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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024; 25:688-704. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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2
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Wang X, Padawer-Curry JA, Bice AR, Kim B, Rosenthal ZP, Lee JM, Goyal MS, Macauley SL, Bauer AQ. Spatiotemporal relationships between neuronal, metabolic, and hemodynamic signals in the awake and anesthetized mouse brain. Cell Rep 2024; 43:114723. [PMID: 39277861 DOI: 10.1016/j.celrep.2024.114723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 07/08/2024] [Accepted: 08/21/2024] [Indexed: 09/17/2024] Open
Abstract
Neurovascular coupling (NVC) and neurometabolic coupling (NMC) provide the basis for functional magnetic resonance imaging and positron emission tomography to map brain neurophysiology. While increases in neuronal activity are often accompanied by increases in blood oxygen delivery and oxidative metabolism, these observations are not the rule. This decoupling is important when interpreting brain network organization (e.g., resting-state functional connectivity [RSFC]) because it is unclear whether changes in NMC/NVC affect RSFC measures. We leverage wide-field optical imaging in Thy1-jRGECO1a mice to map cortical calcium activity in pyramidal neurons, flavoprotein autofluorescence (representing oxidative metabolism), and hemodynamic activity during wake and ketamine/xylazine anesthesia. Spontaneous dynamics of all contrasts exhibit patterns consistent with RSFC. NMC/NVC relative to excitatory activity varies over the cortex. Ketamine/xylazine profoundly alters NVC but not NMC. Compared to awake RSFC, ketamine/xylazine affects metabolic-based connectomes moreso than hemodynamic-based measures of RSFC. Anesthesia-related differences in NMC/NVC timing do not appreciably alter RSFC structure.
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Affiliation(s)
- Xiaodan Wang
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA
| | - Jonah A Padawer-Curry
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Imaging Sciences Program, Washington University in Saint Louis, St. Louis, MO 63130, USA
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Byungchan Kim
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Zachary P Rosenthal
- Department of Psychiatry, University of Pennsylvania Health System Penn Medicine, Philadelphia, PA 19104, USA
| | - Jin-Moo Lee
- Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Manu S Goyal
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Shannon L Macauley
- Department of Physiology, University of Kentucky, Lexington, KY 40508, USA
| | - Adam Q Bauer
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO 63130, USA; Imaging Sciences Program, Washington University in Saint Louis, St. Louis, MO 63130, USA.
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3
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Kim J, Gim S, Yoo SBM, Woo CW. A computational mechanism of cue-stimulus integration for pain in the brain. SCIENCE ADVANCES 2024; 10:eado8230. [PMID: 39259795 PMCID: PMC11389792 DOI: 10.1126/sciadv.ado8230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 08/02/2024] [Indexed: 09/13/2024]
Abstract
The brain integrates information from pain-predictive cues and noxious inputs to construct the pain experience. Although previous studies have identified neural encodings of individual pain components, how they are integrated remains elusive. Here, using a cue-induced pain task, we examined temporal functional magnetic resonance imaging activities within the state space, where axes represent individual voxel activities. By analyzing the features of these activities at the large-scale network level, we demonstrated that overall brain networks preserve both cue and stimulus information in their respective subspaces within the state space. However, only higher-order brain networks, including limbic and default mode networks, could reconstruct the pattern of participants' reported pain by linear summation of subspace activities, providing evidence for the integration of cue and stimulus information. These results suggest a hierarchical organization of the brain for processing pain components and elucidate the mechanism for their integration underlying our pain perception.
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Affiliation(s)
- Jungwoo Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Suhwan Gim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Seng Bum Michael Yoo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
- Department of Neurosurgery and McNair Scholar Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
- Life-inspired Neural Network for Prediction and Optimization Research Group, Suwon, South Korea
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4
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Vallar G. Body schema and body image as internal representations of the body, and their disorders. An historical review. J Neuropsychol 2024. [PMID: 39245899 DOI: 10.1111/jnp.12389] [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: 03/21/2024] [Accepted: 08/13/2024] [Indexed: 09/10/2024]
Abstract
Since the early 1900s, the terms body schema and body image denoted the internal representations of the body. Bonnier's (1905, Revue Neurologique, 13, 605) schema is a conscious spatial representation of the size, shape, and position of the body, and of body parts, whose dysfunction brings about aschématia, and hypo-, hyper-, and paraschématia. The two schemata of Head and Holmes (1911, Brain, 34, 102) are an unconscious plastic postural schema, for the maintenance of posture and balance and for the coding of the position of body parts, and a conscious superficial schema, for the localisation of somatosensory stimuli. Pick's (1922, Psychologische Forschung, 1, 303) body schema refers to a structural description of the body, including the position of body parts and their spatial relationships, defective in autotopagnosia. Schilder's (1935, The image and appearance of the human body) body image is a comprehensive construct, covering physiological, evolutional, neurological and neuropsychological, psychiatric and sociological aspects. Lhermitte's (1939, L'image de notre corps) image, based on the views of the abovementioned authors, is defective in bodily neuropsychological disorders. The two terms have been used interchangeably, to denote (hemi-)asomatognosia, anosognosia, autotopagnosia, depersonalisation, personal neglect, phantom and supernumerary limbs, somatoparaphrenia. Their properties have been summarized with general dichotomies: schema for action in space ("where" system), image for perception ("what" system), after primary sensory processing. While schema and image fractionated into multiple representations of aspects of the body, the two terms are still used to refer to some of these representations, and to their disorders.
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Affiliation(s)
- Giuseppe Vallar
- Department of Psychology, and Mind and Behavior Technological Center - Mibtec, University of Milano-Bicocca, Milan, Italy
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5
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Rodríguez-Nieto G, Alvarez-Anacona DF, Mantini D, Edden RAE, Oeltzschner G, Sunaert S, Swinnen SP. Association between Inhibitory-Excitatory Balance and Brain Activity Response during Cognitive Flexibility in Young and Older Individuals. J Neurosci 2024; 44:e0355242024. [PMID: 39134417 PMCID: PMC11376334 DOI: 10.1523/jneurosci.0355-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 06/21/2024] [Accepted: 07/05/2024] [Indexed: 09/06/2024] Open
Abstract
Cognitive flexibility represents the capacity to switch among different mental schemes, providing an adaptive advantage to a changing environment. The neural underpinnings of this executive function have been deeply studied in humans through fMRI, showing that the left inferior frontal cortex (IFC) and the left inferior parietal lobule (IPL) are crucial. Here, we investigated the inhibitory-excitatory balance in these regions by means of γ-aminobutyric acid (GABA+) and glutamate + glutamine (Glx), measured with magnetic resonance spectroscopy, during a cognitive flexibility task and its relationship with the performance level and the local task-induced blood oxygenation level-dependent (BOLD) response in 40 young (18-35 years; 26 female) and 40 older (18-35 years; 21 female) human adults. As the IFC and the IPL are richly connected regions, we also examined whole-brain effects associated with their local metabolic activity. Results did not show absolute metabolic modulations associated with flexibility performance, but the performance level was related to the direction of metabolic modulation in the IPL with opposite patterns in young and older individuals. The individual inhibitory-excitatory balance modulation showed an inverse relationship with the local BOLD response in the IPL. Finally, the modulation of inhibitory-excitatory balance in IPL was related to whole-brain effects only in older individuals. These findings show disparities in the metabolic mechanisms underlying cognitive flexibility in young and older adults and their association with the performance level and BOLD response. Such metabolic differences are likely to play a role in executive functioning during aging and specifically in cognitive flexibility.
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Affiliation(s)
- Geraldine Rodríguez-Nieto
- Movement Control and Neuroplasticity Research Group, Biomedical Sciences, KU Leuven, Leuven 3001, Belgium
- Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, Biomedical Sciences, KU Leuven, Leuven 3001, Belgium
- Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Stefan Sunaert
- Department of Imaging and Pathology, Biomedical Sciences, KU Leuven, Leuven 3000, Belgium
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Biomedical Sciences, KU Leuven, Leuven 3001, Belgium
- Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium
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6
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Miller JA, Constantinidis C. Timescales of learning in prefrontal cortex. Nat Rev Neurosci 2024; 25:597-610. [PMID: 38937654 DOI: 10.1038/s41583-024-00836-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 06/29/2024]
Abstract
The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA.
