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Krukow P, Rodríguez-González V, Kopiś-Posiej N, Gómez C, Poza J. Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states. Sci Rep 2024; 14:17442. [PMID: 39075178 PMCID: PMC11286934 DOI: 10.1038/s41598-024-68532-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.
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Affiliation(s)
- Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland.
| | - Victor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Natalia Kopiś-Posiej
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Ul. Głuska 1, 20-439, Lublin, Poland
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Valladolid, Spain
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Wang B, Yuan Y, Yang L, Huang Y, Zhang X, Zhang X, Yan W, Li Y, Li D, Xiang J, Yang J, Liu M. Multi-hierarchy Network Configuration Can Predict Brain States and Performance. J Cogn Neurosci 2024; 36:1695-1714. [PMID: 38579269 DOI: 10.1162/jocn_a_02153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
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Affiliation(s)
- Bin Wang
- Taiyuan University of Technology
| | | | - Lan Yang
- Taiyuan University of Technology
| | | | - Xi Zhang
- Taiyuan University of Technology
| | | | | | - Ying Li
- Taiyuan University of Technology
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Bai YX, Luo JX, Peng D, Sun JJ, Gao YF, Hao LX, Tong BG, He XM, Luo JY, Liang ZH, Yang F. Brain network functional connectivity changes in long illness duration chronic schizophrenia. Front Psychiatry 2024; 15:1423008. [PMID: 38962058 PMCID: PMC11221339 DOI: 10.3389/fpsyt.2024.1423008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/03/2024] [Indexed: 07/05/2024] Open
Abstract
Introduction Chronic schizophrenia has a course of 5 years or more and has a widespread abnormalities in brain functional connectivity. This study aimed to find characteristic functional and structural changes in a long illness duration chronic schizophrenia (10 years or more). Methods Thirty-six patients with a long illness duration chronic schizophrenia and 38 healthy controls were analyzed by independent component analysis of brain network functional connectivity. Correlation analysis with clinical duration was performed on six resting state networks: auditory network, default mode network, dorsal attention network, fronto-parietal network, somatomotor network, and visual network. Results The differences in the resting state network between the two groups revealed that patients exhibited enhanced inter-network connections between default mode network and multiple brain networks, while the inter-network connections between somatomotor network, default mode network and visual network were reduced. In patients, functional connectivity of Cuneus_L was negatively correlated with illness duration. Furthermore, receiver operating characteristic curve of functional connectivity showed that changes in Thalamus_L, Rectus_L, Frontal_Mid_R, and Cerebelum_9_L may indicate a longer illness duration chronic schizophrenia. Discussion In our study, we also confirmed that the course of disease is significantly associated with specific brain regions, and the changes in specific brain regions may indicate that chronic schizophrenia has a course of 10 years or more.
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Affiliation(s)
- Yin Xia Bai
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Jia Xin Luo
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Duo Peng
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Jing Jing Sun
- Department of Psychiatry, Inner Mongolia Mental Health Center, Hohhot, China
- Department of Psychiatry, Inner Mongolia Brain Hospital, Hohhot, China
| | - Yi Fang Gao
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Li Xia Hao
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - B. G. Tong
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Xue Mei He
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Jia Yu Luo
- Department of Rehabilitation, Genghis Khan Community Branch of Inner Mongolia People’s Hospital, Hohhot, China
| | - Zi Hong Liang
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
| | - Fan Yang
- Department of Psychiatry, Inner Mongolia People’s Hospital, Hohhot, China
- Department of Research, Inner Mongolia Academy of Medical Science, Hohhot, China
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Laubacher C, Kral TRA, Imhoff-Smith T, Klaus DR, Goldman RI, Sachs J, Davidson RJ, Busse WW, Rosenkranz MA. Resting state functional connectivity changes following mindfulness-based stress reduction predict improvements in disease control for patients with asthma. Brain Behav Immun 2024; 115:480-493. [PMID: 37924961 PMCID: PMC10842225 DOI: 10.1016/j.bbi.2023.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND The staggering morbidity associated with chronic inflammatory diseases can be reduced by psychological interventions, including Mindfulness-Based Stress Reduction (MBSR). Proposed mechanisms for MBSR's beneficial effects include changes in salience network function. Salience network perturbations are also associated with chronic inflammation, including airway inflammation in asthma, a chronic inflammatory disease affecting approximately 10% of the population. However, no studies have examined whether MBSR-related improvements in disease control are related to changes in salience network function. METHODS Adults with asthma were randomized to 8 weeks of MBSR or a waitlist control group. Resting state functional connectivity was measured using fMRI before randomization, immediately post-intervention, and 4 months post-intervention. Using key salience network regions as seeds, we calculated group differences in change in functional connectivity over time and examined whether functional connectivity changes were associated with increased mindfulness, improved asthma control, and decreased inflammatory biomarkers. RESULTS The MBSR group showed greater increases in functional connectivity between salience network regions relative to the waitlist group. Improvements in asthma control correlated with increased functional connectivity between the salience network and regions important for attention control and emotion regulation. Improvements in inflammatory biomarkers were related to decreased functional connectivity between the salience network and other networks. CONCLUSIONS Increased resting salience network coherence and connectivity with networks that subserve attention and emotion regulation may contribute to the benefits of MBSR for patients with asthma. Understanding the neural underpinnings of MBSR-related benefits in patients is a critical step towards optimizing brain-targeted interventions for chronic inflammatory disease management.
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Affiliation(s)
- Claire Laubacher
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Tammi R A Kral
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA; Healthy Minds Innovations, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Ted Imhoff-Smith
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA
| | - Danika R Klaus
- Healthy Minds Innovations, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Robin I Goldman
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Jane Sachs
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA
| | - Richard J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA; Healthy Minds Innovations, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA; Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
| | - William W Busse
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, 600 Highland Ave, Madison, WI 53792, USA
| | - Melissa A Rosenkranz
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave, Madison, WI 53703, USA; Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd, Madison, WI 53719, USA.
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Hunt KJ, Knight LK, Depue BE. Related neural networks underlie suppression of emotion, memory, motor processes as identified by data-driven analysis. BMC Neurosci 2023; 24:44. [PMID: 37620756 PMCID: PMC10463822 DOI: 10.1186/s12868-023-00812-5] [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: 04/30/2022] [Accepted: 07/14/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Goal-directed behavior benefits from self-regulation of cognitive and affective processes, such as emotional reactivity, memory retrieval, and prepotent motor response. Dysfunction in self-regulation is a common characteristic of many psychiatric disorders, such as PTSD and ADHD. This study sought to determine whether common intrinsic connectivity networks (ICNs; e.g. default mode network) are involved in the regulation of emotion, motor, and memory processes, and if a data-driven approach using independent component analysis (ICA) would successfully identify such ICNs that contribute to inhibitory regulation. METHODS Eighteen participants underwent neuroimaging while completing an emotion regulation (ER) task, a memory suppression (Think/No-Think; TNT) task, and a motor inhibition (Stop Signal; SS) task. ICA (CONN; MATLAB) was conducted on the neuroimaging data from each task and corresponding components were selected across tasks based on interrelated patterns of activation. Subsequently, ICNs were correlated with behavioral performance variables from each task. RESULTS ICA indicated a common medial prefrontal network, striatal network, and frontoparietal executive control network, as well as downregulation in task-specific ROIs. CONCLUSIONS These results illustrate that common ICNs were exhibited across three distinct inhibitory regulation tasks, as successfully identified through a data-driven approach (ICA).
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Affiliation(s)
- Karisa J Hunt
- Department of Psychological and Brain Sciences, University of Louisville, 2301 S, 3rd St., Louisville, KY, 40292, USA.
