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Wang G, Jiang N, Ma Y, Chen D, Wu J, Li G, Liang D, Yan T. Connectional-style-guided contextual representation learning for brain disease diagnosis. Neural Netw 2024; 175:106296. [PMID: 38653077 DOI: 10.1016/j.neunet.2024.106296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/26/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024]
Abstract
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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Marcantoni I, Piccolantonio G, Ghoushi M, Valenti M, Reversi L, Mariotti F, Foschi N, Lattanzi S, Burattini L, Fabri M, Polonara G. Interhemispheric functional connectivity: an fMRI study in callosotomized patients. Front Hum Neurosci 2024; 18:1363098. [PMID: 38812473 PMCID: PMC11133720 DOI: 10.3389/fnhum.2024.1363098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction Functional connectivity (FC) is defined in terms of temporal correlations between physiological signals, which mainly depend upon structural (axonal) connectivity; it is commonly studied using functional magnetic resonance imaging (fMRI). Interhemispheric FC appears mostly supported by the corpus callosum (CC), although several studies investigating this aspect have not provided conclusive evidence. In this context, patients in whom the CC was resected for therapeutic reasons (split-brain patients) provide a unique opportunity for research into this issue. The present study was aimed at investigating with resting-state fMRI the interhemispheric FC in six epileptic patients who have undergone surgical resection of the CC. Methods The analysis was performed using fMRI of the Brain Software Library; the evaluation of interhemispheric FC and the recognition of the resting-state networks (RSNs) were performed using probabilistic independent component analysis. Results Generally, bilateral brain activation was often observed in primary sensory RSNs, while in the associative areas, such as those composing the default mode and fronto-parietal networks, the activation was often unilateral. Discussion These results suggest that even in the absence of the CC, some interhemispheric communication is still present. This residual FC might be supported through extra-callosal pathways that are likely subcortical, making it possible for some interhemispheric integration. Further studies are needed to confirm these conclusions.
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Affiliation(s)
- Ilaria Marcantoni
- Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona, Italy
| | - Giusi Piccolantonio
- Dipartimento di Ingegneria per Medicina di Innovazione, Università di Verona, Verona, Italy
| | - Mojgan Ghoushi
- Dipartimento di Scienze Radiologiche, Azienda Ospedaliera-Universitaria Umberto I, Ancona, Italy
| | - Marco Valenti
- Dipartimento di Scienze Radiologiche, Azienda Ospedaliera-Universitaria Umberto I, Ancona, Italy
| | - Luca Reversi
- Dipartimento di Scienze Radiologiche, Azienda Ospedaliera-Universitaria Umberto I, Ancona, Italy
| | - Francesco Mariotti
- Dipartimento di Scienze Radiologiche, Azienda Ospedaliera-Universitaria Umberto I, Ancona, Italy
| | - Nicoletta Foschi
- Dipartimento di Scienze Neurologiche, Azienda Ospedaliera-Universitaria Umberto I, Ancona, Italy
| | - Simona Lattanzi
- Dipartimento di Medicina Sperimentale e Clinica, Università Politecnica delle Marche, Ancona, Italy
| | - Laura Burattini
- Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona, Italy
| | - Mara Fabri
- Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Ancona, Italy
| | - Gabriele Polonara
- Dipartimento di Scienze Cliniche Specialistiche e Odontostomatologiche, Università Politecnica delle Marche, Ancona, Italy
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Lokossou HA, Rabuffo G, Bernard M, Bernard C, Viola A, Perles-Barbacaru TA. Impact of the day/night cycle on functional connectome in ageing male and female mice. Neuroimage 2024; 290:120576. [PMID: 38490583 DOI: 10.1016/j.neuroimage.2024.120576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024] Open
Abstract
To elucidate how time of day, sex, and age affect functional connectivity (FC) in mice, we aimed to examine whether the mouse functional connectome varied with the day/night cycle and whether it depended on sex and age. We explored C57Bl6/J mice (6♀ and 6♂) at mature age (5 ± 1 months) and middle-age (14 ± 1 months). Each mouse underwent Blood Oxygen-Level-Dependent (BOLD) resting-state functional MRI (rs-fMRI) on a 7T scanner at four different times of the day, two under the light condition and two under the dark condition. Data processing consisted of group independent component analysis (ICA) and region-level analysis using resting-state networks (RSNs) derived from literature. Linear mixed-effect models (LMEM) were used to assess the effects of sex, lighting condition and their interactions for each RSN obtained with group-ICA (RSNs-GICA) and six bilateral RSNs adapted from literature (RSNs-LIT). Our study highlighted new RSNs in mice related to day/night alternation in addition to other networks already reported in the literature. In mature mice, we found sex-related differences in brain activation only in one RSNs-GICA comprising the cortical, hippocampal, midbrain and cerebellar regions of the right hemisphere. In males, brain activity was significantly higher in the left hippocampus, the retrosplenial cortex, the superior colliculus, and the cerebellum regardless of lighting condition; consistent with the role of these structures in memory formation and integration, sleep, and sex-differences in memory processing. Experimental constraints limited the analysis to the impact of light/dark cycle on the RSNs for middle-aged females. We detected significant activation in the pineal gland during the dark condition, a finding in line with the nocturnal activity of this gland. For the analysis of RSNs-LIT, new variables "sexage" (sex and age combined) and "edges" (pairs of RSNs) were introduced. FC was calculated as the Pearson correlation between two RSNs. LMEM revealed no effect of sexage or lighting condition. The FC depended on the edges, but there were no interaction effects between sexage, lighting condition and edges. Interaction effects were detected between i) sex and lighting condition, with higher FC in males under the dark condition, ii) sexage and edges with higher FC in male brain regions related to vision, memory, and motor action. We conclude that time of day and sex should be taken into account when designing, analyzing, and interpreting functional imaging studies in rodents.
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Affiliation(s)
- Houéfa Armelle Lokossou
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France; Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France.
| | - Giovanni Rabuffo
- Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France
| | - Monique Bernard
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France
| | - Christophe Bernard
- Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France.
| | - Angèle Viola
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France
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Basaia S, Zavarella M, Rugarli G, Sferruzza G, Cividini C, Canu E, Cacciaguerra L, Bacigaluppi M, Martino G, Filippi M, Agosta F. Caudate functional networks influence brain structural changes with aging. Brain Commun 2024; 6:fcae116. [PMID: 38665962 PMCID: PMC11043654 DOI: 10.1093/braincomms/fcae116] [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: 08/17/2023] [Revised: 02/22/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Neurogenesis decline with aging may be associated with brain atrophy. Subventricular zone neuron precursor cells possibly modulate striatal neuronal activity via the release of soluble molecules. Neurogenesis decay in the subventricular zone may result in structural alterations of brain regions connected to the caudate, particularly to its medial component. The aim of this study was to investigate how the functional organization of caudate networks relates to structural brain changes with aging. One hundred and fifty-two normal subjects were recruited: 52 young healthy adults (≤35 years old), 42 middle-aged (36 ≤ 60 years old) and 58 elderly subjects (≥60 years old). In young adults, stepwise functional connectivity was used to characterize regions that connect to the medial and lateral caudate at different levels of link-step distances. A statistical comparison between the connectivity of medial and lateral caudate in young subjects was useful to define medial and lateral caudate connected regions. Atrophy of medial and lateral caudate connected regions was estimated in young, middle-aged and elderly subjects using T1-weighted images. Results showed that middle-aged and elderly adults exhibited decreased stepwise functional connectivity at one-link step from the caudate, particularly in the frontal, parietal, temporal and occipital brain regions, compared to young subjects. Elderly individuals showed increased stepwise functional connectivity in frontal, parietal, temporal and occipital lobes compared to both young and middle-aged adults. Additionally, elderly adults displayed decreased stepwise functional connectivity compared to middle-aged subjects in specific parietal and subcortical areas. Moreover, in young adults, the medial caudate showed higher direct connectivity to the basal ganglia (left thalamus), superior, middle and inferior frontal and inferior parietal gyri (medial caudate connected region) relative to the lateral caudate. Considering the opposite contrast, lateral caudate showed stronger connectivity to the basal ganglia (right pallidum), orbitofrontal, rostral anterior cingulate and insula cortices (lateral caudate connected region) compared to medial caudate. In elderly subjects, the medial caudate connected region showed greater atrophy relative to the lateral caudate connected region. Brain regions linked to the medial caudate appear to be more vulnerable to aging than lateral caudate connected areas. The adjacency to the subventricular zone may, at least partially, explain these findings. Stepwise functional connectivity analysis can be useful to evaluate the role of the subventricular zone in network disruptions in age-related neurodegenerative disorders.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Matteo Zavarella
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Giulia Rugarli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Giacomo Sferruzza
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neuroimmunology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Marco Bacigaluppi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neuroimmunology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Gianvito Martino
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Neuroimmunology Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
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Weber S, Bühler J, Loukas S, Bolton TAW, Vanini G, Bruckmaier R, Aybek S. Transient resting-state salience-limbic co-activation patterns in functional neurological disorders. Neuroimage Clin 2024; 41:103583. [PMID: 38422831 PMCID: PMC10944183 DOI: 10.1016/j.nicl.2024.103583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/09/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Functional neurological disorders were historically regarded as the manifestation of a dynamic brain lesion which might be linked to trauma or stress, although this association has not yet been directly tested yet. Analysing large-scale brain network dynamics at rest in relation to stress biomarkers assessed by salivary cortisol and amylase could provide new insights into the pathophysiology of functional neurological symptoms. METHODS Case-control resting-state functional magnetic resonance imaging study of 79 patients with mixed functional neurological disorders (i.e., functional movement disorders, functional seizures, persistent perceptual-postural dizziness) and 74 age- and sex-matched healthy controls. Using a two-step hierarchical data-driven neuroimaging approach, static functional connectivity was first computed between 17 resting-state networks. Second, dynamic alterations in these networks were examined using co-activation pattern analysis. Using a partial least squares correlation analysis, the multivariate pattern of correlation between altered temporal characteristics and stress biomarkers as well as clinical scores were evaluated. RESULTS Compared to healthy controls, patients presented with functional aberrancies of the salience-limbic network connectivity. Thus, the insula and amygdala were selected as seed-regions for the subsequent analyses. Insular co-(de)activation patterns related to the salience network, the somatomotor network and the default mode network were detected, which patients entered more frequently than controls. Moreover, an insular co-(de)activation pattern with subcortical regions together with a wide-spread co-(de)activation with diverse cortical networks was detected, which patients entered less frequently than controls. In patients, dynamic alterations conjointly correlated with amylase measures and duration of symptoms. CONCLUSION The relationship between alterations in insular co-activation patterns, stress biomarkers and clinical data proposes inter-related mechanisms involved in stress regulation and functional (network) integration. In summary, altered functional brain network dynamics were identified in patients with functional neurological disorder supporting previously raised concepts of impaired attentional and interoceptive processing.