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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7
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Soylu F. A new ontology for numerical cognition: Integrating evolutionary, embodied, and data informatics approaches. Acta Psychol (Amst) 2024; 249:104416. [PMID: 39121614 DOI: 10.1016/j.actpsy.2024.104416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/07/2024] [Accepted: 07/16/2024] [Indexed: 08/12/2024] Open
Abstract
Numerical cognition is a field that investigates the sociocultural, developmental, cognitive, and biological aspects of mathematical abilities. Recent findings in cognitive neuroscience suggest that cognitive skills are facilitated by distributed, transient, and dynamic networks in the brain, rather than isolated functional modules. Further, research on the bodily and evolutionary bases of cognition reveals that our cognitive skills harness capacities originally evolved for action and that cognition is best understood in conjunction with perceptuomotor capacities. Despite these insights, neural models of numerical cognition struggle to capture the relation between mathematical skills and perceptuomotor systems. One front to addressing this issue is to identify building block sensorimotor processes (BBPs) in the brain that support numerical skills and develop a new ontology connecting the sensorimotor system with mathematical cognition. BBPs here are identified as sensorimotor functions, associated with distributed networks in the brain, and are consistently identified as supporting different cognitive abilities. BBPs can be identified with new approaches to neuroimaging; by examining an array of sensorimotor and cognitive tasks in experimental designs, employing data-driven informatics approaches to identify sensorimotor networks supporting cognitive processes, and interpreting the results considering the evolutionary and bodily foundations of mathematical abilities. New empirical insights on the BBPs can eventually lead to a revamped embodied cognitive ontology in numerical cognition. Among other mathematical skills, numerical magnitude processing and its sensorimotor origins are discussed to substantiate the arguments presented. Additionally, an fMRI study design is provided to illustrate the application of the arguments presented in empirical research.
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Affiliation(s)
- Firat Soylu
- Educational Psychology Program, The University of Alabama, Tuscaloosa, AL, United States.
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8
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Lei T, Liao X, Liang X, Sun L, Xia M, Xia Y, Zhao T, Chen X, Men W, Wang Y, Ma L, Liu N, Lu J, Zhao G, Ding Y, Deng Y, Wang J, Chen R, Zhang H, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Functional network modules overlap and are linked to interindividual connectome differences during human brain development. PLoS Biol 2024; 22:e3002653. [PMID: 39292711 DOI: 10.1371/journal.pbio.3002653] [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: 04/10/2024] [Revised: 09/30/2024] [Accepted: 08/29/2024] [Indexed: 09/20/2024] Open
Abstract
The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.
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Affiliation(s)
- Tianyuan Lei
- Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical College, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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9
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Bischoff H, Kovach C, Kumar S, Bruss J, Tranel D, Khalsa SS. Sensing, feeling and regulating: investigating the association of focal brain damage with voluntary respiratory and motor control. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230251. [PMID: 39005040 DOI: 10.1098/rstb.2023.0251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/26/2024] [Indexed: 07/16/2024] Open
Abstract
Breathing is a complex, vital function that can be modulated to influence physical and mental well-being. However, the role of cortical and subcortical brain regions in voluntary control of human respiration is underexplored. Here we investigated the influence of damage to human frontal, temporal or limbic regions on the sensation and regulation of breathing patterns. Participants performed a respiratory regulation task across regular and irregular frequencies ranging from 6 to 60 breaths per minute (bpm), with a counterbalanced hand motor control task. Interoceptive and affective states induced by each condition were assessed via questionnaire, and autonomic signals were indexed via skin conductance. Participants with focal lesions to the bilateral frontal lobe, right insula/basal ganglia and left medial temporal lobe showed reduced performance relative to individually matched healthy comparisons during the breathing and motor tasks. They also reported significantly higher anxiety during the 60 bpm regular and irregular breathing trials, with anxiety correlating with difficulty in rapid breathing specifically within this group. This study demonstrates that damage to frontal, temporal or limbic regions is associated with abnormal voluntary respiratory and motor regulation and tachypnoea-related anxiety, highlighting the role of the forebrain in affective and motor responses during breathing. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.
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Affiliation(s)
- Henrik Bischoff
- Department of Psychology, University of Stockholm, 10691 Stockholm, Sweden
- Department of Psychology, Carl-von-Ossietzky University Oldenburg, 26129 Oldenburg, Germany
| | - Christopher Kovach
- Department of Neurosurgery, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Sukbhinder Kumar
- Department of Neurosurgery, University of Iowa, Iowa City, IA 52242, USA
| | - Joel Bruss
- Departments of Pediatrics, Neurology, and Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Daniel Tranel
- Departments of Neurology and Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK 74119, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
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10
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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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11
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Wang B, Yuan Y, Yang L, Huang Y, Zhang X, Zhang X, Yan W, Li Y, Li D, Xiang J, Yang J, Liu M. Multi-hierarchy Network Configuration Can Predict Brain States and Performance. J Cogn Neurosci 2024; 36:1695-1714. [PMID: 38579269 DOI: 10.1162/jocn_a_02153] [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] [Indexed: 04/07/2024]
Abstract
The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
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Affiliation(s)
- Bin Wang
- Taiyuan University of Technology
| | | | - Lan Yang
- Taiyuan University of Technology
| | | | - Xi Zhang
- Taiyuan University of Technology
| | | | | | - Ying Li
- Taiyuan University of Technology
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12
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Zhang Z, Wei W, Wang S, Li M, Li X, Li X, Wang Q, Yu H, Zhang Y, Guo W, Ma X, Zhao L, Deng W, Sham PC, Sun Y, Li T. Dynamic structure-function coupling across three major psychiatric disorders. Psychol Med 2024; 54:1629-1640. [PMID: 38084608 DOI: 10.1017/s0033291723003525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). METHODS We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time. RESULTS The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity. CONCLUSIONS These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.
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Affiliation(s)
- Zhe Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- School of Physics, Hangzhou Normal University, Hangzhou, China
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Wei Wei
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Sujie Wang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaoyu Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Yamin Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Wanjun Guo
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Sun
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
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13
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Van Overwalle F, Ma Q, Haihambo N, Bylemans T, Catoira B, Firouzi M, Li M, Pu M, Heleven E, Baeken C, Baetens K, Deroost N. A Functional Atlas of the Cerebellum Based on NeuroSynth Task Coordinates. CEREBELLUM (LONDON, ENGLAND) 2024; 23:993-1012. [PMID: 37608227 PMCID: PMC11102394 DOI: 10.1007/s12311-023-01596-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 08/24/2023]
Abstract
Although the human cerebellum has a surface that is about 80% of that of the cerebral cortex and has about four times as many neurons, its functional organization is still very much uncharted. Despite recent attempts to provide resting-state and task-based parcellations of the cerebellum, these two approaches lead to large discrepancies. This article describes a comprehensive task-based functional parcellation of the human cerebellum based on a large-scale functional database, NeuroSynth, involving an unprecedented diversity of tasks, which were reliably associated with ontological key terms referring to psychological functions. Involving over 44,500 participants from this database, we present a parcellation that exhibits replicability with earlier resting-state parcellations across cerebellar and neocortical structures. The functional parcellation of the cerebellum confirms the major networks revealed in prior work, including sensorimotor, directed (dorsal) attention, divided (ventral) attention, executive control, mentalizing (default mode) networks, tiny patches of a limbic network, and also a unilateral language network (but not the visual network), and the association of these networks with underlying ontological key terms confirms their major functionality. The networks are revealed at locations that are roughly similar to prior resting-state cerebellar parcellations, although they are less symmetric and more fragmented across the two hemispheres. This functional parcellation of the human cerebellum and associated key terms can provide a useful guide in designing studies to test specific functional hypotheses and provide a reference for interpreting the results.
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Affiliation(s)
- Frank Van Overwalle
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
| | - Qianying Ma
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Tom Bylemans
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Beatriz Catoira
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Mahyar Firouzi
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Min Pu
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Elien Heleven
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Chris Baeken
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
- Department of Psychiatry, Universitair Ziekenhuis Brussel, Brussels, Belgium
- Department of Psychiatry, Ghent Experimental Psychiatry Lab, Ghent University, Ghent, Belgium
| | - Kris Baetens
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Natacha Deroost
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
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14
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Toba MN, Malkinson TS, Howells H, Mackie MA, Spagna A. Same, Same but Different? A Multi-Method Review of the Processes Underlying Executive Control. Neuropsychol Rev 2024; 34:418-454. [PMID: 36967445 DOI: 10.1007/s11065-023-09577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/26/2022] [Indexed: 03/29/2023]
Abstract
Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Yet, longstanding evidence from lesion studies has demonstrated both overlap and dissociation in their behavioural expression and anatomical underpinnings, suggesting that a lower dimensional framework could be employed to further identify processes supporting goal-directed behaviour. Here, we describe the anatomical and functional correspondence between attention, working memory, and executive control by providing an overview of cognitive models, as well as recent data from lesion studies, invasive and non-invasive multimodal neuroimaging and brain stimulation. We emphasize the benefits of considering converging evidence from multiple methodologies centred on the identification of brain mechanisms supporting goal-driven behaviour. We propose that expanding on this approach should enable the construction of a comprehensive anatomo-functional framework with testable new hypotheses, and aid clinical neuroscience to intervene on impairments of executive functions.
