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Cahart MS, Giampietro V, Naysmith L, Muraz M, Zelaya F, Williams SCR, O'Daly O. Anhedonia severity mediates the relationship between attentional networks recruitment and emotional blunting during music listening. Sci Rep 2024; 14:20040. [PMID: 39198531 PMCID: PMC11358146 DOI: 10.1038/s41598-024-70293-x] [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: 01/17/2024] [Accepted: 08/14/2024] [Indexed: 09/01/2024] Open
Abstract
Emotion studies have commonly reported impaired emotional processing in individuals with heightened anhedonic depressive symptoms, as typically measured by collecting single subjective ratings for a given emotional cue. However, the interindividual variation in moment-to-moment emotional reactivity, and associated time-varying brain networks recruitment as emotions are unfolding, remains unclear. In this study, we filled this gap by using the unique temporal characteristics of music to investigate behavioural and brain network dynamics as a function of anhedonic depressive symptoms severity. Thirty-one neurotypical participants aged 18-30 years completed anhedonic depression questionnaires and then continuously rated happy, neutral and sad pieces of music whilst undergoing MRI scanning. Using a unique combination of dynamic approaches to behavioural (i.e., emotion dynamics) and fMRI (i.e., leading eigenvector dynamics analysis; LEiDA) data analysis, we found that participants higher in anhedonic depressive symptoms exhibited increased recruitment of attentional networks and blunted emotional response to both happy and sad musical excerpts. Anhedonic depression mediated the relationship between attentional networks recruitment and emotional blunting, and the elevated recruitment of attentional networks during emotional pieces of music carried over into subsequent neutral music. Future studies are needed to investigate whether these findings could be generalised to a clinical population (i.e., major depressive disorder).
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Affiliation(s)
- Marie-Stephanie Cahart
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK.
| | - Vincent Giampietro
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Laura Naysmith
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Mathilde Muraz
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Fernando Zelaya
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Steven C R Williams
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Owen O'Daly
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, 16 De Crespigny Park, London, SE5 8AB, UK
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2
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Martino M, Magioncalda P. A three-dimensional model of neural activity and phenomenal-behavioral patterns. Mol Psychiatry 2024; 29:639-652. [PMID: 38114633 DOI: 10.1038/s41380-023-02356-w] [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: 07/18/2023] [Revised: 11/16/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
How phenomenal experience and behavior are related to neural activity in physiology and psychopathology represents a fundamental question in neuroscience and psychiatry. The phenomenal-behavior patterns may be deconstructed into basic dimensions, i.e., psychomotricity, affectivity, and thought, which might have distinct neural correlates. This work provides a data overview on the relationship of these phenomenal-behavioral dimensions with brain activity across physiological and pathological conditions (including major depressive disorder, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, anxiety disorders, addictive disorders, Parkinson's disease, Tourette syndrome, Alzheimer's disease, and frontotemporal dementia). Accordingly, we propose a three-dimensional model of neural activity and phenomenal-behavioral patterns. In this model, neural activity is organized into distinct units in accordance with connectivity patterns and related input/output processing, manifesting in the different phenomenal-behavioral dimensions. (1) An external neural unit, which involves the sensorimotor circuit/brain's sensorimotor network and is connected with the external environment, processes external inputs/outputs, manifesting in the psychomotor dimension (processing of exteroception/somatomotor activity). External unit hyperactivity manifests in psychomotor excitation (hyperactivity/hyperkinesia/catatonia), while external unit hypoactivity manifests in psychomotor inhibition (retardation/hypokinesia/catatonia). (2) An internal neural unit, which involves the interoceptive-autonomic circuit/brain's salience network and is connected with the internal/body environment, processes internal inputs/outputs, manifesting in the affective dimension (processing of interoception/autonomic activity). Internal unit hyperactivity manifests in affective excitation (anxiety/dysphoria-euphoria/panic), while internal unit hypoactivity manifests in affective inhibition (anhedonia/apathy/depersonalization). (3) An associative neural unit, which involves the brain's associative areas/default-mode network and is connected with the external/internal units (but not with the environment), processes associative inputs/outputs, manifesting in the thought dimension (processing of ideas). Associative unit hyperactivity manifests in thought excitation (mind-wandering/repetitive thinking/psychosis), while associative unit hypoactivity manifests in thought inhibition (inattention/cognitive deficit/consciousness loss). Finally, these neural units interplay and dynamically combine into various neural states, resulting in the complex phenomenal experience and behavior across physiology and neuropsychiatric disorders.
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Affiliation(s)
- Matteo Martino
- Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
| | - Paola Magioncalda
- Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- Department of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
- Department of Medical Research, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
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3
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An Z, Tang K, Xie Y, Tong C, Liu J, Tao Q, Feng Y. Aberrant resting-state co-activation network dynamics in major depressive disorder. Transl Psychiatry 2024; 14:1. [PMID: 38172115 PMCID: PMC10764934 DOI: 10.1038/s41398-023-02722-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/04/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.
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Affiliation(s)
- Ziqi An
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Kai Tang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Chuanjun Tong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jiaming Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Quan Tao
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangdong Province Key Laboratory of Psychiatric Disorders, Department of Neurobiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.
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Ferber SG, Weller A, Soreq H. Boltzmann's Theorem Revisited: Inaccurate Time-to-Action Clocks in Affective Disorders. Curr Neuropharmacol 2024; 22:1762-1777. [PMID: 38500272 PMCID: PMC11284727 DOI: 10.2174/1570159x22666240315100326] [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: 05/30/2023] [Revised: 12/14/2023] [Accepted: 12/17/2023] [Indexed: 03/20/2024] Open
Abstract
Timely goal-oriented behavior is essential for survival and is shaped by experience. In this paper, a multileveled approach was employed, ranging from the polymorphic level through thermodynamic molecular, cellular, intracellular, extracellular, non-neuronal organelles and electrophysiological waves, attesting for signal variability. By adopting Boltzmann's theorem as a thermodynamic conceptualization of brain work, we found deviations from excitation-inhibition balance and wave decoupling, leading to wider signal variability in affective disorders compared to healthy individuals. Recent evidence shows that the overriding on-off design of clock genes paces the accuracy of the multilevel parallel sequencing clocks and that the accuracy of the time-to-action is more crucial for healthy behavioral reactions than their rapidity or delays. In affective disorders, the multilevel clocks run free and lack accuracy of responsivity to environmentally triggered time-to-action as the clock genes are not able to rescue mitochondria organelles from oxidative stress to produce environmentally-triggered energy that is required for the accurate time-to-action and maintenance of the thermodynamic equilibrium. This maintenance, in turn, is dependent on clock gene transcription of electron transporters, leading to higher signal variability and less signal accuracy in affective disorders. From a Boltzmannian thermodynamic and energy-production perspective, the option of reversibility to a healthier time-toaction, reducing entropy is implied. We employed logic gates to show deviations from healthy levelwise communication and the reversed conditions through compensations implying the role of nonneural cells and the extracellular matrix in return to excitation-inhibition balance and accuracy in the time-to-action signaling.
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Affiliation(s)
- Sari Goldstein Ferber
- Psychology Department and The Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Aron Weller
- Psychology Department and The Gonda Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Hermona Soreq
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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Zhu Y, Huang T, Li R, Yang Q, Zhao C, Yang M, Lin B, Li X. Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium. Front Neurosci 2023; 17:1308551. [PMID: 38148946 PMCID: PMC10750394 DOI: 10.3389/fnins.2023.1308551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/24/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Previous studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD). Methods This study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups. Results Our findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group. Conclusion These findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.
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Affiliation(s)
- Yao Zhu
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Tianming Huang
- Department of General Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China
| | - Ruolin Li
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Qianrong Yang
- Department of General Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China
| | - Chaoyue Zhao
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ming Yang
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Bin Lin
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Xuzhou Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Ogura Y, Wakatsuki Y, Hashimoto N, Miyamoto T, Nakai Y, Toyomaki A, Tsuchida Y, Nakagawa S, Inoue T, Kusumi I. Hyperthymic temperament predicts neural responsiveness for nonmonetary reward. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2023; 2:e140. [PMID: 38867834 PMCID: PMC11114308 DOI: 10.1002/pcn5.140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 06/14/2024]
Abstract
Aim Hyperthymic temperament is a cheerful action orientation that is suggested to have a protective effect on depressive symptoms. We recently reported that hyperthymic temperament can positively predict activation of reward-related brain areas in anticipation of monetary rewards, which could serve as a biomarker of hyperthymic temperament. However, the relationship between hyperthymic temperament and neural responsiveness to nonmonetary rewards (i.e., feedback indicating success in a task) remains unclear. Methods Healthy participants performed a modified monetary incentive delay task inside a functional magnetic resonance imaging scanner. To examine the effect of nonmonetary positive feedback, the participants performed feedback and no-feedback trials. We explored brain regions whose neural responsiveness to nonmonetary rewards was predicted by hyperthymic temperament. Results There was premotor area activation in anticipation of a nonmonetary reward, which was negatively predicted by hyperthymic temperament. Moreover, brain areas located mainly in the primary somatosensory area and somatosensory association area were activated by performance feedback, which was positively predicted by hyperthymic temperament. Conclusion We found that hyperthymic temperament is related to neural responsiveness to both monetary and nonmonetary rewards. This may be related to the process of affective regulation in the somatosensory area.
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Affiliation(s)
- Yukiko Ogura
- Center for Experimental Research in Social SciencesHokkaido UniversitySapporoJapan
| | - Yumi Wakatsuki
- Department of PsychiatryThe Hokkaido Medical CenterSapporoJapan
| | - Naoki Hashimoto
- Department of PsychiatryHokkaido University Graduate School of MedicineSapporoJapan
| | - Tamaki Miyamoto
- Department of PsychiatryHokkaido University Graduate School of MedicineSapporoJapan
| | | | - Atsuhito Toyomaki
- Department of PsychiatryHokkaido University Graduate School of MedicineSapporoJapan
| | - Yukio Tsuchida
- School of EducationOsaka University of Health and Sport SciencesOsakaJapan
| | - Shin Nakagawa
- Division of Neuropsychiatry, Department of NeuroscienceYamaguchi University Graduate School of MedicineYamaguchiJapan
| | - Takeshi Inoue
- Department of PsychiatryTokyo Medical UniversityTokyoJapan
| | - Ichiro Kusumi
- Department of PsychiatryHokkaido University Graduate School of MedicineSapporoJapan
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7
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Wade B, Barbour T, Ellard K, Camprodon J. Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity. RESEARCH SQUARE 2023:rs.3.rs-3204245. [PMID: 37609235 PMCID: PMC10441516 DOI: 10.21203/rs.3.rs-3204245/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.
