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Gbyl K, Labanauskas V, Lundsgaard CC, Mathiassen A, Ryszczuk A, Siebner HR, Rostrup E, Madsen K, Videbech P. Electroconvulsive therapy disrupts functional connectivity between hippocampus and posterior default mode network. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110981. [PMID: 38373628 DOI: 10.1016/j.pnpbp.2024.110981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/13/2024] [Accepted: 02/16/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND The mechanisms underlying memory deficits after electroconvulsive therapy (ECT) remain unclear but altered functional interactions between hippocampus and neocortex may play a role. OBJECTIVES To test whether ECT reduces functional connectivity between hippocampus and posterior regions of the default mode network (DMN) and to examine whether altered hippocampal-neocortical functional connectivity correlates with memory impairment. A secondary aim was to explore if these connectivity changes are present 6 months after ECT. METHODS In-patients with severe depression (n = 35) received bitemporal ECT. Functional connectivity of the hippocampus was probed with resting-state fMRI before the first ECT-session, after the end of ECT, and at a six-month follow-up. Memory was assessed with the Verbal Learning Test - Delayed Recall. Seed-based connectivity analyses established connectivity of four hippocampal seeds, covering the anterior and posterior parts of the right and left hippocampus. RESULTS Compared to baseline, three of four hippocampal seeds became less connected to the core nodes of the posterior DMN in the week after ECT with Cohen's d ranging from -0.9 to -1.1. At the group level, patients showed post-ECT memory impairment, but individual changes in delayed recall were not correlated with the reduction in hippocampus-DMN connectivity. At six-month follow-up, no significant hippocampus-DMN reductions in connectivity were evident relative to pre-ECT, and memory scores had returned to baseline. CONCLUSION ECT leads to a temporary disruption of functional hippocampus-DMN connectivity in patients with severe depression, but the change in connectivity strength is not related to the individual memory impairment.
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Affiliation(s)
- Krzysztof Gbyl
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
| | - Vytautas Labanauskas
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Christoffer Cramer Lundsgaard
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - André Mathiassen
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Adam Ryszczuk
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark
| | - Hartwig Roman Siebner
- Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Egill Rostrup
- Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark
| | - Kristoffer Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Poul Videbech
- Center for Neuropsychiatric Depression Research, Mental Health Center Glostrup, Mental Health Services of the Capital Region of Denmark, Copenhagen University Hospital, Glostrup, Denmark; Institute of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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Sacchet MD, Keshava P, Walsh SW, Potash RM, Li M, Liu H, Pizzagalli DA. Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:616-625. [PMID: 38417786 PMCID: PMC11156548 DOI: 10.1016/j.bpsc.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 02/10/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior. METHODS We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures. RESULTS Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits. CONCLUSIONS The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.
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Affiliation(s)
- Matthew D Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
| | - Poorvi Keshava
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Shane W Walsh
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Ruby M Potash
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts; Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; McLean Imaging Center, McLean Hospital, Belmont, Massachusetts
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Liu Y, Hu Q, Xu S, Li W, Liu J, Han L, Mao H, Cai F, Liu Q, Zhu R, Fang C, Lou Y, Wang Z, Yang H, Wang W. Antidepressant effects of dexmedetomidine compared with ECT in patients with treatment-resistant depression. J Affect Disord 2024; 347:437-444. [PMID: 38000472 DOI: 10.1016/j.jad.2023.11.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVE This pilot study was designed to investigate the antidepressant effects of dexmedetomidine (DEX), a selective α2-adrenergic receptor agonist, in patients with treatment-resistant depression (TRD). The antidepressant effects of dexmedetomidine was compared with ECT, which is widely used in clinical practice for treatment of patients with TRD. METHODS Seventy six patients with TRD were randomly assigned to receive 10 sessions of DEX infusions or electroconvulsive therapy (ECT) treatment. The primary outcome was the changes of depression severity determined by the improvement of 24-item Hamilton Depression Rating Scale (HDRS-24). The second outcomes were the rates of therapeutic response (reduction in HDRS-24 ≥ 50 %) and remission (HDRS-24 ≤ 10 and reduction in HDRS-24 ≥ 60 %) at posttreatment and after 3 months of follow-up visits. RESULTS We found that 10 sessions of DEX infusions or ECT treatments significantly improved HDRS-24 scores at posttreatment and after 3 months of follow-up visits compared with the baseline. In addition, there was no significant difference between DEX infusions and ECT treatments regarding HDRS-24 at these evaluating points. Furthermore, the depression severity dropped to mild after 2 sessions of DEX infusion. In contrast, at least 6 sessions of ECT treatment were needed to achieve a same level. Finally, the rates of therapeutic response and remission were comparable between the two groups. No serious adverse events were observed. CONCLUSIONS Based on current published evidence, we conclude that DEX exhibits rapid and durable antidepressant properties similar to ECT but with fewer side effects.