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Affiliation(s)
- Monica N Toba
- Laboratory of Functional Neurosciences (UR UPJV 4559), University Hospital of Amiens and University of Picardie Jules Verne, Amiens, France.
- CHU Amiens Picardie - Site Sud, Centre Universitaire de Recherche en Santé, Avenue René Laënnec, 80054, Amiens Cedex 1, France.
| | - Tal Seidel Malkinson
- Paris Brain Institute, ICM, Hôpital de La Pitié-Salpêtrière, Sorbonne Université, Inserm U 1127, CNRS UMR 7225, 75013, Paris, France
- Université de Lorraine, CRAN, F-54000, Nancy, France
| | - Henrietta Howells
- Laboratory of Motor Control, Department of Medical Biotechnologies and Translational Medicine, Humanitas Research Hospital, IRCCS, Università Degli Studi Di Milano, Milan, Italy
| | - Melissa-Ann Mackie
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alfredo Spagna
- Department of Psychology, Columbia University, New York, NY, 10025, USA.
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15
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Korte JA, Weakley A, Donjuan Fernandez K, Joiner WM, Fan AP. Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging. J Cogn Neurosci 2024; 36:734-755. [PMID: 38285732 DOI: 10.1162/jocn_a_02116] [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] [Indexed: 01/31/2024]
Abstract
The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.
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16
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Watters H, Davis A, Fazili A, Daley L, LaGrow TJ, Schumacher EH, Keilholz S. Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590625. [PMID: 38712098 PMCID: PMC11071428 DOI: 10.1101/2024.04.22.590625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.
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Affiliation(s)
| | - Aleah Davis
- Agnes Scott College
- Georgia Institute of Technology School of Psychology
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - TJ LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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17
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Rasero J, Betzel R, Sentis AI, Kraynak TE, Gianaros PJ, Verstynen T. Similarity in evoked responses does not imply similarity in macroscopic network states. Netw Neurosci 2024; 8:335-354. [PMID: 38711543 PMCID: PMC11073549 DOI: 10.1162/netn_a_00354] [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: 08/22/2023] [Accepted: 11/17/2023] [Indexed: 05/08/2024] Open
Abstract
It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N = 242). Participants performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.
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Affiliation(s)
- Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Amy Isabella Sentis
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
| | - Thomas E. Kraynak
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter J. Gianaros
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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18
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Quah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M. A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.577486. [PMID: 38559071 PMCID: PMC10979851 DOI: 10.1101/2024.01.31.577486] [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: 04/04/2024]
Abstract
Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.
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Affiliation(s)
- S K L Quah
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - B Jo
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - C Geniesse
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - L Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA USA
| | - J A Mumford
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - D M Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St Louis, MO, USA
| | - D A Fair
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
| | - I H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - R A Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - M Saggar
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
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19
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Seitzman BA, Reynoso FJ, Mitchell TJ, Bice AR, Jarang A, Wang X, Mpoy C, Strong L, Rogers BE, Yuede CM, Rubin JB, Perkins SM, Bauer AQ. Functional network disorganization and cognitive decline following fractionated whole-brain radiation in mice. GeroScience 2024; 46:543-562. [PMID: 37749370 PMCID: PMC10828348 DOI: 10.1007/s11357-023-00944-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023] Open
Abstract
Cognitive dysfunction following radiotherapy (RT) is one of the most common complications associated with RT delivered to the brain, but the precise mechanisms behind this dysfunction are not well understood, and to date, there are no preventative measures or effective treatments. To improve patient outcomes, a better understanding of the effects of radiation on the brain's functional systems is required. Functional magnetic resonance imaging (fMRI) has shown promise in this regard, however, compared to neural activity, hemodynamic measures of brain function are slow and indirect. Understanding how RT acutely and chronically affects functional brain organization requires more direct examination of temporally evolving neural dynamics as they relate to cerebral hemodynamics for bridging with human studies. In order to adequately study the underlying mechanisms of RT-induced cognitive dysfunction, the development of clinically mimetic RT protocols in animal models is needed. To address these challenges, we developed a fractionated whole-brain RT protocol (3Gy/day for 10 days) and applied longitudinal wide field optical imaging (WFOI) of neural and hemodynamic brain activity at 1, 2, and 3 months post RT. At each time point, mice were subject to repeated behavioral testing across a variety of sensorimotor and cognitive domains. Disruptions in cortical neuronal and hemodynamic activity observed 1 month post RT were significantly worsened by 3 months. While broad changes were observed in functional brain organization post RT, brain regions most impacted by RT occurred within those overlapping with the mouse default mode network and other association areas similar to prior reports in human subjects. Further, significant cognitive deficits were observed following tests of novel object investigation and responses to auditory and contextual cues after fear conditioning. Our results fill a much-needed gap in understanding the effects of whole-brain RT on systems level brain organization and how RT affects neuronal versus hemodynamic signaling in the cortex. Having established a clinically-relevant injury model, future studies can examine therapeutic interventions designed to reduce neuroinflammation-based injury following RT. Given the overlap of sequelae that occur following RT with and without chemotherapy, these tools can also be easily incorporated to examine chemotherapy-related cognitive impairment.
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Affiliation(s)
- Benjamin A Seitzman
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Francisco J Reynoso
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Timothy J Mitchell
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Annie R Bice
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. Louis, 660 S. Euclid Ave, Campus Box 8225, St. Louis, MO, 63110, USA
| | - Anmol Jarang
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. Louis, 660 S. Euclid Ave, Campus Box 8225, St. Louis, MO, 63110, USA
| | - Xiaodan Wang
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. Louis, 660 S. Euclid Ave, Campus Box 8225, St. Louis, MO, 63110, USA
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Cedric Mpoy
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Lori Strong
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Buck E Rogers
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA
| | - Carla M Yuede
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Joshua B Rubin
- Department of Pediatrics, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Stephanie M Perkins
- Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA.
| | - Adam Q Bauer
- Mallinckrodt Institute of Radiology, School of Medicine, Washington University in St. Louis, 660 S. Euclid Ave, Campus Box 8225, St. Louis, MO, 63110, USA.
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO, USA.
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Ibrahim K, Iturmendi-Sabater I, Vasishth M, Barron DS, Guardavaccaro M, Funaro MC, Holmes A, McCarthy G, Eickhoff SB, Sukhodolsky DG. Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis. Schizophr Res 2024; 264:298-313. [PMID: 38215566 PMCID: PMC10922721 DOI: 10.1016/j.schres.2023.12.003] [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: 02/22/2023] [Revised: 09/07/2023] [Accepted: 12/05/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia. METHODS Activation likelihood estimation (ALE) meta-analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta-analytic connectivity modeling. Functional decoding was also conducted. RESULTS Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta-analytic connectivity to frontoparietal and frontotemporal circuitry. CONCLUSION Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.
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Affiliation(s)
- Karim Ibrahim
- Yale University School of Medicine, Child Study Center, United States of America.
| | | | - Maya Vasishth
- Yale University School of Medicine, Child Study Center, United States of America
| | - Daniel S Barron
- Brigham and Women's Hospital, Department of Psychiatry, Anesthesiology and Pain Medicine, United States of America; Harvard Medical School, Department of Psychiatry, United States of America
| | | | - Melissa C Funaro
- Yale University, Harvey Cushing/John Hay Whitney Medical Library, United States of America
| | - Avram Holmes
- Yale University, Department of Psychology, United States of America; Yale University, Department of Psychiatry, United States of America; Yale University, Wu Tsai Institute, United States of America
| | - Gregory McCarthy
- Yale University, Department of Psychology, United States of America; Yale University, Wu Tsai Institute, United States of America
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Denis G Sukhodolsky
- Yale University School of Medicine, Child Study Center, United States of America
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21
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Assem M, Shashidhara S, Glasser MF, Duncan J. Basis of executive functions in fine-grained architecture of cortical and subcortical human brain networks. Cereb Cortex 2024; 34:bhad537. [PMID: 38244562 PMCID: PMC10839840 DOI: 10.1093/cercor/bhad537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024] Open
Abstract
Theoretical models suggest that executive functions rely on both domain-general and domain-specific processes. Supporting this view, prior brain imaging studies have revealed that executive activations converge and diverge within broadly characterized brain networks. However, the lack of precise anatomical mappings has impeded our understanding of the interplay between domain-general and domain-specific processes. To address this challenge, we used the high-resolution multimodal magnetic resonance imaging approach of the Human Connectome Project to scan participants performing 3 canonical executive tasks: n-back, rule switching, and stop signal. The results reveal that, at the individual level, different executive activations converge within 9 domain-general territories distributed in frontal, parietal, and temporal cortices. Each task exhibits a unique topography characterized by finely detailed activation gradients within domain-general territory shifted toward adjacent resting-state networks; n-back activations shift toward the default mode, rule switching toward dorsal attention, and stop signal toward cingulo-opercular networks. Importantly, the strongest activations arise at multimodal neurobiological definitions of network borders. Matching results are seen in circumscribed regions of the caudate nucleus, thalamus, and cerebellum. The shifting peaks of local gradients at the intersection of task-specific networks provide a novel mechanistic insight into how partially-specialized networks interact with neighboring domain-general territories to generate distinct executive functions.