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Affiliation(s)
- Samantha Weber
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; University of Zurich, Psychiatric University Hospital Zurich, Department of Psychiatry, Psychotherapy and Psychosomatics, 8032 Zurich, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Janine Bühler
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, 3010 Bern, Switzerland
| | - Serafeim Loukas
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Division of Development and Growth, Department of Pediatrics, University of Geneva, 1211 Geneva, Switzerland
| | - Thomas A W Bolton
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; Department of Radiology, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Giorgio Vanini
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Rupert Bruckmaier
- Veterinary Physiology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
| | - Selma Aybek
- Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, 3012 Bern, Switzerland; Faculty of Science and Medicine, University of Fribourg, 1700 Fribourg, Switzerland
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Liu H, Ma Z, Wei L, Chen Z, Peng Y, Jiao Z, Bai H, Jing B. A radiomics-based brain network in T1 images: construction, attributes, and applications. Cereb Cortex 2024; 34:bhae016. [PMID: 38300184 PMCID: PMC10839838 DOI: 10.1093/cercor/bhae016] [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: 11/28/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/02/2024] Open
Abstract
T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.
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Affiliation(s)
- Han Liu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishilu Road, Xicheng District, Beijing 100045, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Zhe Ma
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, 127 Dongming Road, Jinshui District, Zhengzhou, Henan 450008, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Lijiang Wei
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
| | - Zhenpeng Chen
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishilu Road, Xicheng District, Beijing 100045, China
| | - Zhicheng Jiao
- Department of Diagnostic Imaging, Brown University, 593 Eddy Street, Providence, Rhode Island 02903, United States
| | - Harrison Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University, 1800 Orleans Street, Baltimore, Maryland 21205, United States
| | - Bin Jing
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao Youanmenwai, Fengtai District, Beijing 100069, China
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Huang Z, Mashour GA, Hudetz AG. Propofol Disrupts the Functional Core-Matrix Architecture of the Thalamus in Humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576934. [PMID: 38328136 PMCID: PMC10849566 DOI: 10.1101/2024.01.23.576934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. We observed a significant shift in this geometry during unconsciousness, marked by the dominance of unimodal over transmodal geometry. This alteration was closely linked to the spatial variations in the density of matrix cells within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
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Yulug B, Ayyildiz S, Sayman D, Karaca R, Ipek L, Cankaya S, Salar AB, Ayyildiz B, Mikuta C, Yagci N, Oktem EO, Ozsimsek A, Velioglu HA, Hanoglu L. The functional role of the pulvinar in discriminating between objective and subjective cognitive impairment in major depressive disorder. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12450. [PMID: 38356480 PMCID: PMC10865482 DOI: 10.1002/trc2.12450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 02/16/2024]
Abstract
INTRODUCTION Emotionally driven cognitive complaints represent a major diagnostic challenge for clinicians and indicate the importance of objective confirmation of the accuracy of depressive patients' descriptions of their cognitive symptoms. METHODS We compared cognitive status and structural and functional brain connectivity changes in the pulvinar and hippocampus between patients with total depression and healthy controls. The depressive group was also classified as "amnestic" or "nonamnestic," based on the members' subjective reports concerning their forgetfulness. We then sought to determine whether these patients would differ in terms of objective neuroimaging and cognitive findings. RESULTS The right pulvinar exhibited altered connectivity in individuals with depression with objective cognitive impairment, a finding which was not apparent in depressive patients with subjective cognitive impairment. DISCUSSION The pulvinar may play a role in depression-related cognitive impairments. Connectivity network changes may differ between objective and subjective cognitive impairment in depression and may play a role in the increased risk of dementia in patients with depression.
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Affiliation(s)
- Burak Yulug
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
- Department of Neurology and NeuroscienceIstanbul Medipol UniversityIstanbulTurkey
| | - Sevilay Ayyildiz
- School of MedicineDepartment of NeuroradiologyTechnical University of MunichMunichGermany
- School of MedicineTUM‐NIC Neuroimaging CenterTechnical University of MunichMunichGermany
- Anatomy PhD ProgramGraduate School of Health SciencesKocaeli UniversityIstanbulTurkey
| | - Dila Sayman
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ramazan Karaca
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Lutfiye Ipek
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Seyda Cankaya
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ali Behram Salar
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN)Health Sciences and Technology Research Institute (SABITA)Istanbul Medipol UniversityIstanbulTurkey
| | - Behcet Ayyildiz
- Anatomy PhD ProgramGraduate School of Health SciencesKocaeli UniversityIstanbulTurkey
| | - Christian Mikuta
- Translational Research CenterUniversity Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
- Interdisciplinary Biosciences Doctoral Training PartnershipDepartment of PhysiologyAnatomy and GeneticsUniversity of OxfordOxfordUK
| | - Nilay Yagci
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ece Ozdemir Oktem
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Ahmet Ozsimsek
- Department of Neurology and NeuroscienceAlanya Alaaddin Keykubat UniversityAntalyaTurkey
| | - Halil Aziz Velioglu
- School of MedicineTUM‐NIC Neuroimaging CenterTechnical University of MunichMunichGermany
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
| | - Lutfu Hanoglu
- Department of Neurology and NeuroscienceIstanbul Medipol UniversityIstanbulTurkey
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9
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Capouskova K, Zamora‐López G, Kringelbach ML, Deco G. Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest. Hum Brain Mapp 2023; 44:6349-6363. [PMID: 37846551 PMCID: PMC10681658 DOI: 10.1002/hbm.26511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short-lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Gorka Zamora‐López
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical MedicineAarhus UniversityAarhusDenmark
- Centre for Eudaimonia and Human Flourishing, Linacre CollegeUniversity of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
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10
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Aru J, Larkum ME, Shine JM. The feasibility of artificial consciousness through the lens of neuroscience. Trends Neurosci 2023; 46:1008-1017. [PMID: 37863713 DOI: 10.1016/j.tins.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/23/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023]
Abstract
Interactions with large language models (LLMs) have led to the suggestion that these models may soon be conscious. From the perspective of neuroscience, this position is difficult to defend. For one, the inputs to LLMs lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architectures of present-day artificial intelligence algorithms are missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today. The existence of living organisms depends on their actions and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness.