| | - Lindsay K Knight
- Department of Psychological and Brain Sciences, University of Louisville, 2301 S, 3rd St., Louisville, KY, 40292, USA
- Insightec Ltd., Chicago, IL, USA
| | - Brendan E Depue
- Department of Psychological and Brain Sciences, University of Louisville, 2301 S, 3rd St., Louisville, KY, 40292, USA
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Short-Term Head-Out Whole-Body Cold-Water Immersion Facilitates Positive Affect and Increases Interaction between Large-Scale Brain Networks. BIOLOGY 2023; 12:biology12020211. [PMID: 36829490 PMCID: PMC9953392 DOI: 10.3390/biology12020211] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023]
Abstract
An emerging body of evidence indicates that short-term immersion in cold water facilitates positive affect and reduces negative affect. However, the neural mechanisms underlying these effects remain largely unknown. For the first time, we employed functional magnetic resonance imaging (fMRI) to identify topological clusters of networks coupled with behavioural changes in positive and negative affect after a 5 min cold-water immersion. Perceived changes in positive affect were associated with feeling more active, alert, attentive, proud, and inspired, whilst changes in negative affect reflected reductions in distress and nervousness. The increase in positive affect was supported by a unique component of interacting networks, including the medial prefrontal node of the default mode network, a posterior parietal node of the frontoparietal network, and anterior cingulate and rostral prefrontal parts of the salience network and visual lateral network. This component emerged as a result of a focal effect confined to few connections. Changes in negative affect were associated with a distributed component of interacting networks at a reduced threshold. Affective changes after cold-water immersion occurred independently, supporting the bivalence model of affective processing. Interactions between large-scale networks linked to positive affect indicated the integrative effects of cold-water immersion on brain functioning.
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Riedel P, Lee J, Watson CG, Jimenez AM, Reavis EA, Green MF. Reorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder. Psychiatry Res Neuroimaging 2022; 327:111556. [PMID: 36327867 PMCID: PMC10611423 DOI: 10.1016/j.pscychresns.2022.111556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/13/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Functional connectome organization is altered in schizophrenia (SZ) and bipolar disorder (BD). However, it remains unclear whether network reorganization during a task relative to rest is also altered in these disorders. This study examined connectome organization in patients with SZ (N = 43) and BD (N = 42) versus healthy controls (HC; N = 39) using fMRI data during a visual object-perception task and at rest. Graph analyses were conducted for the whole-brain network using indices selected a priori: three reflecting network segregation (clustering coefficient, local efficiency, modularity), two reflecting integration (characteristic path length, global efficiency). Group differences were limited to network segregation and were more evident in SZ (clustering coefficient, modularity) than in BD (clustering coefficient) compared to HC. State differences were found across groups for segregation (local efficiency) and integration (characteristic path length). There was no group-by-state interaction for any graph index. In summary, aberrant network organization compared to HC was confirmed, and was more evident in SZ than in BD. Yet, reorganization was largely intact in both disorders. These findings help to constrain models of dysconnection in SZ and BD, suggesting that the extent of functional dysconnectivity in these disorders tends to persist across changes in mental state.
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Affiliation(s)
- Philipp Riedel
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Würzburger Straße 35, Dresden 01187, Germany.
| | - Junghee Lee
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Behavioral Neurobiology, School of Medicine, The University of Alabama at Birmingham, SC 560, 1720 2nd Ave S, Birmingham, AL 35294-0017, USA
| | - Christopher G Watson
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Amy M Jimenez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Eric A Reavis
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
| | - Michael F Green
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90024, USA; Desert Pacific Mental Illness Research, Education, and Clinical Center, Greater Los Angeles VA Healthcare System, Bldg. 210, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA
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Kumar VJ, Beckmann CF, Scheffler K, Grodd W. Relay and higher-order thalamic nuclei show an intertwined functional association with cortical-networks. Commun Biol 2022; 5:1187. [PMID: 36333448 PMCID: PMC9636420 DOI: 10.1038/s42003-022-04126-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Almost all functional processing in the cortex strongly depends on thalamic interactions. However, in terms of functional interactions with the cerebral cortex, the human thalamus nuclei still partly constitute a terra incognita. Hence, for a deeper understanding of thalamic-cortical cooperation, it is essential to know how the different thalamic nuclei are associated with cortical networks. The present work examines network-specific connectivity and task-related topical mapping of cortical areas with the thalamus. The study finds that the relay and higher-order thalamic nuclei show an intertwined functional association with different cortical networks. In addition, the study indicates that relay-specific thalamic nuclei are not only involved with relay-specific behavior but also in higher-order functions. The study enriches our understanding of interactions between large-scale cortical networks and the thalamus, which may interest a broader audience in neuroscience and clinical research.
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Affiliation(s)
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Klaus Scheffler
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical MagneticResonance, University Hospital Tübingen, Tübingen, Germany
| | - Wolfgang Grodd
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
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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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Smith AP, Kelly TH, Lile JA, Martin CA, Ramirez MP, Wesley MJ. Exploratory examination of the effects of d-amphetamine on active-state functional connectivity: Influence of impulsivity and sensation-seeking status. Exp Clin Psychopharmacol 2022; 30:194-208. [PMID: 33764102 PMCID: PMC8463640 DOI: 10.1037/pha0000406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Recent advances in diagnostic research identified that individuals with higher impulsivity and sensation-seeking scores tend to report more positive subjective responses to stimulant drugs such as amphetamine. The current exploratory study hypothesized that differences in underlying mesocorticolimbic circuitry may mediate the relationship between personality and responses to stimulants due to its previously established implication in reward processes as well as the overlap between its dopaminergic projections and the pharmacodynamics of many stimulants. Forty participants (20 female) were recruited with relatively high- and low-impulsivity and sensation-seeking scores as defined by the Zuckerman-Kuhlman Personality Questionnaire (Form IIIR; Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993) for a double-blind, placebo-controlled, intranasal amphetamine administration study conducted within an MRI scanner. Active state seed-to-voxel connectivity analyses assessed the effects of amphetamine, personality, subjective responses to amphetamine, and their interactions with mesocorticolimbic seeds on data collected during monetary incentive delay and go/no-go task performance. Results indicated that amphetamine administration largely disrupted brain activity as evidenced by connectivity values shifting toward no correlation among brain stem, striatal, and frontal cortex regions. Additionally, associations of impulsivity and connectivity between ventral tegmental and medial orbitofrontal as well as lateral orbitofrontal and putamen regions were inverted from negative to positive during the placebo and amphetamine conditions, respectively. Personality was unrelated to subjective responses to amphetamine. Results are interpreted as providing evidence of underlying differences in mesocorticolimbic circuitry being a potential target for requisite diagnostic and treatment strategies implicated with stimulant use disorders, but further research is needed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Pervaiz U, Vidaurre D, Gohil C, Smith SM, Woolrich MW. Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations. Med Image Anal 2022; 77:102366. [PMID: 35131700 PMCID: PMC8907871 DOI: 10.1016/j.media.2022.102366] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/29/2021] [Accepted: 01/10/2022] [Indexed: 11/23/2022]
Abstract
The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.
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Affiliation(s)
- Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom.
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom; Department of Clinical Medicine, Aarhus University, Denmark
| | - Chetan Gohil
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
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Yankouskaya A, Sui J. Self-prioritization is supported by interactions between large-scale brain networks. Eur J Neurosci 2022; 55:1244-1261. [PMID: 35083806 PMCID: PMC9303922 DOI: 10.1111/ejn.15612] [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: 08/17/2021] [Revised: 11/29/2021] [Accepted: 01/23/2022] [Indexed: 11/30/2022]
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has provided solid evidence that the default-mode network (DMN) is implicated in self-referential processing. The functional connectivity of the DMN has also been observed in tasks where self-referential processing leads to self-prioritization (SPE) in perception and decision-making. However, we are less certain about whether (i) SPE solely depends on the interplay within parts of the DMN or is driven by multiple brain networks; and (ii) whether SPE is associated with a unique component of interconnected networks or can be explained by related effects such as emotion prioritization. We addressed these questions by identifying and comparing topological clusters of networks involved in self-and emotion prioritization effects generated in an associative-matching task. Using network-based statistics, we found that SPE controlled by emotion is supported by a unique component of interacting networks, including the medial prefrontal part of the DMN (MPFC), Frontoparietal network (FPN) and insular Salience network (SN). This component emerged as a result of a focal effect confined to few connections, indicating that interaction between DMN, FPC and SN is critical to cognitive operations for the SPE. This result was validated on a separate data set. In contrast, prioritization of happy emotion was associated with a component formed by interactions between the rostral prefrontal part of SN, posterior parietal part of FPN and the MPFC, while sad emotion reveals a cluster of the DMN, Dorsal Attention Network (DAN) and Visual Medial Network (VMN). We discussed theoretical and methodological aspects of these findings within the more general domain of social cognition.
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Affiliation(s)
- A Yankouskaya
- Department of Psychology, Bournemouth University, UK
| | - J Sui
- School of Psychology, University of Aberdeen, UK
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13
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Faraj MM, Lipanski NM, Morales A, Goldberg E, Bluth MH, Marusak HA, Greenwald MK. A Virtual Reality Meditative Intervention Modulates Pain and the Pain Neuromatrix in Patients with Opioid Use Disorder. PAIN MEDICINE (MALDEN, MASS.) 2021; 22:2739-2753. [PMID: 33956146 DOI: 10.1093/pm/pnab162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Standard of care for opioid use disorder (OUD) includes medication and counseling. However, there is an unmet need for complementary approaches to treat OUD patients coping with pain; furthermore, few studies have probed neurobiological features of pain or its management during OUD treatment. This preliminary study examines neurobiological and behavioral effects of a virtual reality-based meditative intervention in patients undergoing methadone maintenance treatment (MMT). DESIGN Prospective, non-blinded, single-arm, 12-week intervention with standardized assessments. SETTING Academic research laboratory affiliated with an on-site MMT clinic. METHODS Fifteen (11 female) MMT patients completed a virtual reality, therapist-guided meditative intervention that included breathing and relaxation exercisessessions were scheduled twice weekly. Assessments included functional magnetic resonance imaging (fMRI) of pain neuromatrix activation and connectivity (pre- and post-intervention), saliva cortisol and C-reactive protein (CRP) at baseline and weeks 4, 8 and 12; and self-reported pain and affective symptoms before and after each intervention session. RESULTS After each intervention session (relative to pre-session), ratings of pain, opioid craving, anxiety and depression (but not anger) decreased. Saliva cortisol (but not CRP) levels decreased from pre- to post-session. From pre- to post-intervention fMRI assessments, pain task-related left postcentral gyrus (PCG) activation decreased. At baseline, PCG showed positive connectivity with other regions of the pain neuromatrix, but this pattern changed post-intervention. CONCLUSIONS These preliminary findings demonstrate feasibility, therapeutic promise, and brain basis of a meditative intervention for OUD patients undergoing MMT.
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Affiliation(s)
| | - Nina M Lipanski
- Department of Biological Sciences, University of California, San Diego
| | - Austin Morales
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University
| | - Elimelech Goldberg
- School of Medicine, Wayne State University, Detroit, Michigan
- Kids Kicking Cancer
| | - Martin H Bluth
- School of Medicine, Wayne State University, Detroit, Michigan
- Kids Kicking Cancer
- Maimonides Medical Center, Brooklyn, New York, USA
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University
- Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University
| | - Mark K Greenwald
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University
- Department of Pharmacy Practice, Wayne State University
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14
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Kieliba P, Clode D, Maimon-Mor RO, Makin TR. Robotic hand augmentation drives changes in neural body representation. Sci Robot 2021; 6:eabd7935. [PMID: 34043536 PMCID: PMC7612043 DOI: 10.1126/scirobotics.abd7935] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/23/2021] [Indexed: 01/11/2023]
Abstract
Humans have long been fascinated by the opportunities afforded through augmentation. This vision not only depends on technological innovations but also critically relies on our brain's ability to learn, adapt, and interface with augmentation devices. Here, we investigated whether successful motor augmentation with an extra robotic thumb can be achieved and what its implications are on the neural representation and function of the biological hand. Able-bodied participants were trained to use an extra robotic thumb (called the Third Thumb) over 5 days, including both lab-based and unstructured daily use. We challenged participants to complete normally bimanual tasks using only the augmented hand and examined their ability to develop hand-robot interactions. Participants were tested on a variety of behavioral and brain imaging tests, designed to interrogate the augmented hand's representation before and after the training. Training improved Third Thumb motor control, dexterity, and hand-robot coordination, even when cognitive load was increased or when vision was occluded. It also resulted in increased sense of embodiment over the Third Thumb. Consequently, augmentation influenced key aspects of hand representation and motor control. Third Thumb usage weakened natural kinematic synergies of the biological hand. Furthermore, brain decoding revealed a mild collapse of the augmented hand's motor representation after training, even while the Third Thumb was not worn. Together, our findings demonstrate that motor augmentation can be readily achieved, with potential for flexible use, reduced cognitive reliance, and increased sense of embodiment. Yet, augmentation may incur changes to the biological hand representation. Such neurocognitive consequences are crucial for successful implementation of future augmentation technologies.
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Affiliation(s)
- Paulina Kieliba
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK
| | - Danielle Clode
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK
- Dani Clode design, 40 Hillside Road, London SW2 3HW, UK
| | - Roni O Maimon-Mor
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK
- WIN Centre, University of Oxford, Oxford OX3 9DU, UK
| | - Tamar R Makin
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AZ, UK.
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3AR, UK
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15
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Bijsterbosch J, Harrison SJ, Jbabdi S, Woolrich M, Beckmann C, Smith S, Duff EP. Challenges and future directions for representations of functional brain organization. Nat Neurosci 2020; 23:1484-1495. [PMID: 33106677 DOI: 10.1038/s41593-020-00726-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022]
Abstract
A key principle of brain organization is the functional integration of brain regions into interconnected networks. Functional MRI scans acquired at rest offer insights into functional integration via patterns of coherent fluctuations in spontaneous activity, known as functional connectivity. These patterns have been studied intensively and have been linked to cognition and disease. However, the field is fractionated. Diverging analysis approaches have segregated the community into research silos, limiting the replication and clinical translation of findings. A primary source of this fractionation is the diversity of approaches used to reduce complex brain data into a lower-dimensional set of features for analysis and interpretation, which we refer to as brain representations. In this Primer, we provide an overview of different brain representations, lay out the challenges that have led to the fractionation of the field and that continue to form obstacles for convergence, and propose concrete guidelines to unite the field.
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Affiliation(s)
- Janine Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University in St Louis, Saint Louis, MO, USA. .,Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK.
| | - Samuel J Harrison
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK.,Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Saad Jbabdi
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Christian Beckmann
- Donders Institute and Department of Cognitive Neurosciences, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Stephen Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK. .,Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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16
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Greene AS, Gao S, Noble S, Scheinost D, Constable RT. How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships. Cell Rep 2020; 32:108066. [PMID: 32846124 PMCID: PMC7469925 DOI: 10.1016/j.celrep.2020.108066] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 05/27/2020] [Accepted: 08/04/2020] [Indexed: 01/21/2023] Open
Abstract
Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing seven tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. To further characterize these predictive FC changes, we develop and apply an inter-subject PPI analysis. We find that moderate, but not high, task-induced consistency of the blood-oxygen-level-dependent (BOLD) signal across individuals is useful for prediction. Together, these findings demonstrate that in-scanner tasks have distributed, phenotypically relevant effects on brain functional organization, and they offer a framework to leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD/PhD Program, Yale School of Medicine, New Haven, CT, USA.
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; The Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
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17
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Harrison SJ, Bijsterbosch JD, Segerdahl AR, Fitzgibbon SP, Farahibozorg SR, Duff EP, Smith SM, Woolrich MW. Modelling subject variability in the spatial and temporal characteristics of functional modes. Neuroimage 2020; 222:117226. [PMID: 32771617 PMCID: PMC7779373 DOI: 10.1016/j.neuroimage.2020.117226] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 06/26/2020] [Accepted: 07/30/2020] [Indexed: 11/19/2022] Open
Abstract
Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
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Affiliation(s)
- Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland.
| | - Janine D Bijsterbosch
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Department of Radiology, Washington University Medical School, Saint Louis, USA
| | - Andrew R Segerdahl
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sean P Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Eugene P Duff
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UK
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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18
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Arora A, Pletzer B, Aichhorn M, Perner J. What's in a Hub?-Representing Identity in Language and Mathematics. Neuroscience 2020; 432:104-114. [PMID: 32112913 PMCID: PMC7100012 DOI: 10.1016/j.neuroscience.2020.02.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 11/17/2022]
Abstract
Hubs emerge in structural and resting state network analysis as areas highly connected to other parts of the brain and have been shown to respond to several task domains in functional imaging studies. A cognitive explanation for this multi-functionality is still wanting. We propose, that hubs subserve domain-general meta-cognitive functions, relevant to a variety of domain-specific networks and test this hypothesis for the example of processing explicit identity information. To isolate this meta-cognitive function from the processing of domain-specific context, we investigate the overlapping activations to linguistic identity processes (e.g. Mr. Dietrich is the dentist) on the one hand and numerical identity processes (e.g. do "3 × 8" and "36-12" give the same number) on the other hand. The main question was, whether these overlapping activations would fall within areas, consistently identified as hubs by network-based analyses. Indeed, the two contrasts showed significant conjunctions in the left inferior parietal lobe (IPL), precuneus (PC), and posterior cingulate. Accordingly, identity processing may well be one domain-general meta-cognitive function that hub-areas provide to domain-specific networks. For the parietal lobe we back up our hypothesis further with existing reports of activation peaks for other tasks that depend on identity processing, e.g., episodic recollection, theory of mind, and visual perspective taking.
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Affiliation(s)
- Aditi Arora
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Belinda Pletzer
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Markus Aichhorn
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Josef Perner
- Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria.
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19
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Gabitov E, Lungu O, Albouy G, Doyon J. Weaker Inter-hemispheric and Local Functional Connectivity of the Somatomotor Cortex During a Motor Skill Acquisition Is Associated With Better Learning. Front Neurol 2019; 10:1242. [PMID: 31827459 PMCID: PMC6890719 DOI: 10.3389/fneur.2019.01242] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 11/07/2019] [Indexed: 12/20/2022] Open
Abstract
Recently, an increasing interest in investigating interactions between brain regions using functional connectivity (FC) methods has shifted the initial focus of cognitive neuroimaging research from localizing functional circuits based on task activation to mapping brain networks based on intrinsic FC dynamics. Leveraging the advantages of the latter approach, it has been shown that despite primarily invariant intrinsic organization of the large-scale functional networks, interactions between and within these networks significantly differ between various behavioral and cognitive states. These differences presumably indicate transient reconfiguration of functional connections-an instantaneous process that flexibly mediates and calibrates human behavior according to momentary demands of the environment. Nevertheless, the specificity of these reconfigured FC patterns to the task at hand and their relevance to adaptive processes during learning remain elusive. To address this knowledge gap, we investigated (1) to what extent FC within the somatomotor network is reconfigured during motor skill practice, and (2) how these changes are related to learning. We applied a seed-driven FC approach to data collected during a continuous task-free condition, so-called resting state, and during a motor sequence learning task using functional magnetic resonance imaging. During the task, participants repeatedly performed a short five-element sequence with their non-dominant (left) hand. As predicted, such unimanual sequence production was associated with lateralized activation of the right somatomotor cortex (SMC). Using this "active" region as a seed, here we show that unimanual performance of the motor sequence relies on functional segregation between the two SMC and selective integration between the "active" SMC and supplementary motor area. Whereas, greater segregation between the two SMC was associated with gains in performance rate, greater segregation within the "active" SMC itself was associated with more consistent performance by the end of training. Nether the resting-state FC patterns within the somatomotor network nor their relative modulation by the task state predicted these behavioral benefits of learning. Our results suggest that task-induced FC changes reflect reconfiguration of the connectivity patterns within the somatomotor network rather than a simple amplification or silencing of its intrinsic dynamics. Such reconfiguration not only supports motor behavior but may also predict learning.
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Affiliation(s)
- Ella Gabitov
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Ovidiu Lungu
- Functional Neuroimaging Unit, Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC, Canada.,Département de Psychiatrie et d'Addictologie, Université de Montréal, Montreal, QC, Canada
| | - Geneviève Albouy
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Julien Doyon
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
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