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Affiliation(s)
- Moataz Assem
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
| | - Sneha Shashidhara
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
- Psychology Department, Ashoka University, Sonipat, 131029, India
| | - Matthew F Glasser
- Department of Radiology, Washington University in St. Louis, Saint Louis, MO, 63110, United States
- Department of Neuroscience, Washington University in St. Louis, Saint Louis, MO, 63110, United States
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, United Kingdom
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22
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Boen R, Kaufmann T, van der Meer D, Frei O, Agartz I, Ames D, Andersson M, Armstrong NJ, Artiges E, Atkins JR, Bauer J, Benedetti F, Boomsma DI, Brodaty H, Brosch K, Buckner RL, Cairns MJ, Calhoun V, Caspers S, Cichon S, Corvin AP, Crespo-Facorro B, Dannlowski U, David FS, de Geus EJC, de Zubicaray GI, Desrivières S, Doherty JL, Donohoe G, Ehrlich S, Eising E, Espeseth T, Fisher SE, Forstner AJ, Fortaner-Uyà L, Frouin V, Fukunaga M, Ge T, Glahn DC, Goltermann J, Grabe HJ, Green MJ, Groenewold NA, Grotegerd D, Grøntvedt GR, Hahn T, Hashimoto R, Hehir-Kwa JY, Henskens FA, Holmes AJ, Håberg AK, Haavik J, Jacquemont S, Jansen A, Jockwitz C, Jönsson EG, Kikuchi M, Kircher T, Kumar K, Le Hellard S, Leu C, Linden DE, Liu J, Loughnan R, Mather KA, McMahon KL, McRae AF, Medland SE, Meinert S, Moreau CA, Morris DW, Mowry BJ, Mühleisen TW, Nenadić I, Nöthen MM, Nyberg L, Ophoff RA, Owen MJ, Pantelis C, Paolini M, Paus T, Pausova Z, Persson K, Quidé Y, Marques TR, Sachdev PS, Sando SB, Schall U, Scott RJ, Selbæk G, Shumskaya E, Silva AI, Sisodiya SM, Stein F, Stein DJ, Straube B, Streit F, Strike LT, Teumer A, Teutenberg L, Thalamuthu A, Tooney PA, Tordesillas-Gutierrez D, Trollor JN, van 't Ent D, van den Bree MBM, van Haren NEM, Vázquez-Bourgon J, Völzke H, Wen W, Wittfeld K, Ching CRK, Westlye LT, Thompson PM, Bearden CE, Selmer KK, Alnæs D, Andreassen OA, Sønderby IE. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers. Biol Psychiatry 2024; 95:147-160. [PMID: 37661008 PMCID: PMC7615370 DOI: 10.1016/j.biopsych.2023.08.018] [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: 04/11/2023] [Revised: 07/25/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
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Affiliation(s)
- Rune Boen
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Germany; German Center for Mental Health (DZPG), partner site Tübingen, Tübingen, Germany
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Clinical Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm, Sweden
| | - David Ames
- University of Melbourne Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, Victoria, Australia; National Ageing Research Institute, Parkville, Victoria, Australia
| | - Micael Andersson
- Department of Integrative Medical Biology and Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale U1299, École Normale Supérieure Paris-Saclay, Université Paris Saclay, Gif-sur-Yvette, France; Établissement public de santé (EPS) Barthélemy Durand, Etampes, France
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, College of Medicine, Health and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia; Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jochen Bauer
- University Clinic for Radiology, University of Münster, Münster, Germany
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy; Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele Scientific Institute, Milan, Italy
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Randy L Buckner
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, College of Medicine, Health and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University/Georgia Institute of Technology/Emory University, Atlanta, Georgia
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Biomedicine, University of Basel, Basel, Switzerland; University Hospital Basel, Institute of Medical Genetics and Pathology, Basel, Switzerland
| | - Aiden P Corvin
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Benedicto Crespo-Facorro
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Centro superior de investigaciones científicas (CSIC), Sevilla, Spain; Centro de Investigación Biomédica en Red Salud Mental, Sevilla, Spain; Department of Psychiatry, University of Sevilla, Sevilla, Spain
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Greig I de Zubicaray
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Joanne L Doherty
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Gary Donohoe
- School of Psychology and Center for Neuroimaging, Cognition and Genomics, University of Galway, Galway, Ireland
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Else Eising
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Thomas Espeseth
- Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychology, Oslo New University College, Oslo, Norway
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Andreas J Forstner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Lidia Fortaner-Uyà
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy; Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele Scientific Institute, Milan, Italy
| | - Vincent Frouin
- Neurospin, Commissariat a l'Energie Atomique (CEA), Université Paris-Saclay, Gif-sur-Yvette, France
| | - Masaki Fukunaga
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Japan
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - David C Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Melissa J Green
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia; Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Gøril Rolfseng Grøntvedt
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Jayne Y Hehir-Kwa
- Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia; Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, New South Wales, Australia
| | - Avram J Holmes
- Department of Psychiatry, Rutgers University, New Brunswick, New Jersey; Brain Health Institute, Rutgers University, Piscataway, New Jersey
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Radiology and Nuclear Medicine, St. Olav's Hospital, Trondheim, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, Norway; Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Sebastien Jacquemont
- Sainte Justine Hospital Research Center, Montreal, Quebec, Canada; Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany; Core-Facility Brainimaging and Department of Psychiatry, Faculty of Medicine, Philipps-University Marburg, Marburg, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Erik G Jönsson
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Masataka Kikuchi
- Department of Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Kuldeep Kumar
- Sainte Justine Hospital Research Center, Montreal, Quebec, Canada
| | - Stephanie Le Hellard
- Norwegian Centre for Mental Disorders Research, Department of Clinical Science, University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Costin Leu
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Neurology, McGovern Medical School, UTHealth Houston, Houston, Texas
| | - David E Linden
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, United Kingdom; School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jingyu Liu
- Department of Computer Science and Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, Georgia
| | - Robert Loughnan
- Department of Cognitive Science and Population Neuroscience and Genetics Lab, University of California San Diego, La Jolla, California
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Katie L McMahon
- School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Sarah E Medland
- Psychiatric Genetics, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Brisbane, Queensland, Australia; University of Queensland, Brisbane, Queensland, Australia; Queensland University of Technology, Brisbane, Queensland, Australia
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Clara A Moreau
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Derek W Morris
- Centre for Neuroimaging, Cognition and Genomics, School of Biological and Chemical Sciences, University of Galway, Galway, Ireland
| | - Bryan J Mowry
- Queensland Brain Institute and Queensland Centre for Mental Health Research, University of Queensland, Brisbane, Queensland, Australia
| | - Thomas W Mühleisen
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Lars Nyberg
- Departments of Radiation Sciences, Integrative Medical Biology and Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Roel A Ophoff
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands; Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California Los Angeles, Los Angeles, California
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Carlton South, Victoria, Australia; Western Centre for Health Research and Education, Sunshine Hospital, St Albans, Victoria, Australia
| | - Marco Paolini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy; Division of Neuroscience, Psychiatry and Clinical Psychobiology Unit, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele Scientific Institute, Milan, Italy
| | - Tomas Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Sainte Justine Hospital Research Center, University of Montreal, Montreal, Quebec, Canada; Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Zdenka Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Karin Persson
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway; Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Yann Quidé
- Neuroscience Research Australia, Sydney, New South Wales, Australia; School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Tiago Reis Marques
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Sigrid B Sando
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, College of Medicine, Health and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, New South Wales, Australia; Division of Molecular Medicine, New South Wales Health Pathology, Newcastle, New South Wales, Australia
| | - Geir Selbæk
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway; Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Elena Shumskaya
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ana I Silva
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, United Kingdom
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lachlan T Strike
- Psychiatric Genetics, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Brisbane, Queensland, Australia; School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Alexander Teumer
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research, Greifswald, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Paul A Tooney
- School of Biomedical Sciences and Pharmacy, College of Medicine, Health and Wellbeing, University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Diana Tordesillas-Gutierrez
- Instituto de Física de Cantabria UC-CSIC, Santander, Spain; Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute, Instituto de Investigación Sanitaria Valdecilla, Santander, Spain
| | - Julian N Trollor
- Department of Developmental Disability Neuropsychiatry and Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Dennis van 't Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marianne B M van den Bree
- Institute of Psychological Medicine and Clinical Neurosciences and Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red Salud Mental, Sevilla, Spain; Department of Psychiatry, University Hospital Maqués de Valdecilla, Instituto de Investigación Sanitaria Valdecilla, Santander, Spain; Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Santander, Spain
| | - Henry Völzke
- German Centre for Cardiovascular Research, Greifswald, Germany; Greifswald University Hospital, Greifswald, Germany
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California Los Angeles, Los Angeles, California
| | - Kaja K Selmer
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital and the University of Oslo, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Kristiania University College, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ida E Sønderby
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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23
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Falck RS, Hsu CL, Best JR, Boa Sorte Silva NC, Hall PA, Li LC, Liu-Ambrose T. Cross-sectional and longitudinal neural predictors of physical activity and sedentary behaviour from a 6-month randomized controlled trial. Sci Rep 2024; 14:919. [PMID: 38195673 PMCID: PMC10776740 DOI: 10.1038/s41598-023-48715-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: 04/18/2023] [Accepted: 11/29/2023] [Indexed: 01/11/2024] Open
Abstract
A sedentary lifestyle offers immediate gratification, but at the expense of long-term health. It is thus critical to understand how the brain evaluates immediate rewards and long-term health effects in the context of deciding whether to engage in moderate-to-vigorous physical activity (MVPA) or sedentary behaviour (SB). In this secondary analysis of a 6-month randomized controlled trial to increase MVPA and reduce SB among community-dwelling adults, we explored how neural activity during an executive control task was associated with MVPA and SB levels. At baseline, a subset of participants (n = 26/61) underwent task-based functional magnetic resonance imaging (fMRI) to examine neural activity underlying executive control using the Now/Later task. MVPA and SB were measured objectively using the Sensewear Mini at baseline, and 2, 4, and 6 months follow-up. We then examined the associations of baseline neural activation underlying executive control with: (1) baseline MVPA or SB; and (2) changes in MVPA and SB over 6 months. Our results determined that there is a complex neurocognitive system associated with MVPA levels, while SB appears to lack any neurocognitive control. In other words, MVPA appears to require neurocognitive effort, while SB may be the default behavioural pattern in adults.
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Affiliation(s)
- Ryan Stanley Falck
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Chun Liang Hsu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - John R Best
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Narlon Cassio Boa Sorte Silva
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Peter A Hall
- School of Kinesiology, The University of Waterloo, Waterloo, ON, Canada
| | - Linda C Li
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Teresa Liu-Ambrose
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada.
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada.
- Aging, Mobility, and Cognitive Neuroscience Lab, Department of Physical Therapy, Vancouver Coastal Health Research Institute, Faculty of Medicine, University of British Columbia, 212-177 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
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24
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Luppi AI, Girn M, Rosas FE, Timmermann C, Roseman L, Erritzoe D, Nutt DJ, Stamatakis EA, Spreng RN, Xing L, Huttner WB, Carhart-Harris RL. A role for the serotonin 2A receptor in the expansion and functioning of human transmodal cortex. Brain 2024; 147:56-80. [PMID: 37703310 DOI: 10.1093/brain/awad311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Integrating independent but converging lines of research on brain function and neurodevelopment across scales, this article proposes that serotonin 2A receptor (5-HT2AR) signalling is an evolutionary and developmental driver and potent modulator of the macroscale functional organization of the human cerebral cortex. A wealth of evidence indicates that the anatomical and functional organization of the cortex follows a unimodal-to-transmodal gradient. Situated at the apex of this processing hierarchy-where it plays a central role in the integrative processes underpinning complex, human-defining cognition-the transmodal cortex has disproportionately expanded across human development and evolution. Notably, the adult human transmodal cortex is especially rich in 5-HT2AR expression and recent evidence suggests that, during early brain development, 5-HT2AR signalling on neural progenitor cells stimulates their proliferation-a critical process for evolutionarily-relevant cortical expansion. Drawing on multimodal neuroimaging and cross-species investigations, we argue that, by contributing to the expansion of the human cortex and being prevalent at the apex of its hierarchy in the adult brain, 5-HT2AR signalling plays a major role in both human cortical expansion and functioning. Owing to its unique excitatory and downstream cellular effects, neuronal 5-HT2AR agonism promotes neuroplasticity, learning and cognitive and psychological flexibility in a context-(hyper)sensitive manner with therapeutic potential. Overall, we delineate a dual role of 5-HT2ARs in enabling both the expansion and modulation of the human transmodal cortex.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London, SW7 2AZ, UK
| | - Christopher Timmermann
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David Erritzoe
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, H3A 2B4, Canada
| | - Lei Xing
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Wieland B Huttner
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, 01307, Germany
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California SanFrancisco, San Francisco, CA 94158, USA
- Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK
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25
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Vafaii H, Mandino F, Desrosiers-Grégoire G, O'Connor D, Markicevic M, Shen X, Ge X, Herman P, Hyder F, Papademetris X, Chakravarty M, Crair MC, Constable RT, Lake EMR, Pessoa L. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Nat Commun 2024; 15:229. [PMID: 38172111 PMCID: PMC10764905 DOI: 10.1038/s41467-023-44363-z] [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: 04/16/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.
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Affiliation(s)
- Hadi Vafaii
- Department of Physics, University of Maryland, College Park, MD, 20742, USA.
| | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Gabriel Desrosiers-Grégoire
- Computional Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Marija Markicevic
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xinxin Ge
- Department of Physiology, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Section of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Mallar Chakravarty
- Computional Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Psychiatry, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, H3A 0G4, Canada
| | - Michael C Crair
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA.
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA.
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, 20742, USA.
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26
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Naef N, Hottinger SJ, Schlosser L, Greutmann M, Latal B, O'Gorman RT. Association of cerebellar volume with cognitive and motor function in adults with congenital heart disease. Neurol Sci 2023; 44:3979-3987. [PMID: 37351678 PMCID: PMC10570150 DOI: 10.1007/s10072-023-06861-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 05/15/2023] [Indexed: 06/24/2023]
Abstract
INTRODUCTION Patients with congenital heart disease (CHD) are at risk for cognitive and motor function impairments, brain injury, and smaller total brain volumes. The specific vulnerability of the cerebellum and its role in cognitive and motor functions in adults with congenital heart disease is not well defined. METHODS Forty-three patients with CHD and 53 controls between 18 and 32 years underwent brain magnetic resonance imaging and cognitive, executive (EF), and motor function assessment. Cerebellar volumes were obtained using EasyMeasure and SUIT Toolbox. Associations between cerebellar volumes and cognitive and motor function were calculated using linear models. RESULTS General cognitive and pure motor functions were lower in patients compared to controls (P < 0.05). Executive functions were within the normal range. While total cerebellar volumes and the anterior lobes were similar in patients and controls (P > 0.1), the posterior cerebellar lobe was smaller in patients with more complex CHD (P = 0.006). Smaller posterior cerebellar gray matter was not associated with cognitive functions. Smaller anterior cerebellar gray matter was not significantly related to motor functions (P > 0.1). CONCLUSION In adults with CHD, cerebellar volume was largely unimpaired. Patients with more complex CHD may be vulnerable to changes in the posterior cerebellar gray matter. We found no significant contribution of cerebellar gray matter to cognitive and motor impairments. More advanced imaging techniques are necessary to clarify the contribution of the cerebellum to cognitive and motor functions.
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Affiliation(s)
- Nadja Naef
- Child Development Center, University Children's Hospital Zurich, Zurich, CH, Switzerland.
| | - Selma J Hottinger
- Child Development Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
| | - Ladina Schlosser
- Child Development Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Matthias Greutmann
- Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland
| | - Beatrice Latal
- Child Development Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
| | - Ruth Tuura O'Gorman
- Children's Research Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
- MR Research Center, University Children's Hospital Zurich, Zurich, CH, Switzerland
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27
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Li J, Huang M, Cao Y, Qin Z, Lang J. Long-term Intensive Soccer Training Induced Dynamic Reconfiguration of Brain Network. Neuroscience 2023; 530:133-143. [PMID: 37640136 DOI: 10.1016/j.neuroscience.2023.08.020] [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: 04/26/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/31/2023]
Abstract
Long-term motor skill learning has been shown to impact the functional plasticity of the brain. Athletes, as a unique population, exhibit remarkable adaptive changes in the static properties of their brain networks. However, studying the differences between expert and novice athletes using a dynamic brain network framework can provide a fresh perspective on how motor skill learning affects the functional organization of the brain. In this study, we investigated the dynamic properties of brain networks in expert and novice soccer players at the whole-brain, network, and region-based levels. Our findings revealed that expert soccer players displayed reduced integration and increased segregation at the whole-brain level. As for network level, experts exhibited increased segregation and reduced flexibility in the visual network, enhanced integration between the visual and ventral attention networks, and decreased integration in the subcortical-sensorimotor and subcortical-cerebellar networks. Additionally, specific brain regions within the visual network exhibited greater recruitment in expert soccer players compared to novices at the nodal level. Furthermore, classification analyses demonstrated the critical role played by the visual network in the classification process. In conclusion, our study provides new insights into the dynamic properties of brain networks in expert and novice soccer players, and suggests that reduced integration and increased segregation in the visual network may be neuroimaging marker that distinguish expert soccer players from novices. Our findings may have implications for the training and development of athletes and advance our understanding of how motor skill learning affects brain functional organization.
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Affiliation(s)
- Ju Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Minghao Huang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Yaping Cao
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Zhe Qin
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China.
| | - Jian Lang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
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28
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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29
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [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: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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30
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Fotiadis P, Cieslak M, He X, Caciagli L, Ouellet M, Satterthwaite TD, Shinohara RT, Bassett DS. Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex. Nat Commun 2023; 14:6115. [PMID: 37777569 PMCID: PMC10542365 DOI: 10.1038/s41467-023-41686-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/08/2023] [Indexed: 10/02/2023] Open
Abstract
Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mathieu Ouellet
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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31
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Molloy MF, Yu EJ, Mattson WI, Hoskinson KR, Taylor HG, Osher DE, Nelson EE, Saygin ZM. Effect of Extremely Preterm Birth on Adolescent Brain Network Organization. Brain Connect 2023; 13:394-409. [PMID: 37312515 DOI: 10.1089/brain.2022.0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023] Open
Abstract
Introduction: Extremely preterm (EPT) birth, defined as birth at a gestational age (GA) <28 weeks, can have a lasting impact on cognition throughout the life span. Previous investigations reveal differences in brain structure and connectivity between infants born preterm and full-term (FT), but how does preterm birth impact the adolescent connectome? Methods: In this study, we investigate how EPT birth can alter broadscale network organization later in life by comparing resting-state functional magnetic resonance imaging connectome-based parcellations of the entire cortex in adolescents born EPT (N = 22) to age-matched adolescents born FT (GA ≥37 weeks, N = 28). We compare these parcellations to adult parcellations from previous studies and explore the relationship between an individual's network organization and behavior. Results: Primary (occipital and sensorimotor) and frontoparietal networks were observed in both groups. However, there existed notable differences in the limbic and insular networks. Surprisingly, the connectivity profile of the limbic network of EPT adolescents was more adultlike than the same network in FT adolescents. Finally, we found a relationship between adolescents' overall cognition score and their limbic network maturity. Discussion: Overall, preterm birth may contribute to the atypical development of broadscale network organization in adolescence and may partially explain the observed cognitive deficits.
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Affiliation(s)
- M Fiona Molloy
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Emily J Yu
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Whitney I Mattson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - H Gerry Taylor
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio, USA
| | - David E Osher
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Eric E Nelson
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Zeynep M Saygin
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
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32
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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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33
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Zhang J, Chen D, Srirangarajan T, Theriault J, Kragel PA, Hartley L, Lee KM, McVeigh K, Wager TD, Wald LL, Satpute AB, Quigley KS, Whitfield-Gabrieli S, Barrett LF, Bianciardi M. Cortical and subcortical mapping of the allostatic-interoceptive system in the human brain: replication and extension with 7 Tesla fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.548178. [PMID: 37546889 PMCID: PMC10401932 DOI: 10.1101/2023.07.20.548178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The brain continuously anticipates the energetic needs of the body and prepares to meet those needs before they arise, a process called allostasis. In support of allostasis, the brain continually models the internal state of the body, a process called interoception. Using published tract-tracing studies in non-human animals as a guide, we previously identified a large-scale system supporting allostasis and interoception in the human brain with functional magnetic resonance imaging (fMRI) at 3 Tesla. In the present study, we replicated and extended this system in humans using 7 Tesla fMRI (N = 91), improving the precision of subgenual and pregenual anterior cingulate topography as well as brainstem nuclei mapping. We verified over 90% of the anatomical connections in the hypothesized allostatic-interoceptive system observed in non-human animal research. We also identified functional connectivity hubs verified in tract-tracing studies but not previously detected using 3 Tesla fMRI. Finally, we demonstrated that individuals with stronger fMRI connectivity between system hubs self-reported greater interoceptive awareness, building on construct validity evidence from our earlier paper. Taken together, these results strengthen evidence for the existence of a whole-brain system supporting interoception in the service of allostasis and we consider the implications for mental and physical health.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA 02115
| | - Danlei Chen
- Department of Psychology, Northeastern University, Boston, MA 02115
| | | | - Jordan Theriault
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02139
| | | | - Ludger Hartley
- Department of Psychology, Northeastern University, Boston, MA 02115
| | - Kent M. Lee
- Department of Psychology, Northeastern University, Boston, MA 02115
| | - Kieran McVeigh
- Department of Psychology, Northeastern University, Boston, MA 02115
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755
| | - Lawrence L. Wald
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02139
| | - Ajay B. Satpute
- Department of Psychology, Northeastern University, Boston, MA 02115
| | - Karen S. Quigley
- Department of Psychology, Northeastern University, Boston, MA 02115
| | | | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA 02115
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02139
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02139
| | - Marta Bianciardi
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02139
- Division of Sleep Medicine, Harvard University, Boston, MA
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34
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Yan X, Kong R, Xue A, Yang Q, Orban C, An L, Holmes AJ, Qian X, Chen J, Zuo XN, Zhou JH, Fortier MV, Tan AP, Gluckman P, Chong YS, Meaney MJ, Bzdok D, Eickhoff SB, Yeo BTT. Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. Neuroimage 2023; 273:120010. [PMID: 36918136 PMCID: PMC10212507 DOI: 10.1016/j.neuroimage.2023.120010] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/25/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023] Open
Abstract
Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).
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Affiliation(s)
- Xiaoxuan Yan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - 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; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Aihuiping Xue
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Qing Yang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, Unites States of America
| | - Xing Qian
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; National Basic Public Science Data Center, China
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore; Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Peter Gluckman
- UK Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, Unites States of America.
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35
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Willbrand EH, Bunge SA, Weiner KS. Neuroanatomical and functional dissociations between variably present anterior lateral prefrontal sulci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.25.542301. [PMID: 37292839 PMCID: PMC10245924 DOI: 10.1101/2023.05.25.542301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid-specific. While recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from 72 young adult humans aged 22-36 and show that dorsal and ventral components of the paraintermediate frontal sulcus (pimfs) present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in anatomy and function in LPFC, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and highlight the importance of considering individual anatomy when investigating structural and functional features of the cortex.
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Affiliation(s)
- Ethan H. Willbrand
- Department of Psychology, University of California Berkeley, Berkeley, CA, 94720 USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720 USA
| | - Silvia A. Bunge
- Department of Psychology, University of California Berkeley, Berkeley, CA, 94720 USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720 USA
| | - Kevin S. Weiner
- Department of Psychology, University of California Berkeley, Berkeley, CA, 94720 USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720 USA
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36
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Zhang Y, Ji W, Jiang F, Wu F, Li G, Hu Y, Zhang W, Wang J, Fan X, Wei X, Manza P, Tomasi D, Volkow ND, Gao X, Wang GJ, Zhang Y. Associations among body mass index, working memory performance, gray matter volume, and brain activation in healthy children. Cereb Cortex 2023; 33:6335-6344. [PMID: 36573454 PMCID: PMC10422922 DOI: 10.1093/cercor/bhac507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022] Open
Abstract
To investigate the neural mechanisms underlying the association between poorer working memory performance and higher body mass index (BMI) in children. We employed structural-(sMRI) and functional magnetic resonance imaging (fMRI) with a 2-back working memory task to examine brain abnormalities and their associations with BMI and working memory performance in 232 children with overweight/obesity (OW/OB) and 244 normal weight children (NW) from the Adolescent Brain Cognitive Development dataset. OW/OB had lower working memory accuracy, which was associated with higher BMI. They showed smaller gray matter (GM) volumes in the left superior frontal gyrus (SFG_L), dorsal anterior cingulate cortex, medial orbital frontal cortex, and medial superior frontal gyrus, which were associated with lower working memory accuracy. During the working memory task, OW/OB relative to NW showed weaker activation in the left superior temporal pole, amygdala, insula, and bilateral caudate. In addition, caudate activation mediated the relationship between higher BMI and lower working memory accuracy. Higher BMI is associated with smaller GM volumes and weaker brain activation in regions involved with working memory. Task-related caudate dysfunction may account for lower working memory accuracy in children with higher BMI.
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Affiliation(s)
- Yaqi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Weibin Ji
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Fukun Jiang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Feifei Wu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Guanya Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Yang Hu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Wenchao Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Jia Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Xiao Fan
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Xiaorong Wei
- Kindergarten affiliated to Air Force Medical University, No. 127, Changle West Road, Xi'an, Shaanxi 710032, China
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Dardo Tomasi
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Nora D Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Xinbo Gao
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Chongqing 400065, China
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, No. 2, Chongwen Road, Chongqing 400064, China
| | - Gene-Jack Wang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Yi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
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Vafaii H, Mandino F, Desrosiers-Grégoire G, O’Connor D, Shen X, Ge X, Herman P, Hyder F, Papademetris X, Chakravarty M, Crair MC, Constable RT, Lake EMR, Pessoa L. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. RESEARCH SQUARE 2023:rs.3.rs-2823802. [PMID: 37162818 PMCID: PMC10168440 DOI: 10.21203/rs.3.rs-2823802/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employed wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determined cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks are overlapping rather than disjoint. Our results show that multiple BOLD networks are detected via Ca2+ signals; there is considerable network overlap (both modalities); networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks; and, despite similarities, important differences are detected across modalities (e.g., brain region "network diversity"). In conclusion, Ca2+ imaging uncovered overlapping functional cortical organization in the mouse that reflected several, but not all, properties observed with fMRI-BOLD signals.
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Affiliation(s)
- Hadi Vafaii
- Department of Physics, University of Maryland, College Park, MD, 20742, USA
| | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Gabriel Desrosiers-Grégoire
- Comp. Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health Univ. Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xinxin Ge
- Department of Physiology, School of Medicine, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Peter Herman
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Mallar Chakravarty
- Comp. Brain Anatomy Laboratory, Cerebral Imaging Center, Douglas Mental Health Univ. Institute, Montreal, QC, H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Psychiatry, McGill University, Montreal, QC, H3A 0G4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, H3A 0G4, Canada
| | - Michael C. Crair
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Evelyn MR. Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, 20742, USA
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA
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38
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King M, Shahshahani L, Ivry RB, Diedrichsen J. A task-general connectivity model reveals variation in convergence of cortical inputs to functional regions of the cerebellum. eLife 2023; 12:e81511. [PMID: 37083692 PMCID: PMC10129326 DOI: 10.7554/elife.81511] [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: 06/30/2022] [Accepted: 03/31/2023] [Indexed: 04/22/2023] Open
Abstract
While resting-state fMRI studies have provided a broad picture of the connectivity between human neocortex and cerebellum, the degree of convergence of cortical inputs onto cerebellar circuits remains unknown. Does each cerebellar region receive input from a single cortical area or convergent inputs from multiple cortical areas? Here, we use task-based fMRI data to build a range of cortico-cerebellar connectivity models, each allowing for a different degree of convergence. We compared these models by their ability to predict cerebellar activity patterns for novel Task Sets. Models that allow some degree of convergence provided the best predictions, arguing for convergence of multiple cortical inputs onto single cerebellar voxels. Importantly, the degree of convergence varied across the cerebellum with the highest convergence observed in areas linked to language, working memory, and social cognition. These findings suggest important differences in the way that functional subdivisions of the cerebellum support motor and cognitive function.
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Affiliation(s)
- Maedbh King
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
| | | | - Richard B Ivry
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
| | - Jörn Diedrichsen
- Western Institute for Neuroscience, Western UniversityLondonCanada
- Department of Statistical and Actuarial Sciences, Western UniversityLondonCanada
- Department of Computer Science, Western University, LondonOntarioCanada
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Keller AS, Sydnor VJ, Pines A, Fair DA, Bassett DS, Satterthwaite TD. Hierarchical functional system development supports executive function. Trends Cogn Sci 2023; 27:160-174. [PMID: 36437189 PMCID: PMC9851999 DOI: 10.1016/j.tics.2022.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/01/2022] [Accepted: 11/03/2022] [Indexed: 11/26/2022]
Abstract
In this perspective, we describe how developmental improvements in youth executive function (EF) are supported by hierarchically organized maturational changes in functional brain systems. We first highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis of cortical organization. We then review data showing that functional system developmental profiles vary along this axis: systems near the associative end become more functionally segregated, while those in the middle become more integrative. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication. We propose a central role for attention and frontoparietal control systems in the maturation of healthy EF and suggest that reduced functional system differentiation across the sensorimotor-association axis contributes to transdiagnostic EF deficits.
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Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Dani S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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40
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Ito T, Murray JD. Multitask representations in the human cortex transform along a sensory-to-motor hierarchy. Nat Neurosci 2023; 26:306-315. [PMID: 36536240 DOI: 10.1038/s41593-022-01224-0] [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: 12/02/2021] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
Human cognition recruits distributed neural processes, yet the organizing computational and functional architectures remain unclear. Here, we characterized the geometry and topography of multitask representations across the human cortex using functional magnetic resonance imaging during 26 cognitive tasks in the same individuals. We measured the representational similarity across tasks within a region and the alignment of representations between regions. Representational alignment varied in a graded manner along the sensory-association-motor axis. Multitask dimensionality exhibited compression then expansion along this gradient. To investigate computational principles of multitask representations, we trained multilayer neural network models to transform empirical visual-to-motor representations. Compression-then-expansion organization in models emerged exclusively in a rich training regime, which is associated with learning optimized representations that are robust to noise. This regime produces hierarchically structured representations similar to empirical cortical patterns. Together, these results reveal computational principles that organize multitask representations across the human cortex to support multitask cognition.
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Affiliation(s)
- Takuya Ito
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - John D Murray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
- Department of Physics, Yale University, New Haven, CT, USA.
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41
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Mapping the effects of pregnancy on resting state brain activity, white matter microstructure, neural metabolite concentrations and grey matter architecture. Nat Commun 2022; 13:6931. [PMID: 36414622 PMCID: PMC9681770 DOI: 10.1038/s41467-022-33884-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/06/2022] [Indexed: 11/23/2022] Open
Abstract
While animal studies have demonstrated a unique reproduction-related neuroplasticity, little is known on the effects of pregnancy on the human brain. Here we investigated whether pregnancy is associated with changes to resting state brain activity, white matter microstructure, neural metabolite concentrations and grey matter architecture using a comprehensive pre-conception cohort study. We show that pregnancy leads to selective and robust changes in neural architecture and neural network organization, which are most pronounced in the Default Mode Network. These neural changes correlated with pregnancy hormones, primarily third-trimester estradiol, while no associations were found with other factors such as osmotic effects, stress and sleep. Furthermore, the changes related to measures of maternal-fetal bonding, nesting behavior and the physiological responsiveness to infant cues, and predicted measures of mother-infant bonding and bonding impairments. These findings suggest there are selective pregnancy-related modifications in brain structure and function that may facilitate peripartum maternal processes of key relevance to the mother-infant dyad.
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Abdallah M, Iovene V, Zanitti G, Wassermann D. Meta-analysis of the functional neuroimaging literature with probabilistic logic programming. Sci Rep 2022; 12:19431. [PMID: 36371447 PMCID: PMC9653422 DOI: 10.1038/s41598-022-21801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis by designing NeuroLang: a domain-specific language to express and test hypotheses using probabilistic first-order logic programming. By leveraging formalisms found at the crossroads of artificial intelligence and knowledge representation, NeuroLang provides the expressivity to address a larger repertoire of hypotheses in a meta-analysis, while seamlessly modeling the uncertainty inherent to neuroimaging data. We demonstrate the language's capabilities in conducting comprehensive neuroimaging meta-analysis through use-case examples that address questions of structure-function associations. Specifically, we infer the specific functional roles of three canonical brain networks, support the role of the visual word-form area in visuospatial attention, and investigate the heterogeneous organization of the frontoparietal control network.
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Affiliation(s)
- Majd Abdallah
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Valentin Iovene
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Gaston Zanitti
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France
| | - Demian Wassermann
- Inria, CEA, Neurospin, MIND Team, Université Paris Saclay, 91120, Palaiseau, France.
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43
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Schultz DH, Ito T, Cole MW. Global connectivity fingerprints predict the domain generality of multiple-demand regions. Cereb Cortex 2022; 32:4464-4479. [PMID: 35076709 PMCID: PMC9574240 DOI: 10.1093/cercor/bhab495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 01/26/2023] Open
Abstract
A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.
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Affiliation(s)
- Douglas H Schultz
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.,Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, NJ 07102, USA
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44
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Meijer A, Königs M, Pouwels PJW, Smith J, Visscher C, Bosker RJ, Hartman E, Oosterlaan J. Effects of aerobic versus cognitively demanding exercise interventions on brain structure and function in healthy children-Results from a cluster randomized controlled trial. Psychophysiology 2022; 59:e14034. [PMID: 35292978 PMCID: PMC9541584 DOI: 10.1111/psyp.14034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 01/08/2022] [Accepted: 01/28/2022] [Indexed: 11/29/2022]
Abstract
The beneficial effects of physical activity on neurocognitive functioning in children are considered to be facilitated by physical activity-induced changes in brain structure and functioning. In this study, we examined the effects of two 14-week school-based exercise interventions in healthy children on white matter microstructure and brain activity in resting-state networks (RSNs) and whether changes in white matter microstructure and RSN activity mediate the effects of the exercise interventions on neurocognitive functioning. A total of 93 children were included in this study (51% girls, mean age 9.13 years). The exercise interventions consisted of four physical education lessons per week, focusing on either aerobic or cognitively demanding exercise and were compared with a control group that followed their regular physical education program of two lessons per week. White matter microstructure was assessed using diffusion tensor imaging in combination with tract-based spatial statistics. Independent component analysis was performed on resting-state data to identify RSNs. Furthermore, neurocognitive functioning (information processing and attention, working memory, motor response inhibition, interference control) was assessed by a set of computerized tasks. Results indicated no Group × Time effects on white matter microstructure or RSN activity, indicating no effects of the exercise interventions on these aspects of brain structure and function. Likewise, no Group × Time effects were found for neurocognitive performance. This study indicated that 14-week school-based interventions regarding neither aerobic exercise nor cognitive-demanding exercise interventions influence brain structure and brain function in healthy children. This study was registered in the Netherlands Trial Register (NTR5341).
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Affiliation(s)
- Anna Meijer
- Clinical Neuropsychology SectionVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Marsh Königs
- Department of Pediatrics, Amsterdam Reproduction & Development, Emma Neuroscience GroupEmma Children’s Hospital, Amsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Petra J. W. Pouwels
- Radiology and Nuclear medicine, Amsterdam NeuroscienceAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
| | - Joanne Smith
- Center for Human Movement SciencesUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Chris Visscher
- Center for Human Movement SciencesUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Roel J. Bosker
- Groningen Institute for Educational ResearchUniversity of GroningenGroningenThe Netherlands
| | - Esther Hartman
- Center for Human Movement SciencesUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - Jaap Oosterlaan
- Clinical Neuropsychology SectionVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Department of Pediatrics, Amsterdam Reproduction & Development, Emma Neuroscience GroupEmma Children’s Hospital, Amsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
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45
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Fazekas C, Avian A, Noehrer R, Matzer F, Vajda C, Hannich H, Neubauer A. Interoceptive awareness and self-regulation contribute to psychosomatic competence as measured by a new inventory. Wien Klin Wochenschr 2022; 134:581-592. [PMID: 32430611 PMCID: PMC9418284 DOI: 10.1007/s00508-020-01670-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/29/2020] [Indexed: 12/02/2022]
Abstract
BACKGROUND The interrelation of interoception, cognitive appraisal of bodily signals and conscious self-regulatory behavior is insufficiently understood although it may be relevant for health and disease. Therefore, it was intended to develop a novel self-report measure targeting this link. METHODS Item development was theoretically based on the multidimensional conceptual framework of the psychosomatic intelligence hypothesis and included an iterative process of refinement of items. In a preliminary test a principal components analysis (PROMAX rotation) and item analysis were calculated for item reduction. In the field test an item response theory approach was used for development of final scales and items. For validation purposes, associations with established measures of related constructs were analyzed. RESULTS The final 44-item questionnaire consisted of 6 interrelated scales: (1) interoceptive awareness, (2) mentalization, (3) body-related cognitive congruence, (4) body-related health literacy, (5) general self-regulation, and (6) stress experience and stress regulation. Psychometric properties of this instrument demonstrated good model fit, internal consistency and construct validity. According to the validation, the final instrument measures a form of competence rather than intelligence and was termed the psychosomatic competence inventory. CONCLUSION Interoceptive awareness and conscious body-related self-regulation seem to jointly contribute to a basic competence which may serve homeostatic/allostatic control; however, further research is needed to confirm the reported preliminary findings in a large-scale test.
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Affiliation(s)
- Christian Fazekas
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Auenbruggerplatz 3, 8036, Graz, Austria.
| | - Alexander Avian
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Rita Noehrer
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Auenbruggerplatz 3, 8036, Graz, Austria
| | - Franziska Matzer
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Auenbruggerplatz 3, 8036, Graz, Austria
| | - Christian Vajda
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Auenbruggerplatz 3, 8036, Graz, Austria
| | - Hans Hannich
- Department of Medical Psychology and Psychotherapy, Medical University of Graz, Auenbruggerplatz 3, 8036, Graz, Austria
- Institute of Medical Psychology, University Medicine Greifswald, Greifswald, Germany
| | - Aljoscha Neubauer
- Department of Differential Psychology, Institute of Psychology, Karl-Franzens University Graz, Graz, Austria
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46
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Mapping correlated neurological deficits after stroke to distributed brain networks. Brain Struct Funct 2022; 227:3173-3187. [PMID: 35881254 DOI: 10.1007/s00429-022-02525-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 06/12/2022] [Indexed: 11/02/2022]
Abstract
Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.
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47
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Ngo GH, Nguyen M, Chen NF, Sabuncu MR. A transformer-Based neural language model that synthesizes brain activation maps from free-form text queries. Med Image Anal 2022; 81:102540. [PMID: 35914394 DOI: 10.1016/j.media.2022.102540] [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: 03/01/2022] [Revised: 06/14/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022]
Abstract
Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses.
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Affiliation(s)
- Gia H Ngo
- School of Electrical & Computer Engineering, Cornell University, USA.
| | - Minh Nguyen
- School of Electrical & Computer Engineering, Cornell University, USA
| | - Nancy F Chen
- Institute for Infocomm Research (I2R), A*STAR, Singapore
| | - Mert R Sabuncu
- School of Electrical & Computer Engineering, Cornell University, USA; Department of Radiology, Weill Cornell Medicine, USA
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Lima Dias Pinto I, Rungratsameetaweemana N, Flaherty K, Periyannan A, Meghdadi A, Richard C, Berka C, Bansal K, Garcia JO. Intermittent brain network reconfigurations and the resistance to social media influence. Netw Neurosci 2022; 6:870-896. [PMID: 36605415 PMCID: PMC9810364 DOI: 10.1162/netn_a_00255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/10/2022] [Indexed: 01/09/2023] Open
Abstract
Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.
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Affiliation(s)
| | | | - Kristen Flaherty
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Cornell Tech, New York, NY, USA
| | - Aditi Periyannan
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Tufts University, Medford, MA, USA
| | | | | | - Chris Berka
- Advanced Brain Monitoring, Carlsbad, CA, USA
| | - Kanika Bansal
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Department of Biomedical Engineering, Columbia University, New York, NY, USA,* Corresponding Authors: ;
| | - Javier Omar Garcia
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,* Corresponding Authors: ;
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49
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Obliviate! Reviewing Neural Fundamentals of Intentional Forgetting from a Meta-Analytic Perspective. Biomedicines 2022; 10:biomedicines10071555. [PMID: 35884860 PMCID: PMC9313188 DOI: 10.3390/biomedicines10071555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/04/2022] [Accepted: 06/16/2022] [Indexed: 11/17/2022] Open
Abstract
Intentional forgetting (IF) is an important adaptive mechanism necessary for correct memory functioning, optimal psychological wellbeing, and appropriate daily performance. Due to its complexity, the neuropsychological processes that give birth to successful intentional forgetting are not yet clearly known. In this study, we used two different meta-analytic algorithms, Activation Likelihood Estimation (ALE) & Latent Dirichlet Allocation (LDA) to quantitatively assess the neural correlates of IF and to evaluate the degree of compatibility between the proposed neurobiological models and the existing brain imaging data. We found that IF involves the interaction of two networks, the main “core regions” consisting of a primarily right-lateralized frontal-parietal circuit that is activated irrespective of the paradigm used and sample characteristics and a second less constrained “supportive network” that involves frontal-hippocampal interactions when IF takes place. Additionally, our results support the validity of the inhibitory or thought suppression hypothesis. The presence of a neural signature of IF that is stable regardless of experimental paradigms is a promising finding that may open new venues for the development of effective clinical interventions.
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50
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Pines AR, Larsen B, Cui Z, Sydnor VJ, Bertolero MA, Adebimpe A, Alexander-Bloch AF, Davatzikos C, Fair DA, Gur RC, Gur RE, Li H, Milham MP, Moore TM, Murtha K, Parkes L, Thompson-Schill SL, Shanmugan S, Shinohara RT, Weinstein SM, Bassett DS, Fan Y, Satterthwaite TD. Dissociable multi-scale patterns of development in personalized brain networks. Nat Commun 2022; 13:2647. [PMID: 35551181 PMCID: PMC9098559 DOI: 10.1038/s41467-022-30244-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 04/21/2022] [Indexed: 11/24/2022] Open
Abstract
The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.
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Affiliation(s)
- Adam R Pines
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Chinese Institute for Brain Research, 102206, Beijing, China
| | - Valerie J Sydnor
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Department of Pediatrics, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Ruben C Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hongming Li
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA.,Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Tyler M Moore
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kristin Murtha
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Linden Parkes
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Sheila Shanmugan
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Yong Fan
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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