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Affiliation(s)
- Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
| | - Matthew E Larkum
- Institute of Biology, Humboldt University Berlin, Berlin, Germany.
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia.
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11
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Lin S, Zhang C, Zhang Y, Chen S, Lin X, Peng B, Xu Z, Hou G, Qiu Y. Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study. J Affect Disord 2023; 341:304-312. [PMID: 37661059 DOI: 10.1016/j.jad.2023.08.139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode). METHODS Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations. RESULTS BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity. CONCLUSION These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.
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Affiliation(s)
- Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Ave 89, Nanshan district, Shenzhen 518000, PR China
| | - Chao Zhang
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong 510060, People's Republic of China
| | - Yingli Zhang
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Ave 89, Nanshan district, Shenzhen 518000, PR China
| | - Xiaoshan Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Ave 89, Nanshan district, Shenzhen 518000, PR China
| | - Bo Peng
- Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong 518020, People's Republic of China
| | - Ziyun Xu
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Ave 89, Nanshan district, Shenzhen 518000, PR China.
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12
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Zarghami TS. A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network. Brain Struct Funct 2023; 228:1917-1941. [PMID: 37658184 DOI: 10.1007/s00429-023-02697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined.
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Affiliation(s)
- Tahereh S Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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13
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Yang Y, Zheng Z, Liu L, Zheng H, Zhen Y, Zheng Y, Wang X, Tang S. Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes. Nat Commun 2023; 14:6744. [PMID: 37875493 PMCID: PMC10598018 DOI: 10.1038/s41467-023-42053-4] [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: 07/12/2022] [Accepted: 09/28/2023] [Indexed: 10/26/2023] Open
Abstract
While the link between brain structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupling from unimodal to transmodal cortex. However, this hypothesis is constrained by the underlying models which may neglect requisite information. Here we relate structural and functional connectivity derived from diffusion and functional MRI through orthogonal eigenmodes governing frequency-specific diffusion patterns. We find that low-frequency eigenmodes contribute little to functional interactions in transmodal cortex, resulting in divergent structure-function relationships. Conversely, high-frequency eigenmodes predominantly support neuronal coactivation patterns in these areas, inducing structure-function convergence along a unimodal-transmodal hierarchy. High-frequency information, although weak and scattered, could enhance the structure-function tethering, especially in transmodal association cortices. Our findings suggest that the structure-function decoupling may not be an intrinsic property of brain organization, but can be narrowed through multiplexed and regionally specialized spatiotemporal propagation regimes.
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Affiliation(s)
- Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, 100191, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China
| | - Zhiming Zheng
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China
- Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, 100191, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China
- PengCheng Laboratory, Shenzhen, 518055, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China
| | - Longzhao Liu
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China
- Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, 100191, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China
- PengCheng Laboratory, Shenzhen, 518055, China
| | - Hongwei Zheng
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China
- Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing, 100085, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, 100191, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, 100191, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China
| | - Xin Wang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China.
- Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China.
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, 100191, China.
- Zhongguancun Laboratory, Beijing, China.
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China.
- PengCheng Laboratory, Shenzhen, 518055, China.
| | - Shaoting Tang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, 100191, China.
- Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China.
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, 100191, China.
- Zhongguancun Laboratory, Beijing, China.
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China.
- PengCheng Laboratory, Shenzhen, 518055, China.
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, China.
- School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China.
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14
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Groot JM, Miletic S, Isherwood SJS, Tse DHY, Habli S, Håberg AK, Forstmann BU, Bazin PL, Mittner M. Echoes from Intrinsic Connectivity Networks in the Subcortex. J Neurosci 2023; 43:6609-6618. [PMID: 37562962 PMCID: PMC10538587 DOI: 10.1523/jneurosci.1020-23.2023] [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: 06/01/2023] [Revised: 07/11/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.
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Affiliation(s)
- Josephine M Groot
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Steven Miletic
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Scott J S Isherwood
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Desmond H Y Tse
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Sarah Habli
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, 8900, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, 8900, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, 7006, Norway
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Pierre-Louis Bazin
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Departments of Neurophysics and Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany
| | - Matthias Mittner
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
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15
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Uselman TW, Jacobs RE, Bearer EL. Reconfiguration of brain-wide neural activity after early life adversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.10.557058. [PMID: 38328213 PMCID: PMC10849645 DOI: 10.1101/2023.09.10.557058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Early life adversity (ELA) predisposes individuals to both physical and mental disorders lifelong. How ELA affects brain function leading to this vulnerability is under intense investigation. Research has begun to shed light on ELA effects on localized brain regions within defined circuits. However, investigations into brain-wide neural activity that includes multiple localized regions, determines relationships of activity between regions and identifies shifts of activity in response to experiential conditions is necessary. Here, we performed longitudinal manganese-enhanced magnetic resonance imaging (MEMRI) to image the brain in normally reared or ELA-exposed adults. Images were captured in the freely moving home cage condition, and short- and long-term after naturalistic threat. Images were analyzed with new computational methods, including automated segmentation and fractional activation or difference volumes. We found that neural activity was increased after ELA compared to normal rearing in multiple brain regions, some of which are involved in defensive and/or reward circuitry. Widely distributed patterns of neural activity, "brain states", and their dynamics after threat were altered with ELA. Upon acute threat, ELA-mice retained heightened neural activity within many of these regions, and new hyperactive responses emerged in monoaminergic centers of the mid- and hindbrain. Nine days after acute threat, heightened neural activity remained within locus coeruleus and increased within posterior amygdala, ventral hippocampus, and dorso- and ventromedial hypothalamus, while reduced activity emerged within medial prefrontal cortical regions (prelimbic, infralimbic, anterior cingulate). These results reveal that functional imbalances arise between multiple brain-systems which are dependent upon context and cumulative experiences after ELA.
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Affiliation(s)
- Taylor W Uselman
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131
| | - Russell E Jacobs
- Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033
- California Institute of Technology, Pasadena, CA 91125
| | - Elaine L Bearer
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131
- California Institute of Technology, Pasadena, CA 91125
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16
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Shine JM, Lewis LD, Garrett DD, Hwang K. The impact of the human thalamus on brain-wide information processing. Nat Rev Neurosci 2023; 24:416-430. [PMID: 37237103 DOI: 10.1038/s41583-023-00701-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 05/28/2023]
Abstract
The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.
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Affiliation(s)
- James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Kai Hwang
- Cognitive Control Collaborative, Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA, USA.
- Department of Psychiatry, The University of Iowa, Iowa City, IA, USA.
- Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA.
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17
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Hou C, Jiang S, Liu M, Li H, Zhang L, Duan M, Yao G, He H, Yao D, Luo C. Spatiotemporal dynamics of functional connectivity and association with molecular architecture in schizophrenia. Cereb Cortex 2023:7179746. [PMID: 37231204 DOI: 10.1093/cercor/bhad185] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/27/2023] Open
Abstract
Schizophrenia is a self-disorder characterized by disrupted brain dynamics and architectures of multiple molecules. This study aims to explore spatiotemporal dynamics and its association with psychiatric symptoms. Resting-state functional magnetic resonance imaging data were collected from 98 patients with schizophrenia. Brain dynamics included the temporal and spatial variations in functional connectivity density and association with symptom scores were evaluated. Moreover, the spatial association between dynamics and receptors/transporters according to prior molecular imaging in healthy subjects was examined. Patients demonstrated decreased temporal variation and increased spatial variation in perceptual and attentional systems. However, increased temporal variation and decreased spatial variation were revealed in higher order networks and subcortical networks in patients. Specifically, spatial variation in perceptual and attentional systems was associated with symptom severity. Moreover, case-control differences were associated with dopamine, serotonin and mu-opioid receptor densities, serotonin reuptake transporter density, dopamine transporter density, and dopamine synthesis capacity. Therefore, this study implicates the abnormal dynamic interactions between the perceptual system and cortical core networks; in addition, the subcortical regions play a role in the dynamic interaction among the cortical regions in schizophrenia. These convergent findings support the importance of brain dynamics and emphasize the contribution of primary information processing to the pathological mechanism underlying schizophrenia.
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Affiliation(s)
- Changyue Hou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Mei Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
| | - Lang Zhang
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Gang Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- Department of Psychiatry, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
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18
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Huang Z, Mashour GA, Hudetz AG. Functional geometry of the cortex encodes dimensions of consciousness. Nat Commun 2023; 14:72. [PMID: 36604428 PMCID: PMC9814511 DOI: 10.1038/s41467-022-35764-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Consciousness is a multidimensional phenomenon, but key dimensions such as awareness and wakefulness have been described conceptually rather than neurobiologically. We hypothesize that dimensions of consciousness are encoded in multiple neurofunctional dimensions of the brain. We analyze cortical gradients, which are continua of the brain's overarching functional geometry, to characterize these neurofunctional dimensions. We demonstrate that disruptions of human consciousness - due to pharmacological, neuropathological, or psychiatric causes - are associated with a degradation of one or more of the major cortical gradients depending on the state. Network-specific reconfigurations within the multidimensional cortical gradient space are associated with behavioral unresponsiveness of various etiologies, and these spatial reconfigurations correlate with a temporal disruption of structured transitions of dynamic brain states. In this work, we therefore provide a unifying neurofunctional framework for multiple dimensions of human consciousness in both health and disease.
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Affiliation(s)
- Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. .,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
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19
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Shine JM. Adaptively navigating affordance landscapes: How interactions between the superior colliculus and thalamus coordinate complex, adaptive behaviour. Neurosci Biobehav Rev 2022; 143:104921. [DOI: 10.1016/j.neubiorev.2022.104921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 11/06/2022]
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20
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Representations and decodability of diverse cognitive functions are preserved across the human cortex, cerebellum, and subcortex. Commun Biol 2022; 5:1245. [PMCID: PMC9663596 DOI: 10.1038/s42003-022-04221-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 11/03/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractWhich part of the brain contributes to our complex cognitive processes? Studies have revealed contributions of the cerebellum and subcortex to higher-order cognitive functions; however, it has been unclear whether such functional representations are preserved across the cortex, cerebellum, and subcortex. In this study, we use functional magnetic resonance imaging data with 103 cognitive tasks and construct three voxel-wise encoding and decoding models independently using cortical, cerebellar, and subcortical voxels. Representational similarity analysis reveals that the structure of task representations is preserved across the three brain parts. Principal component analysis visualizes distinct organizations of abstract cognitive functions in each part of the cerebellum and subcortex. More than 90% of the cognitive tasks are decodable from the cerebellum and subcortical activities, even for the novel tasks not included in model training. Furthermore, we show that the cerebellum and subcortex have sufficient information to reconstruct activity in the cerebral cortex.
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21
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Favaretto C, Allegra M, Deco G, Metcalf NV, Griffis JC, Shulman GL, Brovelli A, Corbetta M. Subcortical-cortical dynamical states of the human brain and their breakdown in stroke. Nat Commun 2022; 13:5069. [PMID: 36038566 PMCID: PMC9424299 DOI: 10.1038/s41467-022-32304-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01–0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome. Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.
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Affiliation(s)
- Chiara Favaretto
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy.,Department of Physics and Astronomy "Galileo Galilei", University of Padova, via Marzolo 8, 35131, Padova, Italy.,Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Catalonia, Spain
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA.,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy. .,Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, 35129, Padova, Italy.
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22
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Capouskova K, Kringelbach ML, Deco G. Modes of cognition: Evidence from metastable brain dynamics. Neuroimage 2022; 260:119489. [PMID: 35882268 DOI: 10.1016/j.neuroimage.2022.119489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 01/31/2023] Open
Abstract
Managing cognitive load depends on adequate resource allocation by the human brain through the engagement of metastable substates, which are large-scale functional networks that change over time. We employed a novel analysis method, deep autoencoder dynamical analysis (DADA), with 100 healthy adults selected from the Human Connectome Project (HCP) data set in rest and six cognitive tasks. The deep autoencoder of DADA described seven recurrent stochastic metastable substates from the functional connectome of BOLD phase coherence matrices. These substates were significantly differentiated in terms of their probability of appearance, time duration, and spatial attributes. We found that during different cognitive tasks, there was a higher probability of having more connected substates dominated by a high degree of connectivity in the thalamus. In addition, compared with those during tasks, resting brain dynamics have a lower level of predictability, indicating a more uniform distribution of metastability between substates, quantified by higher entropy. These novel findings provide empirical evidence for the philosophically motivated cognitive theory, suggesting on-line and off-line as two fundamentally distinct modes of cognition. On-line cognition refers to task-dependent engagement with the sensory input, while off-line cognition is a slower, environmentally detached mode engaged with decision and planning. Overall, the DADA framework provides a bridge between neuroscience and cognitive theory that can be further explored in the future.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, Barcelona 08005, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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23
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DiFabio MS, Smith DR, Breedlove KM, Buckley TA, Johnson CL. Relationships between aggression, sensation seeking, brain stiffness, and head impact exposure: Implications for head impact prevention in ice hockey. Brain Behav 2022; 12:e2627. [PMID: 35620849 PMCID: PMC9304837 DOI: 10.1002/brb3.2627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/22/2022] [Accepted: 04/23/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES The objectives of this study were to (1) examine the relationship between the number of head impacts sustained in a season of men's collegiate club ice hockey and behavioral traits of aggression and sensation seeking, and (2) explore the neural correlates of these behaviors using neuroimaging. DESIGN Retrospective cohort study. METHODS Participants (n = 18) completed baseline surveys to quantify self-reported aggression and sensation-seeking tendencies. Aggression related to playing style was quantified through penalty minutes accrued during a season. Participants wore head impact sensors throughout a season to quantify the number of head impacts sustained. Participants (n = 15) also completed baseline anatomical and magnetic elastography neuroimaging scans to measure brain volumetric and viscoelastic properties. Pearson correlation analyses were performed to examine relationships between (1) impacts, aggression, and sensation seeking, and (2) impacts, aggression, and sensation seeking and brain volume, stiffness, and damping ratio, as an exploratory analysis. RESULTS Number of head impacts sustained was significantly related to the number of penalty minutes accrued, normalized to number of games played (r = .62, p < .01). Our secondary, exploratory analysis revealed that number of impacts, sensation seeking, and aggression were related to stiffness or damping ratio of the thalamus, amygdala, hippocampus, and frontal cortex, but not volume. CONCLUSIONS A more aggressive playing style was related to an increased number of head impacts sustained, which may provide evidence for future studies of head impact prevention. Further, magnetic resonance elastography may aid to monitor behavior or head impact exposure. Researchers should continue to examine this relationship and consider targeting behavioral modification programs of aggression to decrease head impact exposure in ice hockey.
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Affiliation(s)
- Melissa S DiFabio
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, USA.,Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximillians-Universität München, Munich, Germany
| | - Daniel R Smith
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, USA
| | - Katherine M Breedlove
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas A Buckley
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, USA
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24
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A Riemannian approach to predicting brain function from the structural connectome. Neuroimage 2022; 257:119299. [PMID: 35636736 DOI: 10.1016/j.neuroimage.2022.119299] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022] Open
Abstract
Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.
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25
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Seguin C, Mansour L S, Sporns O, Zalesky A, Calamante F. Network communication models narrow the gap between the modular organization of structural and functional brain networks. Neuroimage 2022; 257:119323. [PMID: 35605765 DOI: 10.1016/j.neuroimage.2022.119323] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 05/17/2022] [Indexed: 11/28/2022] Open
Abstract
Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States; Cognitive Science Program, Indiana University, Bloomington, IN, United States; Program in Neuroscience, Indiana University, Bloomington, IN, United States; Network Science Institute, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Fernando Calamante
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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26
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Cortico-subcortical interactions in overlapping communities of edge functional connectivity. Neuroimage 2022; 250:118971. [PMID: 35131435 PMCID: PMC9903436 DOI: 10.1016/j.neuroimage.2022.118971] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/25/2022] [Accepted: 02/03/2022] [Indexed: 02/01/2023] Open
Abstract
Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.
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27
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Ebisch SJH, Scalabrini A, Northoff G, Mucci C, Sergi MR, Saggino A, Aquino A, Alparone FR, Perrucci MG, Gallese V, Di Plinio S. Intrinsic Shapes of Empathy: Functional Brain Network Topology Encodes Intersubjective Experience and Awareness Traits. Brain Sci 2022; 12:brainsci12040477. [PMID: 35448008 PMCID: PMC9024660 DOI: 10.3390/brainsci12040477] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 02/04/2023] Open
Abstract
Trait empathy is an essential personality feature in the intricacy of typical social inclinations of individuals. Empathy is likely supported by multilevel neuronal network functioning, whereas local topological properties determine network integrity. In the present functional MRI study (N = 116), we aimed to trace empathic traits to the intrinsic brain network architecture. Empathy was conceived as composed of two dimensions within the concept of pre-reflective, intersubjective understanding. Vicarious experience consists of the tendency to resonate with the feelings of other individuals, whereas intuitive understanding refers to a natural awareness of others’ emotional states. Analyses of graph theoretical measures of centrality showed a relationship between the fronto-parietal network and psychometric measures of vicarious experience, whereas intuitive understanding was associated with sensorimotor and subcortical networks. Salience network regions could constitute hubs for information processing underlying both dimensions. The network properties related to empathy dimensions mainly concern inter-network information flow. Moreover, interaction effects implied several sex differences in the relationship between functional network organization and trait empathy. These results reveal that distinct intrinsic topological network features explain individual differences in separate dimensions of intersubjective understanding. The findings could help understand the impact of brain damage or stimulation through alterations of empathy-related network integrity.