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Affiliation(s)
- Yusi Liu
- Jinzhou Medical University, Jinzhou, Liaoning 121001, China
| | - Qiyun Hu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China
| | - Sen Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China
| | - Wanwen Li
- Department of Psychiatry, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310063, China
| | - Junyun Liu
- Department of Psychiatry, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310063, China
| | - Liang Han
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Hui Mao
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Fang Cai
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Qiaoyan Liu
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Renlai Zhu
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Caiyun Fang
- Department of Anesthesiology, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310063, China
| | - Yifei Lou
- Department of Anesthesiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310023, China
| | - Zhenhua Wang
- Department of Anesthesiology, Jiaxing Hospital of T.C.M., Affiliated Hospital of Zhejiang Chinese Medical University, Jiaxing, Zhejiang 314015, China
| | - Huiling Yang
- Department of Anesthesiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310023, China
| | - Wenyuan Wang
- Center for Rehabilitation Medicine, Department of Anesthesiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China.
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Dahmani L, Bai Y, Zhang W, Ren J, Li S, Hu Q, Fu X, Ma J, Wei W, Wang M, Liu H, Wang D. Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578238. [PMID: 38352322 PMCID: PMC10862849 DOI: 10.1101/2024.01.31.578238] [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] [Indexed: 02/23/2024]
Abstract
Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.
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Affiliation(s)
- Louisa Dahmani
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Zhang
- Changping Laboratory, Beijing, China
| | | | - Shiyi Li
- Changping Laboratory, Beijing, China
| | - Qingyu Hu
- Changping Laboratory, Beijing, China
| | | | - Jianjun Ma
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Wei
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Hesheng Liu
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
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Petersen JZ, Macoveanu J, Ysbæk-Nielsen AT, Kessing LV, Jørgensen MB, Miskowiak KW. Neural correlates of episodic memory decline following electroconvulsive therapy: An exploratory functional magnetic resonance imaging study. J Psychopharmacol 2024; 38:168-177. [PMID: 38159102 DOI: 10.1177/02698811231221153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) is an efficient and rapid-acting treatment indicated for severe depressive disorders. While ECT is commonly accompanied by transient memory decline, the brain mechanisms underlying these side effects remain unclear. AIMS In this exploratory functional magnetic resonance (fMRI) study, we aimed to compare effects of ECT versus pharmacological treatment on neural response during episodic memory encoding in patients with affective disorders. METHODS This study included 32 ECT-treated patients (major depressive disorder (MDD), n = 23; bipolar depression, n = 9) and 40 partially remitted patients in pharmacological treatment (MDD, n = 24; bipolar disorder, n = 16). Participants underwent neuropsychological assessment, a strategic picture encoding fMRI scan paradigm, and mood rating. The ECT group was assessed before ECT (pre-ECT) and 3 days after their eighth ECT session (post-ECT). RESULTS Groups were comparable on age, gender, and educational years (ps ⩾ 0.05). Within-group analyses revealed a selective reduction in verbal learning and episodic memory pre- to post-ECT (p = 0.012) but no decline in global cognitive performance (p = 0.3). Functional magnetic resonance imaging analyses adjusted for mood symptoms revealed greater activity in ECT-treated patients than pharmacologically treated No-ECT patients across left precentral gyrus (PCG), right dorsomedial prefrontal cortex (dmPFC), and left middle frontal gyrus (MFG). In ECT-treated patients, greater decline in verbal learning and memory performance from pre- to post-ECT correlated with higher PCG response (r = -0.46, p = 0.008), but not with dmPFC or MFG activity (ps ⩾ 0.1), post-ECT. CONCLUSIONS Episodic memory decline was related to greater neural activity in the left PCG, but unrelated to increased dmPFC and MFG activity, immediately after ECT.
