanagan R, Smallwood J, Gallivan J. Reconfigurations of cortical manifold structure during reward-based motor learning. eLife 2024; 12:RP91928. [PMID: 38916598 PMCID: PMC11198988 DOI: 10.7554/elife.91928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024] Open
Abstract
Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the dorsal attention network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.
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Affiliation(s)
- Qasem Nick
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Daniel J Gale
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Corson Areshenkoff
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Anouk De Brouwer
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Joseph Nashed
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Medicine, Queen's UniversityKingstonCanada
| | - Jeffrey Wammes
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Tianyao Zhu
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
| | - Randy Flanagan
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Jonny Smallwood
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
| | - Jason Gallivan
- Centre for Neuroscience Studies, Queen’s UniversityKingstonCanada
- Department of Psychology, Queen’s UniversityKingstonCanada
- Department of Biomedical and Molecular Sciences, Queen’s UniversityKingstonCanada
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13
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Taylor NL, Whyte CJ, Munn BR, Chang C, Lizier JT, Leopold DA, Turchi JN, Zaborszky L, Műller EJ, Shine JM. Causal evidence for cholinergic stabilization of attractor landscape dynamics. Cell Rep 2024; 43:114359. [PMID: 38870015 PMCID: PMC11255396 DOI: 10.1016/j.celrep.2024.114359] [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: 12/18/2023] [Revised: 04/24/2024] [Accepted: 05/30/2024] [Indexed: 06/15/2024] Open
Abstract
There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.
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Affiliation(s)
- Natasha L Taylor
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Christopher J Whyte
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Catie Chang
- Vanderbilt School of Engineering, Vanderbilt University, Nashville, TN, USA
| | - Joseph T Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, Washington DC, USA; Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda MD, USA
| | - Janita N Turchi
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda MD, USA
| | - Laszlo Zaborszky
- Centre for Molecular & Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ, USA
| | - Eli J Műller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
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14
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Wang X, Krieger-Redwood K, Lyu B, Lowndes R, Wu G, Souter NE, Wang X, Kong R, Shafiei G, Bernhardt BC, Cui Z, Smallwood J, Du Y, Jefferies E. The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge. J Neurosci 2024; 44:e2223232024. [PMID: 38527807 PMCID: PMC11140685 DOI: 10.1523/jneurosci.2223-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 03/27/2024] Open
Abstract
Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Katya Krieger-Redwood
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Baihan Lyu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rebecca Lowndes
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Guowei Wu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nicholas E Souter
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, California 95616
| | - Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Jonathan Smallwood
- Department of Psychology, Queens University, Kingston, Ontario K7L 3N6, Canada
| | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
- Chinese Institute for Brain Research, Beijing 102206, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
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15
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Podvalny E, Sanchez-Romero R, Cole MW. Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities. Cereb Cortex 2024; 34:bhae192. [PMID: 38745558 DOI: 10.1093/cercor/bhae192] [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: 01/18/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024] Open
Abstract
Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.
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Affiliation(s)
- Ella Podvalny
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
| | - Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, United States
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16
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Arafat T, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. Prog Neurobiol 2024; 236:102604. [PMID: 38604584 DOI: 10.1016/j.pneurobio.2024.102604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Thaera Arafat
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Department of Neurology, Duke University School of Medicine and Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27705, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3 BG, United Kingdom
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Queretaro, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.
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17
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Zhu QQ, Tian S, Zhang L, Ding HY, Gao YX, Tang Y, Yang X, Zhu Y, Qi M. Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease. Eur J Neurosci 2024; 59:2391-2402. [PMID: 38314647 DOI: 10.1111/ejn.16267] [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: 03/31/2023] [Revised: 01/12/2024] [Accepted: 01/14/2024] [Indexed: 02/06/2024]
Abstract
The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.
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Affiliation(s)
- Qin-Qin Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shui Tian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ling Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hong-Yuan Ding
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ya-Xin Gao
- Rehabilitation Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Yin Tang
- Department of Medical imaging, Jingjiang People's Hospital, Jingjiang, China
| | - Xi Yang
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Yi Zhu
- Rehabilitation Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ming Qi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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18
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Zou Y, Tong C, Peng W, Qiu Y, Li J, Xia Y, Pei M, Zhang K, Li W, Xu M, Liang Z. Cell-type-specific optogenetic fMRI on basal forebrain reveals functional network basis of behavioral preference. Neuron 2024; 112:1342-1357.e6. [PMID: 38359827 DOI: 10.1016/j.neuron.2024.01.017] [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: 07/31/2023] [Revised: 12/12/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
The basal forebrain (BF) is a complex structure that plays key roles in regulating various brain functions. However, it remains unclear how cholinergic and non-cholinergic BF neurons modulate large-scale functional networks and their relevance in intrinsic and extrinsic behaviors. With an optimized awake mouse optogenetic fMRI approach, we revealed that optogenetic stimulation of four BF neuron types evoked distinct cell-type-specific whole-brain BOLD activations, which could be attributed to BF-originated low-dimensional structural networks. Additionally, optogenetic activation of VGLUT2, ChAT, and PV neurons in the BF modulated the preference for locomotion, exploration, and grooming, respectively. Furthermore, we uncovered the functional network basis of the above BF-modulated behavioral preference through a decoding model linking the BF-modulated BOLD activation, low-dimensional structural networks, and behavioral preference. To summarize, we decoded the functional network basis of differential behavioral preferences with cell-type-specific optogenetic fMRI on the BF and provided an avenue for investigating mouse behaviors from a whole-brain view.
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Affiliation(s)
- Yijuan Zou
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 201602, China
| | - Chuanjun Tong
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 201602, China
| | - Wanling Peng
- Songjiang Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Yue Qiu
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University Shanghai, Shanghai 200032, China
| | - Jiangxue Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100101, China
| | - Ying Xia
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengchao Pei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kaiwei Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Weishuai Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Min Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Zhifeng Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 201602, China.
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19
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Zhou B, Zhao Y, Wu X. Differences of individual gray matter networks between MCI patients who converted to AD within 3 Years and nonconverters. Heliyon 2024; 10:e28874. [PMID: 38623255 PMCID: PMC11016615 DOI: 10.1016/j.heliyon.2024.e28874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
Objective Here we aimed to explore the differences in individual gray matter (GM) networks at baseline in mild cognitive impairment patients who converted to Alzheimer's disease (AD) within 3 years (MCI-C) and nonconverters (MCI-NC). Materials and methods Data from 461 MCI patients (180 MCI-C and 281 MCI-NC) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject, a GM network was constructed using 3D-T1 imaging and the Kullback-Leibler divergence method. Gradient and topological analyses of individual GM networks were performed, and partial correlations were calculated to evaluate relationships among network properties, cognitive function, and apolipoprotein E (APOE) €4 alleles. Subsequently, a support vector machine (SVM) model was constructed to discriminate the MCI-C and MCI-NC patients at baseline. Results The gradient analysis revealed that the principal gradient score distribution was more compressed in the MCI-C group than in the MCI-NC group, with scores for the left lingual gyrus, right fusiform gyrus and left middle temporal gyrus being increased in the MCI-C group (p < 0.05, FDR corrected). The topological analysis showed significant differences in nodal efficiency in four nodes between the two groups. Furthermore, the regional gradient scores or nodal efficiency were found to be significantly related to the neuropsychological test scores, and the left middle temporal gyrus gradient scores were positively associated with the number of APOE €4 alleles (r = 0.192, p = 0.002). Ultimately, the SVM model achieved a balanced accuracy of 79.4% in classifying MCI-C and MCI-NC patients (p < 0.001). Conclusion The whole-brain GM network hierarchy in the MCI-C group was more compressed than that in the MCI-NC group, suggesting more serious cognitive impairments in the MCI-C group. The left middle temporal gyrus gradient scores were related to both cognitive function and APOE €4 alleles, thus serving as potential biomarkers distinguishing MCI-C from MCI-NC at baseline.
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Affiliation(s)
- Baiwan Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yueqi Zhao
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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20
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Rodriguez RX, Noble S, Camp CC, Scheinost D. Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588578. [PMID: 38645002 PMCID: PMC11030410 DOI: 10.1101/2024.04.08.588578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity 1-5 . Further, they resemble task activation patterns and are well-studied 3,5-10 . However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP) 11 and the UCLA Consortium for Neuropsychiatric Phenomics 12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest 13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification 14 . They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.
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21
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Hartwigsen G, Lim JS, Bae HJ, Yu KH, Kuijf HJ, Weaver NA, Biesbroek JM, Kopal J, Bzdok D. Bayesian modelling disentangles language versus executive control disruption in stroke. Brain Commun 2024; 6:fcae129. [PMID: 38707712 PMCID: PMC11069117 DOI: 10.1093/braincomms/fcae129] [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: 10/17/2023] [Revised: 02/06/2024] [Accepted: 04/09/2024] [Indexed: 05/07/2024] Open
Abstract
Stroke is the leading cause of long-term disability worldwide. Incurred brain damage can disrupt cognition, often with persisting deficits in language and executive capacities. Yet, despite their clinical relevance, the commonalities and differences between language versus executive control impairments remain under-specified. To fill this gap, we tailored a Bayesian hierarchical modelling solution in a largest-of-its-kind cohort (1080 patients with stroke) to deconvolve language and executive control with respect to the stroke topology. Cognitive function was assessed with a rich neuropsychological test battery including global cognitive function (tested with the Mini-Mental State Exam), language (assessed with a picture naming task), executive speech function (tested with verbal fluency tasks), executive control functions (Trail Making Test and Digit Symbol Coding Task), visuospatial functioning (Rey Complex Figure), as well as verbal learning and memory function (Soul Verbal Learning). Bayesian modelling predicted interindividual differences in eight cognitive outcome scores three months after stroke based on specific tissue lesion topologies. A multivariate factor analysis extracted four distinct cognitive factors that distinguish left- and right-hemispheric contributions to ischaemic tissue lesions. These factors were labelled according to the neuropsychological tests that had the strongest factor loadings: One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized mental flexibility, task switching and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two distinct factors that were labelled as executive speech functions and verbal memory. Impairments on both factors were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.