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Affiliation(s)
- Sjoerd J. H. Ebisch
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.A.); (F.R.A.); (M.G.P.); (S.D.P.)
- Institute of Advanced Biomedical Technologies (ITAB), G. d’Annunzio University of Chieti-Pescara, Via Luigi Polacchi 11, 66100 Chieti, Italy
- Correspondence:
| | - Andrea Scalabrini
- Department of Psychological, Health and Territorial Sciences (DiSPuTer), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Georg Northoff
- The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
- Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou 310030, China
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310030, China
- TMU Research Centre for Brain and Consciousness, Shuang Hospital, Taipei Medical University, Taipei 110, Taiwan
- Graduate Institute of Humanities in Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Clara Mucci
- Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy;
| | - Maria Rita Sergi
- Department of Medicine and Aging Sciences, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (M.R.S.); (A.S.)
| | - Aristide Saggino
- Department of Medicine and Aging Sciences, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (M.R.S.); (A.S.)
| | - Antonio Aquino
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.A.); (F.R.A.); (M.G.P.); (S.D.P.)
| | - Francesca R. Alparone
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.A.); (F.R.A.); (M.G.P.); (S.D.P.)
| | - Mauro Gianni Perrucci
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.A.); (F.R.A.); (M.G.P.); (S.D.P.)
- Institute of Advanced Biomedical Technologies (ITAB), G. d’Annunzio University of Chieti-Pescara, Via Luigi Polacchi 11, 66100 Chieti, Italy
| | - Vittorio Gallese
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy;
| | - Simone Di Plinio
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.A.); (F.R.A.); (M.G.P.); (S.D.P.)
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28
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Tanaka S, Kirino E. Right-Lateralized Enhancement of the Auditory Cortical Network During Imagined Music Performance. Front Neurosci 2022; 16:739858. [PMID: 35221895 PMCID: PMC8866933 DOI: 10.3389/fnins.2022.739858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 01/10/2022] [Indexed: 11/23/2022] Open
Abstract
Although the primary role of the auditory cortical areas is to process actual sounds, these areas are also activated by tasks that process imagined music, suggesting that the auditory cortical areas are involved in the processes underlying musical imagery. However, the mechanism by which these areas are involved in such processes is unknown. To elucidate this feature of the auditory cortical areas, we analyzed their functional networks during imagined music performance in comparison with those in the resting condition. While imagined music performance does not produce any musical sounds, the participants heard the same actual sounds from the MRI equipment in both experimental conditions. Therefore, if the functional connectivity between these conditions differs significantly, one can infer that the auditory cortical areas are actively involved in imagined music performance. Our functional connectivity analysis revealed a significant enhancement in the auditory network during imagined music performance relative to the resting condition. The reconfiguration profile of the auditory network showed a clear right-lateralized increase in the connectivity of the auditory cortical areas with brain regions associated with cognitive, memory, and emotional information processing. On the basis of these results, we hypothesize that auditory cortical areas and their networks are actively involved in imagined music performance through the integration of auditory imagery into mental imagery associated with music performance.
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Affiliation(s)
- Shoji Tanaka
- Department of Information and Communication Sciences, Sophia University, Tokyo, Japan
- *Correspondence: Shoji Tanaka,
| | - Eiji Kirino
- Department of Psychiatry, Juntendo University School of Medicine, Tokyo, Japan
- Juntendo University Shizuoka Hospital, Shizuoka, Japan
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29
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Dall'Orso S, Arichi T, Fitzgibbon SP, Edwards AD, Burdet E, Muceli S. Development of functional organization within the sensorimotor network across the perinatal period. Hum Brain Mapp 2022; 43:2249-2261. [PMID: 35088920 PMCID: PMC8996360 DOI: 10.1002/hbm.25785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/30/2021] [Accepted: 01/10/2022] [Indexed: 11/18/2022] Open
Abstract
In the mature human brain, the neural processing related to different body parts is reflected in patterns of functional connectivity, which is strongest between functional homologs in opposite cortical hemispheres. To understand how this organization is first established, we investigated functional connectivity between limb regions in the sensorimotor cortex in 400 preterm and term infants aged across the equivalent period to the third trimester of gestation (32–45 weeks postmenstrual age). Masks were obtained from empirically derived functional responses in neonates from an independent data set. We demonstrate the early presence of a crude but spatially organized functional connectivity, that rapidly matures across the preterm period to achieve an adult‐like configuration by the normal time of birth. Specifically, connectivity was strongest between homolog regions, followed by connectivity between adjacent regions (different limbs but same hemisphere) already in the preterm brain, and increased with age. These changes were specific to the sensorimotor network. Crucially, these trajectories were strongly dependent on age more than age of birth. This demonstrates that during the perinatal period the sensorimotor cortex undergoes preprogrammed changes determining the functional movement organization that are not altered by preterm birth in absence of brain injury.
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Affiliation(s)
- Sofia Dall'Orso
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London.,Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.,Paediatric Neurosciences, Evelina London Children's Hospital, St. Thomas' Hospital, London, UK.,Medical Research Council Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London.,Medical Research Council Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Etienne Burdet
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Silvia Muceli
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London
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30
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Jiang G, Feng Y, Li M, Wen H, Wang T, Shen Y, Chen Z, Li S. Distinct alterations of functional connectivity of the basal forebrain subregions in insomnia disorder. Front Psychiatry 2022; 13:1036997. [PMID: 36311494 PMCID: PMC9606586 DOI: 10.3389/fpsyt.2022.1036997] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cholinergic basal forebrain (BF) plays an important role in sleep-wake regulation and is implicated in cortical arousal and activation. However, less is known currently regarding the abnormal BF-related neuronal circuit in human patients with insomnia disorder (ID). In this study, we aimed to explore alterations of functional connectivity (FC) in subregions of the BF and the relationships between FC alterations and sleep and mood measures in ID. MATERIALS AND METHODS One hundred and two ID patients and ninety-six healthy controls (HC) were included in this study. Each subject underwent both resting-state fMRI and high-resolution anatomical scanning. All participants completed the sleep and mood questionnaires in ID patients. Voxel-based resting-state FC in each BF subregion (Ch_123 and Ch_4) were computed. For the voxel-wise FC differences between groups, a two-sample t-test was performed on the individual maps in a voxel-by-voxel manner. To examine linear relationships with sleep and mood measures, Pearson correlations were calculated between FC alterations and sleep and mood measures, respectively. RESULTS The ID group showed significantly decreased FC between the medial superior frontal gyrus and Ch_123 compared to HC. However, increased FC between the midbrain and Ch_4 was found in ID based on the voxel-wise analysis. The correlation analysis only revealed that the altered FC between the midbrain with Ch_4 was significantly negatively correlated with the self-rating anxiety scale. CONCLUSION Our findings of decreased FC between Ch_123 and medial superior frontal gyrus and increased FC between midbrain and Ch4 suggest distinct roles of subregions of BF underlying the neurobiology of ID.
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Affiliation(s)
- Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Ying Feng
- Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Meng Li
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Hua Wen
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Tianyue Wang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yanan Shen
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Ziwei Chen
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Jinan University, Guangzhou, China
| | - Shumei Li
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
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31
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A thalamo-centric neural signature for restructuring negative self-beliefs. Mol Psychiatry 2022; 27:1611-1617. [PMID: 34974523 PMCID: PMC9095461 DOI: 10.1038/s41380-021-01402-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/13/2021] [Accepted: 11/24/2021] [Indexed: 11/08/2022]
Abstract
Negative self-beliefs are a core feature of psychopathology. Despite this, we have a limited understanding of the brain mechanisms by which negative self-beliefs are cognitively restructured. Using a novel paradigm, we had participants use Socratic questioning techniques to restructure negative beliefs during ultra-high resolution 7-Tesla functional magnetic resonance imaging (UHF 7 T fMRI) scanning. Cognitive restructuring elicited prominent activation in a fronto-striato-thalamic circuit, including the mediodorsal thalamus (MD), a group of deep subcortical nuclei believed to synchronize and integrate prefrontal cortex activity, but which has seldom been directly examined with fMRI due to its small size. Increased activity was also identified in the medial prefrontal cortex (MPFC), a region consistently activated by internally focused mental processing, as well as in lateral prefrontal regions associated with regulating emotional reactivity. Using Dynamic Causal Modelling (DCM), evidence was found to support the MD as having a strong excitatory effect on the activity of regions within the broader network mediating cognitive restructuring. Moreover, the degree to which participants modulated MPFC-to-MD effective connectivity during cognitive restructuring predicted their individual tendency to engage in repetitive negative thinking. Our findings represent a major shift from a cortico-centric framework of cognition and provide important mechanistic insights into how the MD facilitates key processes in cognitive interventions for common psychiatric disorders. In addition to relaying integrative information across basal ganglia and the cortex, we propose a multifaceted role for the MD whose broad excitatory pathways act to increase synchrony between cortical regions to sustain complex mental representations, including the self.
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32
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Wang L, Zhou C, Cheng W, Rolls ET, Huang P, Ma N, Liu Y, Zhang Y, Guan X, Guo T, Wu J, Gao T, Xuan M, Gu Q, Xu X, Zhang B, Gong W, Du J, Zhang W, Feng J, Zhang M. Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease. Hum Brain Mapp 2021; 43:1598-1610. [PMID: 34904766 PMCID: PMC8886656 DOI: 10.1002/hbm.25745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.
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Affiliation(s)
- Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ningning Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yajuan Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Huang H, Zhao K, Zhu W, Li H, Zhu W. Abnormal Cerebral Blood Flow and Functional Connectivity Strength in Subjects With White Matter Hyperintensities. Front Neurol 2021; 12:752762. [PMID: 34744987 PMCID: PMC8564178 DOI: 10.3389/fneur.2021.752762] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/13/2021] [Indexed: 11/16/2022] Open
Abstract
White matter hyperintensities (WMHs) are common neuroimaging findings in the aging population and are associated with various clinical symptoms, especially cognitive impairment. Abnormal global cerebral blood flow (CBF) and specific functional connections have been reported in subjects with higher WMH loads. Nevertheless, the comprehensive functional mechanisms underlying WMH are yet to be established. In this study, by combining resting-state functional magnetic resonance imaging and arterial spin labeling, we investigated the neurovascular dysfunction in subjects with WMH in CBF, functional connectivity strength (FCS), and CBF–FCS coupling. The whole-brain alterations of all these measures were explored among non-dementia subjects with different WMH loads using a fine-grained Human Brainnetome Atlas. In addition, exploratory mediation analyses were conducted to further determine the relationships between these neuroimaging indicators, WMH load, and cognition. The results showed that subjects with higher WMH loads displayed decreased CBF and FCS mainly in regions involving the cognitive- and emotional-related brain networks, including the default mode network, salience network, and central executive network. Notably, subjects with higher WMH loads also showed an abnormal regional CBF–FCS coupling in several regions of the thalamus, posterior cingulate cortex, and parahippocampal gyrus involving the default mode network. Furthermore, regional CBF in the right inferior temporal gyrus and right dorsal caudate may mediate the relationship between WMH load and cognition in WMH subjects. These findings indicated characteristic changes in cerebral blood supply, brain activity, and neurovascular coupling in regions involving specific brain networks with the development of WMH, providing further information on pathophysiology underpinnings of the WMH and related cognitive impairment.
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Affiliation(s)
- Hao Huang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Li
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenhao Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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34
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Xiong G, Dong D, Cheng C, Jiang Y, Sun X, He J, Li C, Gao Y, Zhong X, Zhao H, Wang X, Yao S. Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties. NEUROIMAGE-CLINICAL 2021; 32:102871. [PMID: 34749291 PMCID: PMC8578037 DOI: 10.1016/j.nicl.2021.102871] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/20/2021] [Accepted: 10/29/2021] [Indexed: 11/18/2022]
Abstract
It has been proposed recently that major depressive disorder (MDD) could represent an adaptation to conserve energy after the perceived loss of an investment in a vital source, such as group identity, personal assets, or relationships. Energy conserving behaviors associated with MDD may form a persistent marker in brain regions and networks involved in cognition and emotion regulation. In this study, we examined whether subcortical regions and volume-based structural covariance networks (SCNs) have state-independent alterations (trait markers). First-episode drug-naïve currently depressed (cMDD) patients (N = 131), remitted MDD (RD) patients (N = 67), and healthy controls (HCs, N = 235) underwent structural magnetic resonance imaging (MRI). Subcortical gray matter volumes (GMVs) were calculated in FreeSurfer software, and group differences in GMVs and SCN were analyzed. Compared to HCs, major findings were decreased GMVs of left pallidum and pulvinar anterior of thalamus in the cMDD and RD groups, indicative of a trait marker. Relative to HCs, subcortical SCNs of both cMDD and RD patients were found to have reduced small-world-ness and path length, which together may represent a trait-like topological feature of depression. In sum, the left pallidum, left pulvinar anterior of thalamus volumetric alterations may represent trait marker and reduced small-world-ness, path length may represent trait-like topological feature of MDD.
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Affiliation(s)
- Ge Xiong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Daifeng Dong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chang Cheng
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Yali Jiang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Xiaoqiang Sun
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Jiayue He
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Chuting Li
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China
| | - Yidian Gao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xue Zhong
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Haofei Zhao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China; Medical Psychological Institute of Central South University, Changsha, Hunan 410011, China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan 410011, China.
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35
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Kawabata K, Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Ohdake R, Masuda M, Ogura A, Kato T, Koyama S, Katsuno M, Wakabayashi T, Kuzuya M, Hoshiyama M, Isoda H, Naganawa S, Ozaki N, Sobue G. Bridging large-scale cortical networks: Integrative and function-specific hubs in the thalamus. iScience 2021; 24:103106. [PMID: 34622159 PMCID: PMC8479782 DOI: 10.1016/j.isci.2021.103106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 08/02/2021] [Accepted: 09/02/2021] [Indexed: 12/03/2022] Open
Abstract
The thalamus is critical for the brain's integrative hub functions; however, the localization and characterization of the different thalamic hubs remain unclear. Using a voxel-level network measure called functional connectivity overlap ratio (FCOR), we examined the thalamus' association with large-scale resting-state networks (RSNs) to elucidate its connector hub roles. Connections to the core-neurocognitive networks were localized in the anterior and medial parts, such as the anteroventral and mediodorsal nuclei areas. Regions functionally connected to the sensorimotor network were distinctively located around the lateral pulvinar nucleus but to a limited extent. Prominent connector hubs include the anteroventral, ventral lateral, and mediodorsal nuclei with functional connections to multiple RSNs. These findings suggest that the thalamus, with extensive connections to most of the RSNs, is well placed as a critical integrative functional hub and could play an important role for functional integration facilitating brain functions associated with primary processing and higher cognition. Multiple large-scale cortical networks converged in the thalamus Neurocognitive associated hub existed in the anterior and medial region Control-processing hub localized in the intermediate thalamus Sensorimotor network was located around the lateral pulvinar nucleus
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Affiliation(s)
- Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.,Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan
| | - Epifanio Bagarinao
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Neurology, Fujita Health University School of Medicine, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, Japan
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Daisuke Mori
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Reiko Ohdake
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Neurology, Fujita Health University School of Medicine, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shuji Koyama
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Toshihiko Wakabayashi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masafumi Kuzuya
- Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine and Institutes of Innovation for Future Society, Nagoya University, Nagoya, Aichi, Japan
| | - Minoru Hoshiyama
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Haruo Isoda
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Norio Ozaki
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan.,Aichi Medical University, Nagakute, Aichi, Japan
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36
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Neudorf J, Kress S, Borowsky R. Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity. Brain Struct Funct 2021; 227:331-343. [PMID: 34633514 PMCID: PMC8741721 DOI: 10.1007/s00429-021-02403-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).
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Affiliation(s)
- Josh Neudorf
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Shaylyn Kress
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ron Borowsky
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada.
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37
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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38
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van Houtum LAEM, Wever MCM, Janssen LHC, van Schie CC, Will GJ, Tollenaar MS, Elzinga BM. Vicarious praise and pain: parental neural responses to social feedback about their adolescent child. Soc Cogn Affect Neurosci 2021; 16:406-417. [PMID: 33433604 PMCID: PMC7990067 DOI: 10.1093/scan/nsab004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/01/2020] [Accepted: 01/11/2021] [Indexed: 11/22/2022] Open
Abstract
Social feedback, such as praise or critique, profoundly impacts our mood and social interactions. It is unknown, however, how parents experience praise and critique about their child and whether their mood and neural responses to such ‘vicarious’ social feedback are modulated by parents’ perceptions of their child. Parents (n = 60) received positive, intermediate and negative feedback words (i.e. personality characteristics) about their adolescent child during a magnetic resonance imaging scan. After each word, parents indicated their mood. After positive feedback their mood improved and activity in ventromedial prefrontal cortex and posterior cingulate cortex/precuneus increased. Negative feedback worsened parents’ mood, especially when perceived as inapplicable to their child, and increased activity in anterior cingulate cortex, anterior insula, dorsomedial prefrontal cortex and precuneus. Parents who generally viewed their child more positively showed amplified mood responses to both positive and negative feedback and increased activity in dorsal striatum, inferior frontal gyrus and insula in response to negative feedback. These findings suggest that vicarious feedback has similar effects and engages similar brain regions as observed during feedback about the self and illustrates this is dependent on parents’ beliefs of their child’s qualities and flaws. Potential implications for parent–child dynamics and children’s own self-views are discussed.
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Affiliation(s)
- Lisanne A E M van Houtum
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
| | - Mirjam C M Wever
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
| | - Loes H C Janssen
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
| | - Charlotte C van Schie
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands.,Illawarra Health and Medical Research Institute and School of Psychology, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Geert-Jan Will
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
| | - Marieke S Tollenaar
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
| | - Bernet M Elzinga
- Department of Clinical Psychology, Institute of Psychology, Leiden University, Leiden, South Holland 2300 RB, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, South Holland 2300 RC, The Netherlands
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39
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Chiêm B, Crevecoeur F, Delvenne JC. Structure-informed functional connectivity driven by identifiable and state-specific control regions. Netw Neurosci 2021; 5:591-613. [PMID: 34189379 PMCID: PMC8233121 DOI: 10.1162/netn_a_00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states. Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.
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Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Jean-Charles Delvenne
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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40
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Strindberg M, Fransson P, Cabral J, Ådén U. Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain. Neuroimage 2021; 239:118287. [PMID: 34153450 DOI: 10.1016/j.neuroimage.2021.118287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 06/14/2021] [Accepted: 06/17/2021] [Indexed: 11/30/2022] Open
Abstract
Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.
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Affiliation(s)
- Marika Strindberg
- Department of Women's and Children's health, Karolinska Institutet, Sweden.
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | - Joana Cabral
- Life and health Sciences Research Institute (ICVS), University of Minho, Portugal; Department of Psychiatry, University of Oxford, UK
| | - Ulrika Ådén
- Department of Women's and Children's health, Karolinska Institutet, Sweden
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41
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Zhu W, Huang H, Yang S, Luo X, Zhu W, Xu S, Meng Q, Zuo C, Liu Y, Wang W. Cortical and Subcortical Grey Matter Abnormalities in White Matter Hyperintensities and Subsequent Cognitive Impairment. Neurosci Bull 2021; 37:789-803. [PMID: 33826095 PMCID: PMC8192646 DOI: 10.1007/s12264-021-00657-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/28/2020] [Indexed: 01/18/2023] Open
Abstract
Grey matter (GM) alterations may contribute to cognitive decline in individuals with white matter hyperintensities (WMH) but no consensus has yet emerged. Here, we investigated cortical thickness and grey matter volume in 23 WMH patients with mild cognitive impairment (WMH-MCI), 43 WMH patients without cognitive impairment, and 55 healthy controls. Both WMH groups showed GM atrophy in the bilateral thalamus, fronto-insular cortices, and several parietal-temporal regions, and the WMH-MCI group showed more extensive and severe GM atrophy. The GM atrophy in the thalamus and fronto-insular cortices was associated with cognitive decline in the WMH-MCI patients and may mediate the relationship between WMH and cognition in WMH patients. Furthermore, the main results were well replicated in an independent dataset from the Alzheimer's Disease Neuroimaging Initiative database and in other control analyses. These comprehensive results provide robust evidence of specific GM alterations underlying WMH and subsequent cognitive impairment.
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Affiliation(s)
- Wenhao Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hao Huang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shiqi Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiang Luo
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shabei Xu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qi Meng
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chengchao Zuo
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
- University of the Chinese Academy of Sciences, Beijing, 100049, China.
| | - Wei Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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42
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Lumaca M, Baggio G, Vuust P. White matter variability in auditory callosal pathways contributes to variation in the cultural transmission of auditory symbolic systems. Brain Struct Funct 2021; 226:1943-1959. [PMID: 34050791 DOI: 10.1007/s00429-021-02302-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 05/17/2021] [Indexed: 12/11/2022]
Abstract
The cultural transmission of spoken language and music relies on human capacities for encoding and recalling auditory patterns. In this experiment, we show that interindividual differences in this ability are associated with variation in the organization of cross-callosal white matter pathways. First, high-angular resolution diffusion MRI (dMRI) data were analyzed in a large participant sample (N = 51). Subsequently, these participants underwent a behavioral test that models in the laboratory the cultural transmission of auditory symbolic systems: the signaling game. Cross-callosal and intrahemispheric (arcuate fasciculus) pathways were reconstructed and analyzed using conventional diffusion tensor imaging (DTI) as well as a more advanced dMRI technique: fixel-based analysis (FBA). The DTI metric of fractional anisotropy (FA) in auditory callosal pathways predicted-weeks after scanning-the fidelity of transmission of an artificial tone system. The ability to coherently transmit auditory signals in one signaling game, irrespective of the signals learned during the previous game, was predicted by morphological properties of the fiber bundles in the most anterior portions of the corpus callosum. The current study is the first application of dMRI in the field of cultural transmission, and the first to connect individual characteristics of callosal pathways to core behaviors in the transmission of auditory symbolic systems.
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Affiliation(s)
- Massimo Lumaca
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, 8000, Aarhus C, Denmark.
| | - Giosuè Baggio
- Language Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, 7941, Trondheim, Norway
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, 8000, Aarhus C, Denmark
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43
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Phase fMRI defines brain resting-state functional hubs within central and posterior regions. Brain Struct Funct 2021; 226:1925-1941. [PMID: 34050790 DOI: 10.1007/s00429-021-02301-z] [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: 04/20/2020] [Accepted: 05/12/2021] [Indexed: 10/21/2022]
Abstract
From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our study, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrix calculation (in size of 50 × 50) and FC matrix eigen decomposition; thereby obtained the 50-node eigencentrality values in the eigenvector associated with the largest eigenvalue. We also compared the hub structures inferred from FC matrices under different thresholding. Alternatively, we obtained the geometric hubs among p value the 50 nodes involved in the FC matrix through the use of harmonic centrality metric. Our results showed that phase fMRI data analysis defines the resting-state brain functional hubs primarily in the central region (subcortex) and the posterior region (parieto-occipital lobes and cerebella). The brain central hubness was supported by the geometric central hubness, which, however, is distinct from the magnitude-inferred hubness in brain superior region (frontal and parietal lobes). Our findings pose a new understanding of (or a debate over) brain functional connectivity architecture.
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44
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Levinson M, Podvalny E, Baete SH, He BJ. Cortical and subcortical signatures of conscious object recognition. Nat Commun 2021; 12:2930. [PMID: 34006884 PMCID: PMC8131711 DOI: 10.1038/s41467-021-23266-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/12/2021] [Indexed: 11/29/2022] Open
Abstract
The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition. Cortical and subcortical neural activity supporting conscious object recognition has not yet been well defined. Here, the authors describe these networks and show recognition-related category information can be decoded from widespread cortical activity but not subcortical activity.
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Affiliation(s)
- Max Levinson
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA
| | - Ella Podvalny
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA
| | - Steven H Baete
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA. .,Department of Radiology, New York University School of Medicine, New York, NY, USA. .,Department of Neurology, New York University School of Medicine, New York, NY, USA. .,Department of Neuroscience & Physiology, New York University School of Medicine, New York, NY, USA.
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45
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Corticostriatal Regulation of Language Functions. Neuropsychol Rev 2021; 31:472-494. [PMID: 33982264 DOI: 10.1007/s11065-021-09481-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 01/20/2021] [Indexed: 10/21/2022]
Abstract
The role of corticostriatal circuits in language functions is unclear. In this review, we consider evidence from language learning, syntax, and controlled language production and comprehension tasks that implicate various corticostriatal circuits. Converging evidence from neuroimaging in healthy individuals, studies in populations with subcortical dysfunction, pharmacological studies, and brain stimulation suggests a domain-general regulatory role of corticostriatal systems in language operations. The role of corticostriatal systems in language operations identified in this review is likely to reflect a broader function of the striatum in responding to uncertainty and conflict which demands selection, sequencing, and cognitive control. We argue that this role is dynamic and varies depending on the degree and form of cognitive control required, which in turn will recruit particular corticostriatal circuits and components organised in a cognitive hierarchy.
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46
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Lan M, Peng M, Zhao X, Li H, Yang J. Neural processing of the physical attractiveness stereotype: Ugliness is bad vs. beauty is good. Neuropsychologia 2021; 155:107824. [PMID: 33713669 DOI: 10.1016/j.neuropsychologia.2021.107824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/23/2021] [Accepted: 03/07/2021] [Indexed: 10/22/2022]
Abstract
The physical attractiveness stereotype (PAS) is characterized by the belief that beauty is good and ugliness is bad. Although the belief is not encouraged, people still express "beauty is good" explicitly. However, expressing that ugliness is bad is considered impolite in public. In this study, the neural underpinnings of PAS were investigated, particularly the comparison of the neural processing of ugliness is bad and beauty is good. During functional magnetic resonance imaging scanning, participants were asked to create trait-conformity judgments based on the facial attractiveness of the images. Our results showed that brain areas, including the fusiform, thalamus, anterior insula (AI), precuneus, inferior temporal gyrus, dorsal lateral prefrontal cortex (dlPFC), and dorsal medial prefrontal cortex (dmPFC) were involved in the processing of PAS. The left AI, left dlPFC, and right dmPFC showed stronger activation for ugliness is bad than for the converse. Moreover, we found a stronger connectivity between the left dlPFC and AI in terms of ugliness is bad than in the converse. Our study suggests that participants were unwilling to express the stereotype of ugliness is bad, and more mental resources were needed to control its expression than the expression of beauty is good.
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Affiliation(s)
- Mengxue Lan
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Maoying Peng
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Xiaolin Zhao
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Huixiang Li
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China.
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47
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Mueller JM, Pritschet L, Santander T, Taylor CM, Grafton ST, Jacobs EG, Carlson JM. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle. Netw Neurosci 2021; 5:125-144. [PMID: 33688609 PMCID: PMC7935041 DOI: 10.1162/netn_a_00169] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization. Sex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood whether day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network connectivity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the course of a menstrual cycle, temporary reorganization of several large-scale functional brain networks occurs during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with nodes belonging to the temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.
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Affiliation(s)
- Joshua M Mueller
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Laura Pritschet
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Tyler Santander
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Caitlin M Taylor
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Scott T Grafton
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily Goard Jacobs
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jean M Carlson
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
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48
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Aguilera M, Di Paolo EA. Critical integration in neural and cognitive systems: Beyond power-law scaling as the hallmark of soft assembly. Neurosci Biobehav Rev 2021; 123:230-237. [PMID: 33485887 DOI: 10.1016/j.neubiorev.2021.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/10/2020] [Accepted: 01/07/2021] [Indexed: 11/20/2022]
Abstract
Inspired by models of self-organized criticality, a family of measures quantifies long-range correlations in neural and behavioral activity in the form of self-similar (e.g., power-law scaled) patterns across a range of scales. Long-range correlations are often taken as evidence that a system is near a critical transition, suggesting interaction-dominant, softly assembled relations between its parts. Psychologists and neuroscientists frequently use power-law scaling as evidence of critical regimes and soft assembly in neural and cognitive activity. Critics, however, argue that this methodology operates at most at the level of an analogy between cognitive and other natural phenomena. This is because power-laws do not provide information about a particular system's organization or what makes it specifically cognitive. We respond to this criticism using recent work in Integrated Information Theory. We propose a more principled understanding of criticality as a system's susceptibility to changes in its own integration, a property cognitive agents are expected to manifest. We contrast critical integration with power-law measures and find the former more informative about the underlying processes.
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Affiliation(s)
- Miguel Aguilera
- IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Department of Informatics & Sussex Neuroscience, University of Sussex, Falmer, Brighton, UK; ISAAC Lab, Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
| | - Ezequiel A Di Paolo
- IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Ikerbasque, Basque Foundation for Science, Bizkaia, Spain; Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Falmer, Brighton, UK
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49
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Chard DT, Alahmadi AAS, Audoin B, Charalambous T, Enzinger C, Hulst HE, Rocca MA, Rovira À, Sastre-Garriga J, Schoonheim MM, Tijms B, Tur C, Gandini Wheeler-Kingshott CAM, Wink AM, Ciccarelli O, Barkhof F. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol 2021; 17:173-184. [PMID: 33437067 DOI: 10.1038/s41582-020-00439-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2020] [Indexed: 12/21/2022]
Abstract
MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.
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Affiliation(s)
- Declan T Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. .,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.
| | - Adnan A S Alahmadi
- Department of Diagnostic Radiology, Faculty of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Bertrand Audoin
- Aix-Marseille University, CNRS, CRMBM, Marseille, France.,AP-HM, University Hospital Timone, Department of Neurology, Marseille, France
| | - Thalis Charalambous
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Enzinger
- Department of Neurology, Research Unit for Neuronal Repair and Plasticity, Medical University of Graz, Graz, Austria.,Department of Radiology, Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Hanneke E Hulst
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Betty Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Neurology, Luton and Dunstable University Hospital, Luton, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Alle Meije Wink
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK
| | - Frederik Barkhof
- National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, UK.,Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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50
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Avram M, Brandl F, Knolle F, Cabello J, Leucht C, Scherr M, Mustafa M, Koutsouleris N, Leucht S, Ziegler S, Sorg C. Aberrant striatal dopamine links topographically with cortico-thalamic dysconnectivity in schizophrenia. Brain 2020; 143:3495-3505. [PMID: 33155047 DOI: 10.1093/brain/awaa296] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/30/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023] Open
Abstract
Aberrant dopamine function in the dorsal striatum and aberrant intrinsic functional connectivity (iFC) between distinct cortical networks and thalamic nuclei are among the most consistent large-scale brain imaging findings in schizophrenia. A pathophysiological link between these two alterations is suggested by theoretical models based on striatal dopamine's topographic modulation of cortico-thalamic connectivity within cortico-basal-ganglia-thalamic circuits. We hypothesized that aberrant striatal dopamine links topographically with aberrant cortico-thalamic iFC, i.e. aberrant associative striatum dopamine is associated with aberrant iFC between the salience network and thalamus, and aberrant sensorimotor striatum dopamine with aberrant iFC between the auditory-sensorimotor network and thalamus. Nineteen patients with schizophrenia during remission of psychotic symptoms and 19 age- and sex-comparable control subjects underwent simultaneous fluorodihydroxyphenyl-l-alanine PET (18F-DOPA-PET) and resting state functional MRI (rs-fMRI). The influx constant kicer based on 18F-DOPA-PET was used to measure striatal dopamine synthesis capacity; correlation coefficients between rs-fMRI time series of cortical networks and thalamic regions of interest were used to measure iFC. In the salience network-centred system, patients had reduced associative striatum dopamine synthesis capacity, which correlated positively with decreased salience network-mediodorsal-thalamus iFC. This correlation was present in both patients and healthy controls. In the auditory-sensorimotor network-centred system, patients had reduced sensorimotor striatum dopamine synthesis capacity, which correlated positively with increased auditory-sensorimotor network-ventrolateral-thalamus iFC. This correlation was present in patients only. Results demonstrate that reduced striatal dopamine synthesis capacity links topographically with cortico-thalamic intrinsic dysconnectivity in schizophrenia. Data suggest that aberrant striatal dopamine and cortico-thalamic dysconnectivity are pathophysiologically related within dopamine-modulated cortico-basal ganglia-thalamic circuits in schizophrenia.
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Affiliation(s)
- Mihai Avram
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Felix Brandl
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Franziska Knolle
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jorge Cabello
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Claudia Leucht
- Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Martin Scherr
- Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Mona Mustafa
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry, University Hospital, LMU Munich, Munich, 81377, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AB, UK
| | - Stefan Leucht
- Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Psychosis studies, King's College London, UK
| | - Sibylle Ziegler
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, 81377, Germany
| | - Christian Sorg
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany.,Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, 81675, Germany
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