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Affiliation(s)
- Jeff Zarp Petersen
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Neurocogntion and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Julian Macoveanu
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Neurocogntion and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Alexander Tobias Ysbæk-Nielsen
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Neurocogntion and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Science, University of Copenhagen, København Ø, Denmark
| | - Martin Balslev Jørgensen
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Science, University of Copenhagen, København Ø, Denmark
| | - Kamilla Woznica Miskowiak
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Frederiksberg Hospital, Copenhagen, Denmark
- Neurocogntion and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
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Fu Z, Abbott CC, Miller J, Deng ZD, McClintock SM, Sendi MSE, Sui J, Calhoun VD. Cerebro-cerebellar functional neuroplasticity mediates the effect of electric field on electroconvulsive therapy outcomes. Transl Psychiatry 2023; 13:43. [PMID: 36746924 PMCID: PMC9902462 DOI: 10.1038/s41398-023-02312-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 02/08/2023] Open
Abstract
Electroconvulsive therapy (ECT) is the most effective treatment for severe depression and works by applying an electric current through the brain. The applied current generates an electric field (E-field) and seizure activity, changing the brain's functional organization. The E-field, which is determined by electrode placement (right unilateral or bitemporal) and pulse amplitude (600, 700, or 800 milliamperes), is associated with the ECT response. However, the neural mechanisms underlying the relationship between E-field, functional brain changes, and clinical outcomes of ECT are not well understood. Here, we investigated the relationships between whole-brain E-field (Ebrain, the 90th percentile of E-field magnitude in the brain), cerebro-cerebellar functional network connectivity (FNC), and clinical outcomes (cognitive performance and depression severity). A fully automated independent component analysis framework determined the FNC between the cerebro-cerebellar networks. We found a linear relationship between Ebrain and cognitive outcomes. The mediation analysis showed that the cerebellum to middle occipital gyrus (MOG)/posterior cingulate cortex (PCC) FNC mediated the effects of Ebrain on cognitive performance. In addition, there is a mediation effect through the cerebellum to parietal lobule FNC between Ebrain and antidepressant outcomes. The pair-wise t-tests further demonstrated that a larger Ebrain was associated with increased FNC between cerebellum and MOG and decreased FNC between cerebellum and PCC, which were linked with decreased cognitive performance. This study implies that an optimal E-field balancing the antidepressant and cognitive outcomes should be considered in relation to cerebro-cerebellar functional neuroplasticity.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | | | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Shawn M McClintock
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Mohammad S E Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Fu Z, Abbott CC, Sui J, Calhoun VD. Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes. Front Pharmacol 2023; 14:1102413. [PMID: 36755955 PMCID: PMC9899999 DOI: 10.3389/fphar.2023.1102413] [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: 11/18/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Christopher C. Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Shi Z, Jiang B, Liu T, Wang L, Pei G, Suo D, Zhang J, Funahashi S, Wu J, Yan T. Individual-level functional connectomes predict the motor symptoms of Parkinson's disease. Cereb Cortex 2023; 33:6282-6290. [PMID: 36627247 DOI: 10.1093/cercor/bhac503] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 01/12/2023] Open
Abstract
Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.