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8
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Cîrstian R, Pilmeyer J, Bernas A, Jansen JFA, Breeuwer M, Aldenkamp AP, Zinger S. Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI. J Neuroimaging 2023; 33:404-414. [PMID: 36710075 DOI: 10.1111/jon.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND PURPOSE The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.
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Affiliation(s)
- Ramona Cîrstian
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Antoine Bernas
- Department of Biophysics, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
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9
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Li N, Jin D, Wei J, Huang Y, Xu J. Functional brain abnormalities in major depressive disorder using a multiscale community detection approach. Neuroscience 2022; 501:1-10. [PMID: 35964834 DOI: 10.1016/j.neuroscience.2022.08.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022]
Abstract
Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.
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Affiliation(s)
- Na Li
- Tianjin Key Lab of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Di Jin
- Tianjin Key Lab of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jianguo Wei
- Tianjin Key Lab of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Yuxiao Huang
- Columbian College of Arts & Sciences, George Washington University, Washington D.C., USA
| | - Junhai Xu
- Tianjin Key Lab of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China.
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10
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Lan Z, Zhang W, Wang D, Tan Z, Wang Y, Pan C, Xiao Y, Kuai C, Xue SW. Decreased modular segregation of the frontal-parietal network in major depressive disorder. Front Psychiatry 2022; 13:929812. [PMID: 35935436 PMCID: PMC9353222 DOI: 10.3389/fpsyt.2022.929812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant large-scale distributed brain networks. However, it is unclear how the network dysfunction in MDD patients is characterized by imbalance or derangement of network modular segregation. Fifty-one MDD patients and forty-three matched healthy controls (HC) were recruited in the present study. We analyzed intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI) and then examined brain network segregation by computing the participation coefficient (PC). Further intra- and inter-modular connections analysis were preformed to explain atypical PC. Besides, we explored the potential relationship between the above graph theory measures and symptom severity in MDD. Lower modular segregation of the frontal-parietal network (FPN) was found in MDD compared with the HC group. The MDD group exhibited increased inter-module connections between the FPN and cingulo-opercular network (CON), between the FPN and cerebellum (Cere), between the CON and Cere. At the nodal level, the PC of the anterior prefrontal cortex, anterior cingulate cortex, inferior parietal lobule (IPL), and intraparietal sulcus showed larger in MDD. Additionally, the inter-module connections between the FPN and CON and the PC values of the IPL were negatively correlated with depression symptom in the MDD group. These findings might give evidence about abnormal FPN in MDD from the perspective of modular segregation in brain networks.
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Affiliation(s)
- Zhihui Lan
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Wei Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Zhonglin Tan
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Chenyuan Pan
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Yang Xiao
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
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11
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Tozlu C, Jamison K, Gauthier SA, Kuceyeski A. Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis. Front Neurosci 2021; 15:763966. [PMID: 34966255 PMCID: PMC8710545 DOI: 10.3389/fnins.2021.763966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/17/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Susan A. Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York, NY, United States
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
- *Correspondence: Amy Kuceyeski
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12
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Geller WN, Liu K, Warren SL. Specificity of anhedonic alterations in resting-state network connectivity and structure: A transdiagnostic approach. Psychiatry Res Neuroimaging 2021; 317:111349. [PMID: 34399282 DOI: 10.1016/j.pscychresns.2021.111349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/11/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Anhedonia is a prominent characteristic of depression and related pathology that is associated with a prolonged course of mood disturbance and treatment resistance. However, the neurobiological mechanisms of anhedonia are poorly understood as few studies have disentangled the specific effects of anhedonia from other co-occurring symptoms. Here, we take a transdiagnostic, dimensional approach to distinguish anhedonia alterations from other internalizing symptoms on intrinsic functional brain circuits. 53 adults with varying degrees of anxiety and/or depression completed resting-state fMRI. Neural networks were identified through independent components analysis. Dual regression was used to characterize within-network functional connectivity alterations associated with individual differences in anhedonia. Modulation of between-network functional connectivity by anhedonia was tested using region-of-interest to region-of-interest correlational analyses. Anhedonia was associated with visual network hyperconnectivity and expansion of the visual, dorsal attention, and default networks. Additionally, anhedonia was associated with decreased between-network connectivity among default, salience, dorsal attention, somatomotor, and visual networks. Findings suggest that anhedonia is associated with aberrant connectivity and structural alterations in resting-state networks that contribute to impairments in reward learning, low motivation, and negativity bias characteristic of depression. Results reveal dissociable effects of anhedonia on resting-state network dynamics, characterizing possible neurocircuit mechanisms for intervention.
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Affiliation(s)
- Whitney N Geller
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA
| | - Kevin Liu
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA
| | - Stacie L Warren
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA.
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13
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Gupta A, Wolff A, Northoff DG. Extending the "resting state hypothesis of depression" - dynamics and topography of abnormal rest-task modulation. Psychiatry Res Neuroimaging 2021; 317:111367. [PMID: 34555652 DOI: 10.1016/j.pscychresns.2021.111367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/21/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
Major depressive disorder (MDD) is characterized by changes in both rest and task states as manifested in temporal dynamics (EEG) and spatial patterns (fMRI). Are rest and task changes related to each other? Extending the "Resting state hypothesis of depression" (RSHD) (Northoff et al., 2011), we, using multimodal imaging, take a tripartite approach: (i) we conduct a review of EEG studies in MDD combining both rest and task states; (ii) we present our own EEG data in MDD on brain dynamics, i.e., intrinsic neural timescales as measured by the autocorrelation window (ACW); and (iii) we review fMRI studies in MDD to probe whether different regions exhibit different rest-task modulation. Review of EEG data shows reduced rest-task change in MDD in different measures of temporal dynamics like peak frequency (and others). Notably, our own EEG data show decreased rest-task change as measured by ACW in frontal electrodes of MDD. The fMRI data reveal that different regions exhibit different rest-task relationships (normal rest-abnormal task, abnormal rest-normal task, abnormal rest-abnormal task) in MDD. Together, we demonstrate altered spatiotemporal dynamics of rest-task modulation in MDD; this further supports and extends the key role of the spontaneous activity in MDD as proposed by the RSHD.
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Affiliation(s)
- Anvita Gupta
- Mind, Brain Imaging and Neuroethics, University of Ottawa Institute of Mental Health Research, Ottawa, Canada; Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada
| | - Annemarie Wolff
- Mind, Brain Imaging and Neuroethics, University of Ottawa Institute of Mental Health Research, Ottawa, Canada
| | - Dr Georg Northoff
- Mind, Brain Imaging and Neuroethics, University of Ottawa Institute of Mental Health Research, Ottawa, Canada; Mental Health Center, 7th hospital, Zhejiang University School of Medicine, 7th hospital, Hangzhou, Zhejiang, China; Centre for Research Ethics & Bioethics, University of Uppsala, Uppsala, Sweden.
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14
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Simard I, Denomme WJ, Shane MS. Altered power spectra in antisocial males during rest as a function of cocaine dependence: A network analysis. Psychiatry Res Neuroimaging 2021; 309:111235. [PMID: 33484936 PMCID: PMC7904621 DOI: 10.1016/j.pscychresns.2020.111235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 12/02/2020] [Accepted: 12/10/2020] [Indexed: 11/20/2022]
Abstract
Abnormalities in the spectral power of offenders' neural oscillations have been noted within select Resting-State Networks (RSNs); however, no study has yet evaluated the influence of cocaine dependence, drug use severity, and psychopathic traits on these abnormalities. To this end, the present study compared rest-related power spectral characteristics between two groups of offenders (with and without a DSM-IV-TR cocaine-dependence diagnosis) and a non-offender control group. Results indicated that both offender groups presented with lower low frequency power ratio (LFPR) scores (i.e. across all RSNs) than non-offenders. These differences in LFPR scores were due to both higher high-frequency power (0.15-0.25 Hz; within seven (in non-dependent offenders) and five (in cocaine-dependent offenders) of eight investigated networks) and decreased low-frequency power (0.01-0.10 Hz; within six (in non-dependent offenders) and one (in cocaine-dependent offenders) of eight investigated networks) compared to non-offenders. Thus, both cocaine-dependent and non-dependent offenders displayed abnormal neural oscillations, suggesting that these oscillatory abnormalities could exist as neurobiological features associated with offender status. Offenders' LFPR levels correlated with lifetime years of cocaine use, but not with the level of psychopathic traits. These findings supplement our knowledge regarding the influence of substance use on resting-state activity in offenders; moreover, they provide further indication of the importance of evaluating shared/unique variance associated with drug use and pyschopathic personality traits.
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Affiliation(s)
- Isabelle Simard
- University of Ontario Institute of Technology, Faculty of Social Science and Humanities, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada.
| | - William J Denomme
- University of Ontario Institute of Technology, Faculty of Social Science and Humanities, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada.
| | - Matthew S Shane
- University of Ontario Institute of Technology, Faculty of Social Science and Humanities, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada.
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15
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Kakanakova A, Popov S, Maes M. Immunological Disturbances and Neuroimaging Findings in Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD) Comorbid Patients. Curr Top Med Chem 2021; 20:759-769. [PMID: 32108009 DOI: 10.2174/1568026620666200228093935] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 11/17/2019] [Accepted: 12/02/2019] [Indexed: 01/02/2023]
Abstract
Mood disorders and Major Depressive Disorder, in particular, appear to be some of the most common psychiatric disorders with a high rate of comorbidity most frequently of anxiety or substance abuse disorders (alcohol use disorder). In both cases - MDD and AUD, a number of immunological disturbances are observed, such as chronic mild inflammation response, increased level of cytokines, hypercortisolaemia, which lead to specific changes in brain neurotransmitter functions. Some of the contemporary brain imaging techniques are functional magnetic resonance imaging (fMRI) and magnetic spectroscopy which are most commonly used to assess the brain metabolism and functional connectivity changes such as altered responses to emotional stimuli in MDD or overactivation of ventromedial prefrontal areas during delayed and underactivation of dorsolateral prefrontal regions during impulsive reward decisions in AUD and dysfunction of gamma-aminobutyric acid (GABA) and/or glutamate neurotransmitter systems, low NAA and myo-Inositol in both MDD and AUD.
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Affiliation(s)
- Andriana Kakanakova
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Faculty of Medicine, Plovdiv, Bulgaria
| | - Stefan Popov
- Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Faculty of Medicine, Plovdiv, Bulgaria
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16
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Lai CH. Task MRI-Based Functional Brain Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:19-33. [PMID: 33834392 DOI: 10.1007/978-981-33-6044-0_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.
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Affiliation(s)
- Chien-Han Lai
- Psychiatry & Neuroscience Clinic, Taoyuan, Taiwan. .,Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan.
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17
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Fateh AA, Cui Q, Duan X, Yang Y, Chen Y, Li D, He Z, Chen H. Disrupted dynamic functional connectivity in right amygdalar subregions differentiates bipolar disorder from major depressive disorder. Psychiatry Res Neuroimaging 2020; 304:111149. [PMID: 32738725 DOI: 10.1016/j.pscychresns.2020.111149] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 06/18/2020] [Accepted: 06/25/2020] [Indexed: 12/12/2022]
Abstract
Notwithstanding being the object of a growing field of clinical research, the investigation of the dynamic resting-state functional connectivity alterations in psychiatric illnesses is still in its early days. Current research on major depressive disorder (MDD) and bipolar disorder (BD) has evidenced abnormal resting-state functional connectivity (rsFC), especially in regions subserving emotional processing and regulation such as the amygdala. However, dynamic changes in functional connectivity within the amygdalar subregions in distinguishing BD and MDD has not yet been fully understood. In this paper, we aim at analyzing the patterns characterizing dynamic FC (dFC) in the right amygdala to investigate the differences between similarly depressed BD and MDD. A number of 40 BD patients, 61 MDD patients and 63 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) at rest. Using the right-amygdala as seed region, we compared the dFC within three subdivisions, namely, laterobasal (LB), centromedial (CM) and superficial (SF) between all the groups. To do so, one-way ANOVA followed by post-hoc t-tests were employed. Compared to HCs, patients with BD had a decreased dFC between right LB and the left postcentral gyrus as well as an increased dFC between right CM and the right cerebellum.Compared to BD patients, patients with MDD showed a decreased dFC between right CM and the cerebellum and an increased dFC between right LB and the left postcentral gyrus. These findings present initial evidence that abnormal patterns of the right-amygdalar subregions shared by BD and MDD supports the differential pathophysiology of these disorders.
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Affiliation(s)
- Ahmed Ameen Fateh
- 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, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Yang
- 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, China
| | - Yuyan Chen
- 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, China
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- 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, China.
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18
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Barbour T, Holmes AJ, Farabaugh AH, DeCross SN, Coombs G, Boeke EA, Wolthusen RPF, Nyer M, Pedrelli P, Fava M, Holt DJ. Elevated Amygdala Activity in Young Adults With Familial Risk for Depression: A Potential Marker of Low Resilience. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:194-202. [PMID: 31948836 PMCID: PMC7448615 DOI: 10.1016/j.bpsc.2019.10.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 10/25/2019] [Accepted: 10/25/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND Amygdala overactivity has been frequently observed in patients with depression, as well as in nondepressed relatives of patients with depression. A remaining unanswered question is whether elevated amygdala activity in those with familial risk for depression is related to the presence of subthreshold symptoms or to a trait-level vulnerability for illness. METHODS To examine this question, functional magnetic resonance imaging data were collected in nondepressed young adults with (family history [FH+]) (n = 27) or without (FH-) (n = 45) a first-degree relative with a history of depression while they viewed images of "looming" or withdrawing stimuli (faces and cars) that varied in salience by virtue of their apparent proximity to the subject. Activation of the amygdala and 2 other regions known to exhibit responses to looming stimuli, the dorsal intraparietal sulcus (DIPS) and ventral premotor cortex (PMv), were measured, as well as levels of resilience, anxiety, and psychotic and depressive symptoms. RESULTS Compared with the FH- group, the FH+ group exhibited significantly greater responses of the amygdala, but not the dorsal intraparietal sulcus or ventral premotor cortex, to looming face stimuli. Moreover, amygdala responses in the FH+ group were negatively correlated with levels of resilience and unrelated to levels of subthreshold symptoms of psychopathology. CONCLUSIONS These findings indicate that elevated amygdala activity in nondepressed young adults with a familial history of depression is more closely linked to poor resilience than to current symptom state.
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Affiliation(s)
- Tracy Barbour
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Amy H Farabaugh
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Stephanie N DeCross
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Garth Coombs
- Department of Psychology, Harvard University, Cambridge, Massachusetts
| | - Emily A Boeke
- Department of Psychology, New York University, New York, New York
| | - Rick P F Wolthusen
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Translational Developmental Neuroscience Section, Department of Child and Adolescent Psychiatry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Maren Nyer
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Paola Pedrelli
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts
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19
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Hirjak D, Rashidi M, Kubera KM, Northoff G, Fritze S, Schmitgen MM, Sambataro F, Calhoun VD, Wolf RC. Multimodal Magnetic Resonance Imaging Data Fusion Reveals Distinct Patterns of Abnormal Brain Structure and Function in Catatonia. Schizophr Bull 2020; 46:202-210. [PMID: 31174212 PMCID: PMC6942158 DOI: 10.1093/schbul/sbz042] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Catatonia is a nosologically unspecific syndrome, which subsumes a plethora of mostly complex affective, motor, and behavioral phenomena. Although catatonia frequently occurs in schizophrenia spectrum disorders (SSD), specific patterns of abnormal brain structure and function underlying catatonia are unclear at present. Here, we used a multivariate data fusion technique for multimodal magnetic resonance imaging (MRI) data to investigate patterns of aberrant intrinsic neural activity (INA) and gray matter volume (GMV) in SSD patients with and without catatonia. Resting-state functional MRI and structural MRI data were collected from 87 right-handed SSD patients. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). A multivariate analysis approach was used to examine co-altered patterns of INA and GMV. Following a categorical approach, we found predominantly frontothalamic and corticostriatal abnormalities in SSD patients with catatonia (NCRS total score ≥ 3; n = 24) when compared to SSD patients without catatonia (NCRS total score = 0; n = 22) matched for age, gender, education, and medication. Corticostriatal network was associated with NCRS affective scores. Following a dimensional approach, 33 SSD patients with catatonia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision were identified. NCRS behavioral scores were associated with a joint structural and functional system that predominantly included cerebellar and prefrontal/cortical motor regions. NCRS affective scores were associated with frontoparietal INA. This study provides novel neuromechanistic insights into catatonia in SSD suggesting co-altered structure/function-interactions in neural systems subserving coordinated visuospatial functions and motor behavior.
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Affiliation(s)
- Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany,To whom correspondence should be addressed; tel: 49-621-1703-0, fax: 49-621-1703-2305, e-mail:
| | - Mahmoud Rashidi
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany,Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Mike M Schmitgen
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padova, Italy
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, The University of New Mexico and the Mind Research Network, Albuquerque, NM
| | - Robert C Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
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20
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Sambataro F, Thomann PA, Nolte HM, Hasenkamp JH, Hirjak D, Kubera KM, Hofer S, Seidl U, Depping MS, Stieltjes B, Maier-Hein K, Wolf RC. Transdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depression. Eur Neuropsychopharmacol 2019; 29:925-935. [PMID: 31279591 DOI: 10.1016/j.euroneuro.2019.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/14/2019] [Accepted: 06/10/2019] [Indexed: 12/30/2022]
Abstract
Major depressive disorder (MDD) and schizophrenia (SCZ) share neurobiological and clinical commonalities. Altered functional connectivity of large-scale brain networks has been associated with both disorders. Electroconvulsive therapy (ECT) has proven to be an effective treatment in severe forms of MDD and SCZ. However, the role of ECT on the modulation of the dynamics of brain networks is still unknown. In this study, we used resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity in 16 pharmacoresistant patients with SCZ or MDD and a matched group of normal controls. Patients were scanned before and after right-sided unilateral ECT. Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate the effects of ECT treatment on intrinsic components (INs). Functional network connectivity (FNC) was calculated between pairs of INs. Patients had reduced connectivity within a striato-thalamic network in the thalamus as well as increased low frequency oscillations in a striatal network. ECT reduced low frequency oscillations (LFOs) on a striatal network along with increasing functional connectivity in the medial prefrontal cortex within the DMN. Following ECT treatment, the FNC of the executive network was reduced with the DMN and increased with the salience network, respectively. Our findings suggest transnosological effects of ECT on the connectivity of large-scale networks as well as at the level of their interplay. Furthermore, they support a transnosological approach for the investigation not only of the neural correlates of the disease but also of the brain mechanism of treatment of mental disorders.
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Affiliation(s)
- Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy.
| | - Philipp Arthur Thomann
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany; Center for Mental Health, Odenwald District Healthcare Center, Erbach, Germany
| | - Henrike Maria Nolte
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - J H Hasenkamp
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, 68159 Mannheim, Germany
| | - Katharina M Kubera
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Stefan Hofer
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany; Department of Anaesthesiology, Westpfalz-Klinikum GmbH, 67655 Kaiserslautern, Germany
| | - Ulrich Seidl
- Department of Anaesthesiology, Westpfalz-Klinikum GmbH, 67655 Kaiserslautern, Germany
| | - Malte Sebastian Depping
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany
| | - Bram Stieltjes
- Department of Radiology, Section Quantitative Imaging Based Disease Characterization, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing Group, Division of Medical and Biological Informatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Robert Christian Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, 69115 Heidelberg, Germany.
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Molent C, Olivo D, Wolf RC, Balestrieri M, Sambataro F. Functional neuroimaging in treatment resistant schizophrenia: A systematic review. Neurosci Biobehav Rev 2019; 104:178-190. [PMID: 31276716 DOI: 10.1016/j.neubiorev.2019.07.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 06/25/2019] [Accepted: 07/01/2019] [Indexed: 01/06/2023]
Abstract
Despite the availability of several drugs, about 30% of patients with schizophrenia still fail to respond properly to a course of appropriate antipsychotic treatment. Functional neuroimaging studies have shown widespread patterns of altered activation and functional connectivity in treatment-resistant schizophrenia (TRS). The aim of the present study was to examine the available functional magnetic resonance imaging studies investigating TRS and to identify common patterns of altered brain function that could predict the lack of response to antipsychotic treatment in this disorder. Alterations of activation and functional connectivity in fronto-temporal, cortico-striatal, default mode network and salience networks, and of their interplay, were associated with TRS. Our findings support the notion that large-scale network alterations present in schizophrenia lie in a continuum within treatment response with the most severe dysfunction in TRS. Few studies with small sample size and without adequate control group limit the generalizability of current literature. Future controlled longitudinal studies are needed to identify neuroimaging biomarkers of pharmacotherapy response to inform individual treatment selection and facilitate early clinical response.
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Affiliation(s)
- Cinzia Molent
- Department of Medicine (DAME), University of Udine, Udine, Italy
| | - Daniele Olivo
- Department of Medicine (DAME), University of Udine, Udine, Italy; Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Robert Christian Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Germany
| | | | - Fabio Sambataro
- Department of Medicine (DAME), University of Udine, Udine, Italy; Department of Neuroscience (DNS), University of Padova, Padua, Italy.
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22
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Iwabuchi SJ, Auer DP, Lankappa ST, Palaniyappan L. Baseline effective connectivity predicts response to repetitive transcranial magnetic stimulation in patients with treatment-resistant depression. Eur Neuropsychopharmacol 2019; 29:681-690. [PMID: 30827757 DOI: 10.1016/j.euroneuro.2019.02.012] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 02/04/2019] [Accepted: 02/14/2019] [Indexed: 12/11/2022]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) has become a popular treatment option for treatment-resistant depression (TRD). However, suboptimal response rates highlight the need for improved efficacy through optimisation of treatment protocol and patient selection. We investigate whether the limbic salience network and its connectivity with prefrontal stimulation sites predict immediate and longer-term responsiveness to rTMS. Twenty-seven patients with TRD were randomly allocated to receive 16 sessions of either conventional rTMS or intermittent theta-burst (iTBS) over 4 weeks; delivered using connectivity profiling and neuronavigation to target person-specific dorsolateral prefrontal cortex (DLPFC). At baseline and 3-month follow-up, patients underwent clinical assessment and scanning session, and 1-month clinical follow-up. Resting-state fMRI data were entered into seed-based functional and effective connectivity analyses between right anterior insula (rAI) and DLPFC target, and independent components analysis to extract resting-state networks. Cerebral blood flow (CBF) was also assessed in the rAI. All brain measures were compared between baseline and follow-up, and related to treatment response at 1- and 3-months. Baseline fronto-insular effective connectivity and salience network connectivity were significantly positively correlated, while baseline rAI CBF was negatively correlated, with early (1-month) response to rTMS treatment but not sustained response (3-months), suggesting persistence of therapeutic response is not associated with baseline features. Connectivity or CBF measures did not change between the two time points. We demonstrate that fronto-insular and salience-network interactions can predict early response to rTMS in TRD, suggesting that these network nodes may be key regions toward developing rTMS response biomarkers.
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Affiliation(s)
- S J Iwabuchi
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham NG7 2UH, United Kingdom; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - D P Auer
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham NG7 2UH, United Kingdom; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - S T Lankappa
- Nottinghamshire Healthcare NHS Foundation Trust, Nottingham NG7 2UH, UK
| | - L Palaniyappan
- Departments of Psychiatry and Medical Biophysics and Robarts Research Institute, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada.
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23
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Dong D, Ming Q, Zhong X, Pu W, Zhang X, Jiang Y, Gao Y, Sun X, Wang X, Yao S. State-independent alterations of intrinsic brain network in current and remitted depression. Prog Neuropsychopharmacol Biol Psychiatry 2019; 89:475-480. [PMID: 30193990 DOI: 10.1016/j.pnpbp.2018.08.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 08/15/2018] [Accepted: 08/29/2018] [Indexed: 01/30/2023]
Abstract
BACKGROUND It has been proposed that state-independent, or trait, neurobiological alterations across illness phases may contribute to the high recurrence of major depressive disorder (MDD). Although intrinsic brain network abnormalities have been implicated consistently in MDD neuropathology, MDD state-independent and -dependent resting-state network alterations have not been clearly studied. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were collected from 57 medication-naive first-episode current MDD patients, 35 remitted MDD patients, and 66 healthy controls (HCs). Independent component analysis (ICA) was used to extract subnetworks of the default mode network (DMN), central executive network (CEN), and salience network (SN). RESULTS Relative to HCs, the current MDD and remitted MDD groups had decreased intra-intrinsic functional connectivity (iFC) in the dorsal lateral prefrontal cortex (dlPFC) of the left CEN, increased inter-FC between the SN and right CEN (rCEN), and decreased inter-FC between the anterior DMN (aDMN) and rCEN. The altered intra-iFC in the left CEN were correlated negatively with the depressive level in the remitted MDD. CONCLUSIONS Hypoactivity of the dlPFC in the left CEN, increased inter-FC between the SN and rCEN, and decreased inter-FC between the aDMN and rCEN may reflect state-independent biomarkers of MDD.
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Affiliation(s)
- Daifeng Dong
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Qingsen Ming
- Dpartment of Psychiatry, The First Affiliated Hospital of Sochoow University, Suzhou, Jiangsu, PR China
| | - Xue Zhong
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Weidan Pu
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Xiaocui Zhang
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Yali Jiang
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Yidian Gao
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Xiaoqiang Sun
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Xiang Wang
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China
| | - Shuqiao Yao
- Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China; Medical Psychological Institute of Central South University, Changsha, Hunan, PR China; China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China.
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24
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Al Zoubi O, Mayeli A, Tsuchiyagaito A, Misaki M, Zotev V, Refai H, Paulus M, Bodurka J. EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects. Front Hum Neurosci 2019; 13:56. [PMID: 30863294 PMCID: PMC6399140 DOI: 10.3389/fnhum.2019.00056] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/31/2019] [Indexed: 01/15/2023] Open
Abstract
Electroencephalography (EEG) measures the brain’s electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts’ brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Japan Society for the Promotion Science, Tokyo, Japan.,Research Center for Child Development, Chiba University, Chiba, Japan
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Hazem Refai
- Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States.,Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
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25
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Agrawal S, Chinnadurai V, Kaur A, Kumar P, Kaur P, Sharma R, Kumar Singh A. Estimation of Functional Connectivity Modulations During Task Engagement and Their Neurovascular Underpinnings Through Hemodynamic Reorganization Method. Brain Connect 2019; 9:341-355. [PMID: 30688078 DOI: 10.1089/brain.2018.0656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
This study proposes an approach to understand the effect of task engagement through integrated analysis of modulations in functional networks and associated changes in their neurovascular underpinnings at every voxel. For this purpose, a novel approach that brings reorganization in acquired task-functional magnetic resonance imaging information based on hemodynamic characteristics of every task stimulus is proposed and validated. At first, modulations in functional networks of visual target detection task were estimated at every voxel through proposed methodology. It revealed task stimulus dependency in the modulation of default mode network (DMN). The DMN modulated as task negative network (TNN) during target stimulus. On the contrary, it was not entirely TNN during nontarget stimulus. The frontal-parietal and visual networks modulated as task positive network during both task stimuli. Further, modulations of neurovascular underpinnings associated with engagement of task were estimated by correlating the hemodynamically reorganized task blood oxygen level dependent information with simultaneously acquired electroencephalography frequency powers. It revealed the strong association of neurovascular underpinnings with their modulation of functional networks and the associated neuronal activity during task engagement. Finally, graph theoretical parameters such as local, global efficiency and clustering coefficient were also measured at the specific regions for validating the results of proposed method. Modulation observed in graph theory measures clearly validated the activation and deactivation of functional networks observed by the proposed method during task engagement. Thus, the voxel-wise estimation of task-related modulation of functional networks and associated neurovascular underpinnings through proposed technique provide better insights into neuronal mechanism involved during engagement in a task.
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Affiliation(s)
- Swati Agrawal
- 1 NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
| | | | - Ardaman Kaur
- 1 NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
| | - Pawan Kumar
- 1 NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
| | - Prabhjot Kaur
- 1 NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
| | - Rinku Sharma
- 2 Delhi Technological University, Shahbad Daulatpur, Delhi, India
| | - Ajay Kumar Singh
- 1 NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
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26
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van Geest Q, Douw L, van 't Klooster S, Leurs CE, Genova HM, Wylie GR, Steenwijk MD, Killestein J, Geurts JJG, Hulst HE. Information processing speed in multiple sclerosis: Relevance of default mode network dynamics. NEUROIMAGE-CLINICAL 2018; 19:507-515. [PMID: 29984159 PMCID: PMC6030565 DOI: 10.1016/j.nicl.2018.05.015] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 04/30/2018] [Accepted: 05/13/2018] [Indexed: 11/19/2022]
Abstract
Objective To explore the added value of dynamic functional connectivity (dFC) of the default mode network (DMN) during resting-state (RS), during an information processing speed (IPS) task, and the within-subject difference between these conditions, on top of conventional brain measures in explaining IPS in people with multiple sclerosis (pwMS). Methods In 29 pwMS and 18 healthy controls, IPS was assessed with the Letter Digit Substitution Test and Stroop Card I and combined into an IPS-composite score. White matter (WM), grey matter (GM) and lesion volume were measured using 3 T MRI. WM integrity was assessed with diffusion tensor imaging. During RS and task-state fMRI (i.e. symbol digit modalities task, IPS), stationary functional connectivity (sFC; average connectivity over the entire time series) and dFC (variation in connectivity using a sliding window approach) of the DMN was calculated, as well as the difference between both conditions (i.e. task-state minus RS; ΔsFC-DMN and ΔdFC-DMN). Regression analysis was performed to determine the most important predictors for IPS. Results Compared to controls, pwMS performed worse on IPS-composite (p = 0.022), had lower GM volume (p < 0.05) and WM integrity (p < 0.001), but no alterations in sFC and dFC at the group level. In pwMS, 52% of variance in IPS-composite could be predicted by cortical volume (β = 0.49, p = 0.01) and ΔdFC-DMN (β = 0.52, p < 0.01). After adding dFC of the DMN to the model, the explained variance in IPS increased with 26% (p < 0.01). Conclusion On top of conventional brain measures, dFC from RS to task-state explains additional variance in IPS. This highlights the potential importance of the DMN to adapt upon cognitive demands to maintain intact IPS in pwMS. Problems with information processing speed occur often in multiple sclerosis (MS) Dynamics in brain communication can reflect information transfer within the brain With fMRI, dynamic communication can be measured, which increases upon task demands This increase in dynamics explains information processing speed in MS
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Affiliation(s)
- Q van Geest
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - L Douw
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - S van 't Klooster
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - C E Leurs
- Department of Neurology, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - H M Genova
- Neuropsychology and Neuroscience Laboratory, Kessler Foundation, West Orange, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - G R Wylie
- Neuropsychology and Neuroscience Laboratory, Kessler Foundation, West Orange, NJ, USA; Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - M D Steenwijk
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - J Killestein
- Department of Neurology, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - J J G Geurts
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - H E Hulst
- Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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27
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Geng X, Xu J, Liu B, Shi Y. Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity. Front Neurosci 2018; 12:38. [PMID: 29515348 PMCID: PMC5825897 DOI: 10.3389/fnins.2018.00038] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/16/2018] [Indexed: 12/29/2022] Open
Abstract
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.
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Affiliation(s)
- Xiangfei Geng
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
| | - Baolin Liu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, China
- State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China
| | - Yonggang Shi
- Laboratory of Neural Imaging, Keck School of Medicine, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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