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Affiliation(s)
- Gesa Hartwigsen
- Wilhelm Wundt Institute for Psychology, Leipzig University, 04109 Leipzig, Germany
- Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul, 13620, South Korea
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, 14068, Republic of Korea
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Nick A Weaver
- Department of Neurology and Neurosurgery, Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, Utrecht Brain Center, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
- Department of Neurology, Diakonessenhuis Hospital, 3582 KE Utrecht, The Netherlands
| | - Jakub Kopal
- Department of Biomedical Engineering, Faculty of Medicine, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2BA, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Quebec H2S 3H1, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Faculty of Medicine, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2BA, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Quebec H2S 3H1, Canada
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22
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Corriveau-Lecavalier N, Barnard LR, Botha H, Graff-Radford J, Ramanan VK, Lee J, Dicks E, Rademakers R, Boeve BF, Machulda MM, Fields JA, Dickson DW, Graff-Radford N, Knopman DS, Lowe VJ, Petersen RC, Jack CR, Jones DT. Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases. Brain 2024; 147:1483-1496. [PMID: 37831661 PMCID: PMC10994526 DOI: 10.1093/brain/awad356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/28/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.
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Affiliation(s)
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Molecular Neurology, Antwerp University, Antwerp, Belgium
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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23
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Podvalny E, Sanchez-Romero R, Cole MW. Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574917. [PMID: 38617344 PMCID: PMC11014470 DOI: 10.1101/2024.01.09.574917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.
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Affiliation(s)
- Ella Podvalny
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Ruben Sanchez-Romero
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Michael W. Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
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24
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Roeder L, Breakspear M, Kerr GK, Boonstra TW. Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait. iScience 2024; 27:109162. [PMID: 38414847 PMCID: PMC10897916 DOI: 10.1016/j.isci.2024.109162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/16/2023] [Accepted: 02/05/2024] [Indexed: 02/29/2024] Open
Abstract
Walking is a complex motor activity that requires coordinated interactions between the sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young people, older people, and individuals with Parkinson's disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralized network activated during the left swing phase, and a right-lateralized network active during the right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. Individuals with the impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interindividual differences in neuromuscular connectivity during gait.
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Affiliation(s)
- Luisa Roeder
- School of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, QLD, Australia
- Chair of Human Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Michael Breakspear
- College of Engineering Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia
| | - Graham K Kerr
- School of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Tjeerd W Boonstra
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
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25
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Nanni-Zepeda M, DeGutis J, Wu C, Rothlein D, Fan Y, Grimm S, Walter M, Esterman M, Zuberer A. Neural signatures of shared subjective affective engagement and disengagement during movie viewing. Hum Brain Mapp 2024; 45:e26622. [PMID: 38488450 DOI: 10.1002/hbm.26622] [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] [Revised: 01/10/2024] [Accepted: 01/26/2024] [Indexed: 03/19/2024] Open
Abstract
When watching a negative emotional movie, we differ from person to person in the ease with which we engage and the difficulty with which we disengage throughout a temporally evolving narrative. We investigated neural responses of emotional processing, by considering inter-individual synchronization in subjective emotional engagement and disengagement. The neural underpinnings of these shared responses are ideally studied in naturalistic scenarios like movie viewing, wherein individuals emotionally engage and disengage at their own time and pace throughout the course of a narrative. Despite the rich data that naturalistic designs can bring to the study, there is a challenge in determining time-resolved behavioral markers of subjective engagement and disengagement and their underlying neural responses. We used a within-subject cross-over design instructing 22 subjects to watch clips of either neutral or sad content while undergoing functional magnetic resonance imaging (fMRI). Participants watched the same movies a second time while continuously annotating the perceived emotional intensity, thus enabling the mapping of brain activity and emotional experience. Our analyses revealed that between-participant similarity in waxing (engagement) and waning (disengagement) of emotional intensity was directly related to the between-participant similarity in spatiotemporal patterns of brain activation during the movie(s). Similar patterns of engagement reflected common activation in the bilateral ventromedial prefrontal cortex, regions often involved in self-referenced evaluation and generation of negative emotions. Similar patterns of disengagement reflected common activation in central executive and default mode network regions often involved in top-down emotion regulation. Together this work helps to better understand cognitive and neural mechanisms underpinning engagement and disengagement from emotionally evocative narratives.
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Affiliation(s)
- Melanni Nanni-Zepeda
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Joseph DeGutis
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Charley Wu
- Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany
| | - David Rothlein
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Yan Fan
- Department Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at the TU Dortmund (IfADo), Dortmund, Germany
| | - Simone Grimm
- Berlin Institute of Health, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Department of Psychology, MSB Medical School Berlin, Berlin, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Clinical Affective Neuroimaging Laboratory, Otto-von-Guericke-University, Magdeburg, Germany
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Michael Esterman
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA
- National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Agnieszka Zuberer
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, Massachusetts, USA
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26
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Jacob LPL, Bailes SM, Williams SD, Stringer C, Lewis LD. Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577429. [PMID: 38352426 PMCID: PMC10862763 DOI: 10.1101/2024.01.29.577429] [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: 02/22/2024]
Abstract
The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.
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Affiliation(s)
- Leandro P L Jacob
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney M Bailes
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Boston University, Boston, MA, USA
| | - Stephanie D Williams
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Boston University, Boston, MA, USA
| | | | - Laura D Lewis
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA USA
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27
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Dong X, Li Q, Wang X, He Y, Zeng D, Chu L, Zhao K, Li S. How brain structure-function decoupling supports individual cognition and its molecular mechanism. Hum Brain Mapp 2024; 45:e26575. [PMID: 38339909 PMCID: PMC10826895 DOI: 10.1002/hbm.26575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 02/12/2024] Open
Abstract
Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.
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Affiliation(s)
- Xiaoxi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Xuetong Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical EngineeringBeihang UniversityBeijingChina
| | - Lei Chu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical EngineeringBeihang UniversityBeijingChina
| | - Kun Zhao
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
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28
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge time series components of functional connectivity and cognitive function in Alzheimer's disease. Brain Imaging Behav 2024; 18:243-255. [PMID: 38008852 PMCID: PMC10844434 DOI: 10.1007/s11682-023-00822-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2023] [Indexed: 11/28/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA.
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA.
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA.
| | - Sarah A Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Shannon L Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Liana G Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Brenna C McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
- Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Psychology Building 308, 1101 E 10th St, Bloomington, IN, 47405, USA
- Indiana University Network Sciences Institute, IU, Bloomington, IN, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, IUSM, Indianapolis, IN, USA
- Program in Neuroscience, IU, Bloomington, IN, USA
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, USA
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29
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose RF, Camacho MC, Alexopoulos D, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal cortical surface. Cereb Cortex 2024; 34:bhae047. [PMID: 38372292 PMCID: PMC10875653 DOI: 10.1093/cercor/bhae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/20/2024] Open
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Jiaxin C Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
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30
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Choi H, Byeon K, Lee J, Hong S, Park B, Park H. Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning. Hum Brain Mapp 2024; 45:e26581. [PMID: 38224537 PMCID: PMC10789215 DOI: 10.1002/hbm.26581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean ± standard deviation [SD] age = 47.07 ± 18.89 years; 67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean ± SD age = 38.97 ± 19.80 years; 35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity.
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Affiliation(s)
- Hyoungshin Choi
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
| | | | - Jong‐eun Lee
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
| | - Seok‐Jun Hong
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- Center for the Developing BrainChild Mind InstituteNew YorkUSA
- Department of Biomedical EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
| | - Bo‐yong Park
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- Department of Data ScienceInha UniversityIncheonRepublic of Korea
- Department of Statistics and Data ScienceInha UniversityIncheonRepublic of Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- School of Electronic and Electrical EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
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31
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Duong-Tran D, Kaufmann R, Chen J, Wang X, Garai S, Xu F, Bao J, Amico E, Kaplan AD, Petri G, Goni J, Zhao Y, Shen L. Homological landscape of human brain functional sub-circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573062. [PMID: 38187668 PMCID: PMC10769445 DOI: 10.1101/2023.12.22.573062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H 1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H 0 , ii) Motor task H 1 , and iii) Working memory task H 2 . At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Ralph Kaufmann
- Department of Mathematics, Purdue University, West Lafayette, IN, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA, USA
| | - Xuan Wang
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Enrico Amico
- Neuro-X Institute, EPFL, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Giovanni Petri
- CENTAI Institute, 10138 Torino, Italy
- NPLab, Network Science Institute, Northeastern University London, London, E1W 1LP, United Kingdom
- Networks Unit, IMT Lucca Institute, 55100 Lucca, Italy
| | - Joaquin Goni
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, US
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA, USA
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32
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Pak V, Hashmi JA. Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks. Netw Neurosci 2023; 7:1248-1265. [PMID: 38144683 PMCID: PMC10631789 DOI: 10.1162/netn_a_00328] [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: 11/14/2022] [Accepted: 06/23/2023] [Indexed: 12/26/2023] Open
Abstract
Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.
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Affiliation(s)
- Veronika Pak
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | - Javeria Ali Hashmi
- Department of Anesthesia, Pain Management, and Perioperative Medicine, Nova Scotia Health Authority, Halifax, NS, Canada
- Dalhousie University, Halifax, NS, Canada
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33
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Capouskova K, Zamora‐López G, Kringelbach ML, Deco G. Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest. Hum Brain Mapp 2023; 44:6349-6363. [PMID: 37846551 PMCID: PMC10681658 DOI: 10.1002/hbm.26511] [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: 05/23/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short-lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Gorka Zamora‐López
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical MedicineAarhus UniversityAarhusDenmark
- Centre for Eudaimonia and Human Flourishing, Linacre CollegeUniversity of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
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34
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Brown JA, Lee AJ, Fernhoff K, Pistone T, Pasquini L, Wise AB, Staffaroni AM, Luisa Mandelli M, Lee SE, Boxer AL, Rankin KP, Rabinovici GD, Luisa Gorno Tempini M, Rosen HJ, Kramer JH, Miller BL, Seeley WW. Functional network collapse in neurodegenerative disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569654. [PMID: 38106054 PMCID: PMC10723363 DOI: 10.1101/2023.12.01.569654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Cognitive and behavioral deficits in Alzheimer's disease (AD) and frontotemporal dementia (FTD) result from brain atrophy and altered functional connectivity. However, it is unclear how atrophy relates to functional connectivity disruptions across dementia subtypes and stages. We addressed this question using structural and functional MRI from 221 patients with AD (n=82), behavioral variant FTD (n=41), corticobasal syndrome (n=27), nonfluent (n=34) and semantic (n=37) variant primary progressive aphasia, and 100 cognitively normal individuals. Using partial least squares regression, we identified three principal structure-function components. The first component showed overall atrophy correlating with primary cortical hypo-connectivity and subcortical/association cortical hyper-connectivity. Components two and three linked focal syndrome-specific atrophy to peri-lesional hypo-connectivity and distal hyper-connectivity. Structural and functional component scores predicted global and domain-specific cognitive deficits. Anatomically, functional connectivity changes reflected alterations in specific brain activity gradients. Eigenmode analysis identified temporal phase and amplitude collapse as an explanation for atrophy-driven functional connectivity changes.
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Affiliation(s)
- Jesse A. Brown
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Alex J. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Kristen Fernhoff
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Taylor Pistone
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Lorenzo Pasquini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Amy B. Wise
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam M. Staffaroni
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Mandelli
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Suzee E. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam L. Boxer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Katherine P. Rankin
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Gil D. Rabinovici
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Gorno Tempini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Howard J. Rosen
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Joel H. Kramer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Bruce L. Miller
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - William W. Seeley
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
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35
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Chumin EJ, Cutts SA, Risacher SL, Apostolova LG, Farlow MR, McDonald BC, Wu YC, Betzel R, Saykin AJ, Sporns O. Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.13.23289936. [PMID: 38014005 PMCID: PMC10680898 DOI: 10.1101/2023.05.13.23289936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
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Affiliation(s)
- Evgeny J. Chumin
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Shannon L. Risacher
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Liana G. Apostolova
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Martin R. Farlow
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Brenna C. McDonald
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Yu-Chien Wu
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
| | - Andrew J. Saykin
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
- Department of Neurology, IUSM, Indianapolis, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University (IU), Bloomington, IN, United States
- Indiana University Network Sciences Institute, IU, Bloomington, IN, United States
- Stark Neurosciences Research Institute, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States
- Indiana Alzheimer’s Disease Research Center, IUSM, Indianapolis, IN, United States
- Program in Neuroscience, IU, Bloomington, IN, United States
- Department of Radiology and Imaging Sciences, IUSM, Indianapolis, IN, United States
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36
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose R, Camacho MC, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566629. [PMID: 37986902 PMCID: PMC10659431 DOI: 10.1101/2023.11.10.566629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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Lee JJ, Earnest T, Ha SM, Bani A, Kothapalli D, Liu P, Sotiras A. Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298396. [PMID: 38116031 PMCID: PMC10729728 DOI: 10.1101/2023.11.10.23298396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.
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Jajcay N, Hlinka J. Towards a dynamical understanding of microstate analysis of M/EEG data. Neuroimage 2023; 281:120371. [PMID: 37716592 DOI: 10.1016/j.neuroimage.2023.120371] [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: 04/13/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.
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Affiliation(s)
- Nikola Jajcay
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
| | - Jaroslav Hlinka
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
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Munn BR, Müller EJ, Medel V, Naismith SL, Lizier JT, Sanders RD, Shine JM. Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states. Nat Commun 2023; 14:6846. [PMID: 37891167 PMCID: PMC10611774 DOI: 10.1038/s41467-023-42465-2] [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: 11/29/2022] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.
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Affiliation(s)
- Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
| | - Eli J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Vicente Medel
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Sharon L Naismith
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Psychology, Faculty of Science & Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Joseph T Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Robert D Sanders
- Department of Anaesthetics & Institute of Academic Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Central Clinical School & NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
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40
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Hansen JY, Cauzzo S, Singh K, García-Gomar MG, Shine JM, Bianciardi M, Misic B. Integrating brainstem and cortical functional architectures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564245. [PMID: 37961347 PMCID: PMC10634864 DOI: 10.1101/2023.10.26.564245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The brainstem is a fundamental component of the central nervous system yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. Here we use high-resolution 7 Tesla fMRI to derive a functional connectome encompassing cortex as well as 58 brainstem nuclei spanning the midbrain, pons and medulla. We identify a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as multiple emergent phenomena including neurophysiological oscillatory rhythms, patterns of cognitive functional specialization, and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicate all findings using 3 Tesla data from the same participants. Collectively, we find that multiple organizational features of cortical activity can be traced back to the brainstem.
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Affiliation(s)
- Justine Y. Hansen
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Parkinson’s Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - James M. Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard University, Boston, MA, USA
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, QC, Canada
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41
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Kember J, Stepien L, Panda E, Tekok-Kilic A. Resting-state EEG dynamics help explain differences in response control in ADHD: Insight into electrophysiological mechanisms and sex differences. PLoS One 2023; 18:e0277382. [PMID: 37796795 PMCID: PMC10553225 DOI: 10.1371/journal.pone.0277382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 05/12/2023] [Indexed: 10/07/2023] Open
Abstract
Reductions in response control (greater reaction time variability and commission error rate) are consistently observed in those diagnosed with attention-deficit/hyperactivity disorder (ADHD). Previous research suggests these reductions arise from a dysregulation of large-scale cortical networks. Here, we extended our understanding of this cortical-network/response-control pathway important to the neurobiology of ADHD. First, we assessed how dynamic changes in three resting-state EEG network properties thought to be relevant to ADHD (phase-synchronization, modularity, oscillatory power) related with response control during a simple perceptual decision-making task in 112 children/adolescents (aged 8-16) with and without ADHD. Second, we tested whether these associations differed in males and females who were matched in age, ADHD-status and ADHD- subtype. We found that changes in oscillatory power (as opposed to phase-synchrony and modularity) are most related with response control, and that this relationship is stronger in ADHD compared to controls. Specifically, a tendency to dwell in an electrophysiological state characterized by high alpha/beta power (8-12/13-30Hz) and low delta/theta power (1-3/4-7Hz) supported response control, particularly in those with ADHD. Time in this state might reflect an increased initiation of alpha-suppression mechanisms, recruited by those with ADHD to suppress processing unfavourable to response control. We also found marginally significant evidence that this relationship is stronger in males compared to females, suggesting a distinct etiology for response control in the female presentation of ADHD.
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Affiliation(s)
- Jonah Kember
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Child and Youth Studies, Brock University, St. Catharine’s, Ontario, Canada
| | - Lauren Stepien
- Department of Child and Youth Studies, Brock University, St. Catharine’s, Ontario, Canada
| | - Erin Panda
- Department of Child and Youth Studies, Brock University, St. Catharine’s, Ontario, Canada
| | - Ayda Tekok-Kilic
- Department of Child and Youth Studies, Brock University, St. Catharine’s, Ontario, Canada
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42
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Cropper EC, Perkins M, Jing J. Persistent modulatory actions and task switching in the feeding network of Aplysia. Curr Opin Neurobiol 2023; 82:102775. [PMID: 37625344 PMCID: PMC10530010 DOI: 10.1016/j.conb.2023.102775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 07/26/2023] [Accepted: 08/01/2023] [Indexed: 08/27/2023]
Abstract
The activity of multifunctional networks is configured by neuromodulators that exert persistent effects. This raises a question, does this impact the ability of a network to switch from one type of activity to another? We review studies that have addressed this question in the Aplysia feeding circuit. Task switching in this system occurs "asymmetrically." When there is a switch from egestion to ingestion neuromodulation impedes switching (creates a "negative bias"). When there is a switch from ingestion to egestion the biasing is "positive." Ingestion promotes subsequent egestion. We contrast mechanisms responsible for the two types of biasing and show that the observed asymmetry is a consequence of the fact that there is more than one set of egestive circuit parameters.
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Affiliation(s)
- Elizabeth C Cropper
- Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Matthew Perkins
- Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Jian Jing
- Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; State Key Laboratory of Pharmaceutical Biotechnology, Institute for Brain Sciences, Chemistry and Biomedicine Innovation Center, Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, Advanced Institute for Life Sciences, School of Life Sciences, Nanjing University, Nanjing, Jiangsu, China
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43
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Zhang J, Yang Y, Liu T, Shi Z, Pei G, Wang L, Wu J, Funahashi S, Suo D, Wang C, Yan T. Functional connectivity in people at clinical and familial high risk for schizophrenia. Psychiatry Res 2023; 328:115464. [PMID: 37690192 DOI: 10.1016/j.psychres.2023.115464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/12/2023]
Abstract
Patients diagnosed with schizophrenia (SZ) exhibit compromised functional connectivity within extensive brain networks. However, the precise development of this impairment during disease progression in the clinical high-risk (CHR) population and their relatives remains unclear. Our study leveraged data from 128 resting electroencephalography (EEG) channels acquired from 30 SZ patients, 21 CHR individuals, 17 unaffected healthy relatives (RSs; those at heightened SZ risk due to family history), and 31 healthy controls (HCs). These data were harnessed to establish functional connectivity patterns. By calculating the geometric distance between EEG sequences, we unveiled local and global nonlinear relationships within the entire brain. The process of dimensionality reduction led to low-dimensional representations, providing insights into high-dimensional EEG data. Our findings indicated that CHR participants exhibited aberrant functional connectivity across hemispheres, whereas RS individuals showcased anomalies primarily concentrated within hemispheres. In the realm of low-dimensional analysis, RS participants' third-dimensional occipital lobe values lay between those of the CHR individuals and HCs, significantly correlating with scale scores. This low-dimensional approach facilitated the visualization of brain states, potentially offering enhanced comprehension of brain structure, function, and early-stage functional impairment, such as occipital visual deficits, in RS individuals before cognitive decline onset.
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Affiliation(s)
- Jian Zhang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Yaxin Yang
- School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Guangying Pei
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China
| | - Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, 5 South Zhongguancun St, Haidian District, Beijing 100081, China.
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44
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Cai W, Mizuno Y, Tomoda A, Menon V. Bayesian dynamical system analysis of the effects of methylphenidate in children with attention-deficit/hyperactivity disorder: a randomized trial. Neuropsychopharmacology 2023; 48:1690-1698. [PMID: 37491674 PMCID: PMC10516959 DOI: 10.1038/s41386-023-01668-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/24/2023] [Accepted: 07/11/2023] [Indexed: 07/27/2023]
Abstract
Methylphenidate is a widely used and effective treatment for attention-deficit/hyperactivity disorder (ADHD), yet the underlying neural mechanisms and their relationship to changes in behavior are not fully understood. Specifically, it remains unclear how methylphenidate affects brain and behavioral dynamics, and the interplay between these dynamics, in individuals with ADHD. To address this gap, we used a novel Bayesian dynamical system model to investigate the effects of methylphenidate on latent brain states in 27 children with ADHD and 49 typically developing children using a double-blind, placebo-controlled crossover design. Methylphenidate remediated greater behavioral variability on a continuous performance task in children with ADHD. Children with ADHD exhibited aberrant latent brain state dynamics compared to typically developing children, with a single latent state showing particularly abnormal dynamics, which was remediated by methylphenidate. Additionally, children with ADHD showed brain state-dependent hyper-connectivity in the default mode network, which was also remediated by methylphenidate. Finally, we found that methylphenidate-induced changes in latent brain state dynamics, as well as brain state-related functional connectivity between salience and default mode networks, were correlated with improvements in behavioral variability. Taken together, our findings reveal a novel latent brain state dynamical process and circuit mechanism underlying the therapeutic effects of methylphenidate in childhood ADHD. We suggest that Bayesian dynamical system models may be particularly useful for capturing complex nonlinear changes in neural activity and behavioral variability associated with ADHD. Our approach may be of value to clinicians and researchers investigating the neural mechanisms underlying pharmacological treatment of psychiatric disorders.
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Affiliation(s)
- Weidong Cai
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, USA.
| | - Yoshifumi Mizuno
- Research Center for Child Mental Development, University of Fukui, Fukui, 910-1193, Japan
- Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Fukui, 910-1193, Japan
- Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, 910-1193, Japan
| | - Akemi Tomoda
- Research Center for Child Mental Development, University of Fukui, Fukui, 910-1193, Japan
- Division of Developmental Higher Brain Functions, United Graduate School of Child Development, University of Fukui, Fukui, 910-1193, Japan
- Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, 910-1193, Japan
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, USA.
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, USA.
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45
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Qin S. Emotion representations in context: maturation and convergence pathways. Trends Cogn Sci 2023; 27:883-885. [PMID: 37598002 DOI: 10.1016/j.tics.2023.07.009] [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: 07/10/2023] [Accepted: 07/23/2023] [Indexed: 08/21/2023]
Abstract
How does the human brain develop stable emotion representations? According to recent work by Camacho et al., neural representations of contextualized emotional cues are distinct and fairly stable by mid-to-late childhood and activation patterns become increasingly similar between individuals during adolescence. Here, I propose a framework for investigating contextualized emotion processing.
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Affiliation(s)
- Shaozheng Qin
- IDG/McGovern Institute for Brain Research & State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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46
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Hartwigsen G, Lim JS, Bae HJ, Yu KH, Kuijf HJ, Weaver NA, Biesbroek JM, Kopal J, Bzdok D. Bayesian modeling disentangles language versus executive control disruption in stroke. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.06.552147. [PMID: 37609325 PMCID: PMC10441359 DOI: 10.1101/2023.08.06.552147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Stroke is the leading cause of long-term disability worldwide. Incurred brain damage disrupts cognition, often with persisting deficits in language and executive capacities. Despite their clinical relevance, the commonalities, and differences of language versus executive control impairments remain under-specified. We tailored a Bayesian hierarchical modeling solution in a largest-of-its-kind cohort (1080 stroke patients) to deconvolve language and executive control in the brain substrates of stroke insults. Four cognitive factors distinguished left- and right-hemispheric contributions to ischemic tissue lesion. One factor delineated language and general cognitive performance and was mainly associated with damage to left-hemispheric brain regions in the frontal and temporal cortex. A factor for executive control summarized control and visual-constructional abilities. This factor was strongly related to right-hemispheric brain damage of posterior regions in the occipital cortex. The interplay of language and executive control was reflected in two factors: executive speech functions and verbal memory. Impairments on both were mainly linked to left-hemispheric lesions. These findings shed light onto the causal implications of hemispheric specialization for cognition; and make steps towards subgroup-specific treatment protocols after stroke.
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47
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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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48
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Chen X, Ren H, Tang Z, Zhou K, Zhou L, Zuo Z, Cui X, Chen X, Liu Z, He Y, Liao X. Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain. Commun Biol 2023; 6:892. [PMID: 37652993 PMCID: PMC10471630 DOI: 10.1038/s42003-023-05262-7] [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: 04/28/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023] Open
Abstract
Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.
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Affiliation(s)
- Xi Chen
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Haoda Ren
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Zhonghua Tang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaohua Cui
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Xiaosong Chen
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Zonghua Liu
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai, 200241, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
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49
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Müller EJ, Munn BR, Redinbaugh MJ, Lizier J, Breakspear M, Saalmann YB, Shine JM. The non-specific matrix thalamus facilitates the cortical information processing modes relevant for conscious awareness. Cell Rep 2023; 42:112844. [PMID: 37498741 DOI: 10.1016/j.celrep.2023.112844] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/25/2023] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of large-scale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.
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Affiliation(s)
- Eli J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
| | - Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | | | - Joseph Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | | | - Yuri B Saalmann
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Centre, Madison, WI, USA
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
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50
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Froudist-Walsh S, Xu T, Niu M, Rapan L, Zhao L, Margulies DS, Zilles K, Wang XJ, Palomero-Gallagher N. Gradients of neurotransmitter receptor expression in the macaque cortex. Nat Neurosci 2023; 26:1281-1294. [PMID: 37336976 PMCID: PMC10322721 DOI: 10.1038/s41593-023-01351-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/01/2023] [Indexed: 06/21/2023]
Abstract
Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.
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MESH Headings
- Aged
- Animals
- Female
- Humans
- Male
- Rats
- Autoradiography
- Brain Mapping
- Cerebral Cortex/cytology
- Cerebral Cortex/metabolism
- Cognition
- Dendritic Spines
- Gyrus Cinguli/cytology
- Gyrus Cinguli/metabolism
- Macaca fascicularis
- Rats, Inbred Lew
- Receptor, Serotonin, 5-HT1A/analysis
- Receptor, Serotonin, 5-HT1A/metabolism
- Receptors, Cholinergic/analysis
- Receptors, Cholinergic/metabolism
- Receptors, Dopamine/analysis
- Receptors, Dopamine/metabolism
- Receptors, Neurotransmitter/analysis
- Receptors, Neurotransmitter/metabolism
- Serotonin/metabolism
- Species Specificity
- Myelin Sheath/metabolism
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Affiliation(s)
- Sean Froudist-Walsh
- Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, Bristol, UK
- Center for Neural Science, New York University, New York, NY, USA
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | - Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Lucija Rapan
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Ling Zhao
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, University of Paris Cité, Paris, France
| | | | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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