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Affiliation(s)
- Zhongyan Shi
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Bo Jiang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jian Zhang
- Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
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Ji GJ, Zalesky A, Wang Y, He K, Wang L, Du R, Sun J, Bai T, Chen X, Tian Y, Zhu C, Wang K. Linking Personalized Brain Atrophy to Schizophrenia Network and Treatment Response. Schizophr Bull 2023; 49:43-52. [PMID: 36318234 PMCID: PMC9810021 DOI: 10.1093/schbul/sbac162] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia manifests with marked heterogeneity in both clinical presentation and underlying biology. Modeling individual differences within clinical cohorts is critical to translate knowledge reliably into clinical practice. We hypothesized that individualized brain atrophy in patients with schizophrenia may explain the heterogeneous outcomes of repetitive transcranial magnetic stimulation (rTMS). STUDY DESIGN The magnetic resonance imaging (MRI) data of 797 healthy subjects and 91 schizophrenia patients (between January 1, 2015, and December 31, 2020) were retrospectively selected from our hospital database. The healthy subjects were used to establish normative reference ranges for cortical thickness as a function of age and sex. Then, a schizophrenia patient's personalized atrophy map was computed as vertex-wise deviations from the normative model. Each patient's atrophy network was mapped using resting-state functional connectivity MRI from a subgroup of healthy subjects (n = 652). In total 52 of the 91 schizophrenia patients received rTMS in a randomized clinical trial (RCT). Their longitudinal symptom changes were adopted to test the clinical utility of the personalized atrophy map. RESULTS The personalized atrophy maps were highly heterogeneous across patients, but functionally converged to a putative schizophrenia network that comprised regions implicated by previous group-level findings. More importantly, retrospective analysis of rTMS-RCT data indicated that functional connectivity of the personalized atrophy maps with rTMS targets was significantly associated with the symptom outcomes of schizophrenia patients. CONCLUSIONS Normative modeling can aid in mapping the personalized atrophy network associated with treatment outcomes of patients with schizophrenia.
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Affiliation(s)
- Gong-Jun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Andrew Zalesky
- Departments of Psychiatry and Biomedical Engineering, Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - Yingru Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Kongliang He
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
- Department of Psychiatry, Anhui Mental Health Center, Hefei, 230022, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Rongrong Du
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Xingui Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Chunyan Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
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10
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Zhao Y, Dahmani L, Li M, Hu Y, Ren J, Lui S, Wang D, Kuang W, Gong Q, Liu H. Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:42-51. [PMID: 34995770 DOI: 10.1016/j.bpsc.2021.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/03/2021] [Accepted: 12/19/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD are not well understood. We aimed to identify robust, generalizable, functional connectivity (FC)-based biomarkers for different symptom dimensions in MDD using individualized functional connectomes. METHODS Patterns of FC associated with symptom severity were identified using a novel, individualized, functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (discovery dataset, n = 95) and replicated in an independent validation dataset (n = 94). The correlation between FC changes and symptom changes was further explored in a treatment dataset (n = 55). RESULTS Two distinct symptom clusters previously identified in patients with MDD, namely dysphoric and anxiosomatic clusters, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the validation dataset. Moreover, the marker also tracked dysphoric symptom improvement following a 2-week antidepressant treatment. For comparison, we repeated our analyses using a nonindividualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that the generalizability of the connectivity-symptom association was hampered when functional regions were not localized in individuals. CONCLUSIONS This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of independent validation in psychiatric neuroimaging analysis.
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Affiliation(s)
- Youjin Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Yongbo Hu
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts; Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina.
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11
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Social defeat drives hyperexcitation of the piriform cortex to induce learning and memory impairment but not mood-related disorders in mice. Transl Psychiatry 2022; 12:380. [PMID: 36088395 PMCID: PMC9464232 DOI: 10.1038/s41398-022-02151-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/27/2022] [Accepted: 09/02/2022] [Indexed: 12/05/2022] Open
Abstract
Clinical studies have shown that social defeat is an important cause of mood-related disorders, accompanied by learning and memory impairment in humans. The mechanism of mood-related disorders has been widely studied. However, the specific neural network involved in learning and memory impairment caused by social defeat remains unclear. In this study, behavioral test results showed that the mice induced both learning and memory impairments and mood-related disorders after exposure to chronic social defeat stress (CSDS). c-Fos immunofluorescence and fiber photometry recording confirmed that CaMKIIα expressing neurons of the piriform cortex (PC) were selectively activated by exposure to CSDS. Next, chemogenetics and optogenetics were performed to activate PC CaMKIIα expressing neurons, which showed learning and memory impairment but not mood-related disorders. Furthermore, chemogenetic inhibition of PC CaMKIIα expressing neurons significantly alleviated learning and memory impairment induced by exposure to CSDS but did not relieve mood-related disorders. Therefore, our data suggest that the overactivation of PC CaMKIIα expressing neurons mediates CSDS-induced learning and memory impairment, but not mood-related disorders, and provides a potential therapeutic target for learning and memory impairment induced by social defeat.
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12
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Zhang J, Zhao T, Zhang J, Zhang Z, Li H, Cheng B, Pang Y, Wu H, Wang J. Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks. Hum Brain Mapp 2022; 43:4710-4721. [PMID: 35735128 PMCID: PMC9491288 DOI: 10.1002/hbm.25985] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/24/2022] [Indexed: 12/20/2022] Open
Abstract
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large‐scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting‐state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject‐specific functional networks and functional network connectivities (FNCs). A connectome‐based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto‐parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.
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Affiliation(s)
- Jiang Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
- Med‐X Center for InformaticsSichuan UniversityChengduChina
| | - Tianyu Zhao
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Jingyue Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Zhiwei Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Hongming Li
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Bochao Cheng
- Department of RadiologyWest China Second University Hospital of Sichuan UniversityChengduChina
| | - Yajing Pang
- School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)GuangzhouChina
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingChina
- Yunnan Key Laboratory of Primate Biomedical ResearchKunmingYunnanChina
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13
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Wu Y, Ji Y, Bai T, Wei Q, Zu M, Guo Y, Lv H, Zhang A, Qiu B, Wang K, Tian Y. Nodal degree changes induced by electroconvulsive therapy in major depressive disorder: Evidence in two independent cohorts. J Affect Disord 2022; 307:46-52. [PMID: 35331825 DOI: 10.1016/j.jad.2022.03.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Electroconvulsive therapy (ECT), a rapidly acting treatment for major depressive disorder (MDD), has been reported to regulate brain networks. Nodes and their connections are the main components of the brain network and are essential for establishing and maintaining effective information transmission. This study aimed to evaluate the role of nodes in mediating antidepressant effects of ECT. METHODS Voxel-based nodal degree analysis was performed in 42 patients with MDD receiving ECT and 42 matched healthy controls at two time points to identify the nodal changes induced by ECT. Verification analysis was evaluated in a second, independent cohort of 23 MDD patients. RESULTS MDD patients showed improved nodal degree of the bilateral angular cortex (AG), precuneus, inferior frontal gyrus (IFG) and the right superior frontal gyrus (SFG) after ECT, and the increased nodal degree index (IND) rate of the AG and precuneus were negatively correlated to the depressive changes following ECT. Furthermore, validation analysis revealed a similar pattern of IND abnormalities in the first and second cohort of MDD patients. CONCLUSION ECT regulates the disrupted nodal degree of the AG and precuneus to achieve an antidepressant effect. This study may provide further insights into the pathogenesis of depression and provide potential targets for antidepressant pharmacotherapies.
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Affiliation(s)
- Yue Wu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yang Ji
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Tongjian Bai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
| | - Qiang Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China
| | - Meidan Zu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yuanyuan Guo
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Huaming Lv
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Aiguo Zhang
- Anhui Mental Health Center, Hefei 230022, China
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230026, China
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China; The College of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China.
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei 230032, China; The College of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China.
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14
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Qiu W, Ma L, Jiang T, Zhang Y. Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions. Front Neurosci 2022; 16:838347. [PMID: 35356058 PMCID: PMC8959420 DOI: 10.3389/fnins.2022.838347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/24/2022] [Indexed: 11/20/2022] Open
Abstract
Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.
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Affiliation(s)
- Wenyuan Qiu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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15
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Xin Y, