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Dagnino PC, Escrichs A, López-González A, Gosseries O, Annen J, Sanz Perl Y, Kringelbach ML, Laureys S, Deco G. Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation. PLoS Comput Biol 2024; 20:e1011350. [PMID: 38701063 PMCID: PMC11068192 DOI: 10.1371/journal.pcbi.1011350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
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Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Steven Laureys
- Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University of Laval, Québec, Québec, Canada
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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Notte C, Alionte C, Strubakos CD. The efficacy and methodology of using near-infrared spectroscopy to determine resting-state brain networks. J Neurophysiol 2024; 131:668-677. [PMID: 38416714 DOI: 10.1152/jn.00357.2023] [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: 09/27/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024] Open
Abstract
Functional connectivity is a critical aspect of brain function and is essential for understanding, diagnosing, and treating neurological and psychiatric disorders. It refers to the synchronous activity between different regions of the brain, which gives rise to communication and information processing. Resting-state functional connectivity is a subarea of study that allows researchers to examine brain activity in the absence of a task or stimulus. This can provide insight into the brain's intrinsic functional architecture and help identify neural networks that are active during rest. Thus, determining functional connectivity topography is valuable both clinically and in research. Traditional methods using functional magnetic resonance imaging have proven to be effective, however, they have their limitations. In this review, we investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) as a low-cost, portable alternative for measuring functional connectivity. We first establish fNIRS' ability to detect localized brain activity during task-based experiments. Next, we verify its use in resting-state studies with results showing a high degree of correspondence with resting-state functional magnetic resonance imaging (rs-fMRI). Also discussed are various data-processing methods and the validity of filtering the global signal, which is the current standard for analysis. We consider the possible origins of the global signal, if it contains pertinent neuronal information that could be of importance in better understanding neuronal networks, and what we believe is the best method of approaching signal analysis and regression.
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Affiliation(s)
- Christian Notte
- Department of Physics, University of Windsor, Windsor, Ontario, Canada
| | - Caroline Alionte
- Department of Physics, University of Windsor, Windsor, Ontario, Canada
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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Yang C, Biswal B, Cui Q, Jing X, Ao Y, Wang Y. Frequency-dependent alterations of global signal topography in patients with major depressive disorder. Psychol Med 2024:1-10. [PMID: 38362834 DOI: 10.1017/s0033291724000254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. METHODS We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. RESULTS The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. CONCLUSION The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Bharat Biswal
- 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, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiujuan Jing
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Yujia Ao
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
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Lurie DJ, Pappas I, D'Esposito M. Cortical timescales and the modular organization of structural and functional brain networks. Hum Brain Mapp 2024; 45:e26587. [PMID: 38339903 PMCID: PMC10823764 DOI: 10.1002/hbm.26587] [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: 05/25/2023] [Revised: 12/01/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.
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Affiliation(s)
- Daniel J. Lurie
- Department of PsychologyUniversity of CaliforniaBerkeleyCaliforniaUSA
- Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPennsylvaniaUSA
| | - Ioannis Pappas
- Department of Neurology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mark D'Esposito
- Department of Psychology and Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
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Xie M, Huang Y, Cai W, Zhang B, Huang H, Li Q, Qin P, Han J. Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review. Brain Sci 2024; 14:50. [PMID: 38248265 PMCID: PMC10813043 DOI: 10.3390/brainsci14010050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/26/2023] [Accepted: 12/30/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.
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Affiliation(s)
- Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Ying Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
| | - Wendan Cai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Bingqi Zhang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Haonan Huang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
| | - Qingwei Li
- Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China;
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (M.X.); (Y.H.)
- Pazhou Laboratory, Guangzhou 510330, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; (W.C.); (B.Z.); (H.H.)
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7
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Mihaljevic M, Nagpal A, Etyemez S, Narita Z, Ross A, Schaub R, Cascella NG, Coughlin JM, Nestadt G, Nucifora FC, Sedlak TW, Calhoun VD, Faria AV, Yang K, Sawa A. Neuroimaging alterations and relapse in early-stage psychosis. J Psychiatry Neurosci 2024; 49:E135-E142. [PMID: 38569725 PMCID: PMC10980532 DOI: 10.1503/jpn.230115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/22/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Recent reports have indicated that symptom exacerbation after a period of improvement, referred to as relapse, in early-stage psychosis could result in brain changes and poor disease outcomes. We hypothesized that substantial neuroimaging alterations may exist among patients who experience relapse in early-stage psychosis. METHODS We studied patients with psychosis within 2 years after the first psychotic event and healthy controls. We divided patients into 2 groups, namely those who did not experience relapse between disease onset and the magnetic resonance imaging (MRI) scan (no-relapse group) and those who did experience relapse between these 2 timings (relapse group). We analyzed 3003 functional connectivity estimates between 78 regions of interest (ROIs) derived from resting-state functional MRI data by adjusting for demographic and clinical confounding factors. RESULTS We studied 85 patients, incuding 54 in the relapse group and 31 in the no-relapse group, along with 94 healthy controls. We observed significant differences in 47 functional connectivity estimates between the relapse and control groups after multiple comparison corrections, whereas no differences were found between the no-relapse and control groups. Most of these pathological signatures (64%) involved the thalamus. The Jonckheere-Terpstra test indicated that all 47 functional connectivity changes had a significant cross-group progression from controls to patients in the no-relapse group to patients in the relapse group. LIMITATIONS Longitudinal studies are needed to further validate the involvement and pathological importance of the thalamus in relapse. CONCLUSION We observed pathological differences in neuronal connectivity associated with relapse in early-stage psychosis, which are more specifically associated with the thalamus. Our study implies the importance of considering neurobiological mechanisms associated with relapse in the trajectory of psychotic disorders.
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Affiliation(s)
- Marina Mihaljevic
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Anisha Nagpal
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Semra Etyemez
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Zui Narita
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Anna Ross
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Rebecca Schaub
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Nicola G Cascella
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Jennifer M Coughlin
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Gerald Nestadt
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Frederik C Nucifora
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Thomas W Sedlak
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Vince D Calhoun
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Andreia V Faria
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Kun Yang
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
| | - Akira Sawa
- Departments of Psychiatry (Mihaljevic, Nagpal, Etyemez, Narita, Ross, Schaub, Cascella, Coughlin, Nestadt, Nucifora, Sedlak, Yang, Sawa), Radiology and Radiological Sciences (Faria), Neuroscience (Sawa), Biomedical Engineering (Sawa), Phamarchology (Sawa), and Genetic Medicine (Sawa), Johns Hopkins University School of Medicine; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md. (Sawa); Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Ga. (Calhoun)
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Hirsch F, Wohlschlaeger A. Subcortical influences on the topology of cortical networks align with functional processing hierarchies. Neuroimage 2023; 283:120417. [PMID: 37866758 DOI: 10.1016/j.neuroimage.2023.120417] [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: 04/17/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023] Open
Abstract
fMRI of the human brain reveals spatiotemporal patterns of functional connectivity (FC), forming distinct cortical networks. Lately, subcortical contributions to these configurations are receiving renewed interest, but investigations rarely focus explicitly on their effects on cortico-cortical FC. Here, we employ a straightforward multivariable approach and graph-theoretic tools to assess subcortical impact on topological features of cortical networks. Given recent evidence showing that structures like the thalamus and basal ganglia integrate input from multiple networks, we expect increased segregation between cortical networks after removal of subcortical effects on their FC patterns. We analyze resting state data of young and healthy participants (male and female; N = 100) from the human connectome project. We find that overall, the cortical network architecture becomes less segregated, and more integrated, when subcortical influences are accounted for. Underlying these global effects are the following trends: 'Transmodal' systems become more integrated with the rest of the network, while 'unimodal' networks show the opposite effect. For single nodes this hierarchical organization is reflected by a close correspondence with the spatial layout of the principal gradient of FC (Margulies et al., 2016). Lastly, we show that the limbic system is significantly less coherent with subcortical influences removed. The findings are validated in a (split-sample) replication dataset. Our results provide new insight regarding the interplay between subcortex and cortical networks, by putting the integrative impact of subcortex in the context of macroscale patterns of cortical organization.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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9
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Klar P, Çatal Y, Fogel S, Jocham G, Langner R, Owen AM, Northoff G. Auditory inputs modulate intrinsic neuronal timescales during sleep. Commun Biol 2023; 6:1180. [PMID: 37985812 PMCID: PMC10661171 DOI: 10.1038/s42003-023-05566-8] [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: 08/16/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies have demonstrated that intrinsic neuronal timescales (INT) undergo modulation by external stimulation during consciousness. It remains unclear if INT keep the ability for significant stimulus-induced modulation during primary unconscious states, such as sleep. This fMRI analysis addresses this question via a dataset that comprises an awake resting-state plus rest and stimulus states during sleep. We analyzed INT measured via temporal autocorrelation supported by median frequency (MF) in the frequency-domain. Our results were replicated using a biophysical model. There were two main findings: (1) INT prolonged while MF decreased from the awake resting-state to the N2 resting-state, and (2) INT shortened while MF increased during the auditory stimulus in sleep. The biophysical model supported these results by demonstrating prolonged INT in slowed neuronal populations that simulate the sleep resting-state compared to an awake state. Conversely, under sine wave input simulating the stimulus state during sleep, the model's regions yielded shortened INT that returned to the awake resting-state level. Our results highlight that INT preserve reactivity to stimuli in states of unconsciousness like sleep, enhancing our understanding of unconscious brain dynamics and their reactivity to stimuli.
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Affiliation(s)
- Philipp Klar
- Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Room 6435, Ottawa, ON, K1Z 7K4, Canada
| | - Stuart Fogel
- Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, K1Z 7K4, Ottawa, ON, Canada
| | - Gerhard Jocham
- Faculty of Mathematics and Natural Sciences, Institute of Experimental Psychology, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Adrian M Owen
- Departments of Physiology and Pharmacology and Psychology, Western University, London, ON, N6A 5B7, Canada
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Room 6435, Ottawa, ON, K1Z 7K4, Canada
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Tianmu Road 305, Hangzhou, Zhejiang Province, 310013, China
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10
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Han Y, Yan H, Shan X, Li H, Liu F, Li P, Zhao J, Guo W. Disrupted functional connectivity associated with cognitive impairment in generalized anxiety disorder (GAD) and comorbid GAD and depression: a follow-up fMRI study. J Psychiatry Neurosci 2023; 48:E439-E451. [PMID: 37935477 PMCID: PMC10635709 DOI: 10.1503/jpn.230091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/01/2023] [Accepted: 08/26/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Impaired functional connectivity between the bilateral hemispheres may serve as the neural substrate for anxiety and depressive disorders, yet its role in comorbid generalized anxiety disorder (GAD) and depression, as well as the effect of treatment on this connectivity, remains unclear. We sought to examine functional connectivity between homotopic regions of the 2 hemispheres (voxel-mirrored homotopic connectivity [VMHC]) among people with GAD with and without comorbid depression at baseline and after a 4-week paroxetine treatment. METHODS Drug-naïve patients with GAD, with or without comorbid depression and healthy controls underwent functional magnetic resonance imaging and clinical assessments at baseline and after treatment. We compared VMHC and seed-based functional connectivity across the 3 groups. We performed correlation analysis and support vector regression (SVR) to examine the intrinsic relationships between VMHC and symptoms. RESULTS Both patient groups (n = 40 with GAD only, n = 58 with GAD and depression) showed decreased VMHC in the precuneus, posterior cingulate cortex and lingual gyrus compared with healthy controls (n = 54). Moreover, they showed decreased VMHC in different brain regions compared with healthy controls. However, we did not observe any significant differences between the 2 patient groups. Seeds from abnormal VMHC clusters in patient groups had decreased functional connectivity. Voxel-mirrored homotopic connectivity in the precuneus, posterior cingulate cortex and lingual gyrus was negatively correlated with cognitive impairment among patients with GAD only and among all patients. The SVR analysis based on abnormal VMHC showed significant positive correlations (p < 0.0001) between predicted and actual treatment responses. However, we did not observe significant differences in VMHC or functional connectivity after treatment. LIMITATIONS A notable dropout rate and intergroup somatic symptom variations may have biased the results. CONCLUSION Patients with GAD with or without comorbid depression exhibited shared and distinct abnormal VMHC patterns, which might be linked to their cognitive deficits. These patterns have the potential to serve as prognostic biomarkers for GAD.Clinical trial registration: ClinicalTrials.gov NCT03894085.
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Affiliation(s)
- Yiding Han
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Haohao Yan
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Xiaoxiao Shan
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Huabing Li
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Feng Liu
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Ping Li
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Jingping Zhao
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
| | - Wenbin Guo
- From the Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Han, Yan, Shan, Zhao, Guo); the Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China (H. Li); the Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China (Liu); the Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, China (P. Li)
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11
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Zhang Y, Wang F, Sui J. Decoding individual differences in self-prioritization from the resting-state functional connectome. Neuroimage 2023; 276:120205. [PMID: 37253415 DOI: 10.1016/j.neuroimage.2023.120205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/01/2023] Open
Abstract
Although the self has traditionally been viewed as a higher-order mental function by most theoretical frameworks, recent research advocates a fundamental self hypothesis, viewing the self as a baseline function of the brain embedded within its spontaneous activities, which dynamically regulates cognitive processing and subsequently guides behavior. Understanding this fundamental self hypothesis can reveal where self-biased behaviors emerge and to what extent brain signals at rest can predict such biased behaviors. To test this hypothesis, we investigated the association between spontaneous neural connectivity and robust self-bias in a perceptual matching task using resting-state functional magnetic resonance imaging (fMRI) in 348 young participants. By decoding whole-brain connectivity patterns, the support vector regression model produced the best predictions of the magnitude of self-bias in behavior, which was evaluated via a nested cross-validation procedure. The out-of-sample generalizability was further authenticated using an external dataset of older adults. The functional connectivity results demonstrated that self-biased behavior was associated with distinct connections between the default mode, cognitive control, and salience networks. Consensus network and computational lesion analyses further revealed contributing regions distributed across six networks, extending to additional nodes, such as the thalamus, whose role in self-related processing remained unclear. These results provide evidence that self-biased behavior derives from spontaneous neural connectivity, supporting the fundamental self hypothesis. Thus, we propose an integrated neural network model of this fundamental self that synthesizes previous theoretical models and portrays the brain mechanisms by which the self emerges at rest internally and regulates responses to the external environment.
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Affiliation(s)
- Yongfa Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China; The Centre for Positive Psychology Research, Tsinghua University, Beijing 100084, China.
| | - Jie Sui
- School of Psychology, University of Aberdeen, Aberdeen AB24 3FX, Scotland, Great Britain
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12
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Keskin K, Eker MÇ, Gönül AS, Northoff G. Abnormal global signal topography of self modulates emotion dysregulation in major depressive disorder. Transl Psychiatry 2023; 13:107. [PMID: 37012231 PMCID: PMC10070354 DOI: 10.1038/s41398-023-02398-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/03/2023] [Accepted: 03/15/2023] [Indexed: 04/05/2023] Open
Abstract
Major depressive disorder (MDD) is a complex mental disorder featured by an increased focus on the self and emotion dysregulation whose interaction remains unclear, though. At the same time, various studies observed abnormal representation of global fMRI brain activity in specifically those regions, e.g., cortical midline structure (CMS) in MDD that are associated with the self. Are the self and its impact on emotion regulation related to global brain activity unevenly represented in CMS relative to non-CMS? Addressing this yet open question is the main goal of our study. We here investigate post-acute treatment responder MDD and healthy controls in fMRI during an emotion task involving both attention and reappraisal of negative and neutral stimuli. We first demonstrate abnormal emotion regulation with increased negative emotion severity on the behavioral level. Next, focusing on a recently established three-layer topography of self, we show increased representation of global fMRI brain activity in specifically those regions mediating the mental (CMS) and exteroceptive (Right temporo-parietal junction and mPFC) self in post-acute MDD during the emotion task. Applying a complex statistical model, namely multinomial regression analyses, we show that increased global infra-slow neural activity in the regions of the mental and exteroceptive self modulates the behavioral measures of specifically negative emotion regulation (emotion attention and reappraisal/suppression). Together, we demonstrate increased representation of global brain activity in regions of the mental and exteroceptive self, including their modulation of negative emotion dysregulation in specifically the infra-slow frequency range (0.01 to 0.1 Hz) of post-acute MDD. These findings support the assumption that the global infra-slow neural basis of the increased self-focus in MDD may take on the role as basic disturbance in that it generates the abnormal regulation of negative emotions.
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Affiliation(s)
- Kaan Keskin
- Department of Psychiatry, Ege University, Izmir, Turkey.
- SoCAT Lab, Ege University, Izmir, Turkey.
- Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, ON, Canada.
| | - Mehmet Çağdaş Eker
- Department of Psychiatry, Ege University, Izmir, Turkey
- SoCAT Lab, Ege University, Izmir, Turkey
| | - Ali Saffet Gönül
- Department of Psychiatry, Ege University, Izmir, Turkey
- SoCAT Lab, Ege University, Izmir, Turkey
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, ON, Canada
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13
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Pfurtscheller G, Kaminski M, J Blinowska K, Rassler B, Schwarz G, Klimesch W. Respiration-entrained brain oscillations in healthy fMRI participants with high anxiety. Sci Rep 2023; 13:2380. [PMID: 36765092 PMCID: PMC9918542 DOI: 10.1038/s41598-023-29482-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Brain-body interactions can be studied by using directed coupling measurements of fMRI oscillations in the low (0.1-0.2 Hz) and high frequency bands (HF; 0.2-0.4 Hz). Recently, a preponderance of oscillations in the information flow between the brainstem and the prefrontal cortex at around 0.15/0.16 Hz was shown. The goal of this study was to investigate the information flow between BOLD-, respiratory-, and heart beat-to-beat interval (RRI) signals in the HF band in healthy subjects with high anxiety during fMRI examinations. A multivariate autoregressive model was concurrently applied to the BOLD signals from the middle frontal gyrus (MFG), precentral gyrus and the brainstem, as well as to respiratory and RRI signals. Causal coupling between all signals was determined using the Directed Transfer Function (DTF). We found a salience of fast respiratory waves with a period of 3.1 s (corresponding to ~ 0.32 Hz) and a highly significant (p < 0.001) top-down information-flow from BOLD oscillations in the MFG to the brainstem. Additionally, there was a significant (p < 0.01) information flow from RRI to respiratory oscillations. We speculate that brain oscillations around 0.32 Hz, triggered by nasal breathing, are projected downwards to the brainstem. Particularly interesting is the driving force of cardiac to respiratory waves with a ratio of 1:1 or 1:2. These results support the binary hierarchy model with preferred respiratory frequencies at 0.32 Hz and 0.16 Hz.
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Affiliation(s)
- Gert Pfurtscheller
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Maciej Kaminski
- Faculty of Physics, University of Warsaw, ul. Pasteura 5, 02-093, Warsaw, Poland.
| | - Katarzyna J Blinowska
- Faculty of Physics, University of Warsaw, ul. Pasteura 5, 02-093, Warsaw, Poland.,Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Ks. Trojdena 4 St., 02-109, Warsaw, Poland
| | - Beate Rassler
- Carl-Ludwig-Institute of Physiology, University of Leipzig, Leipzig, Germany
| | - Gerhard Schwarz
- Department of Anaesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, Austria
| | - Wolfgang Klimesch
- Centre of Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
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14
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Towards a systematization of brain oscillatory activity in actions. Commun Biol 2023; 6:137. [PMID: 36732548 PMCID: PMC9894929 DOI: 10.1038/s42003-023-04531-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023] Open
Abstract
Information processing in the brain is governed by oscillatory activity. Activity oscillations in specific frequency bands (theta, alpha, beta and gamma) have been associated with various cognitive functions. A drawback of this is that the plethora of findings led to considerable uncertainty as to the functional relevance of activity in different frequency bands and their interrelation. Here, we use a novel cognitive-science theoretical framework to better understand and conceptually harmonize neurophysiological research on human action control. We outline how this validated starting point can systematize and probably reframe the functional relevance of oscillatory activity relevant for action control and beyond.
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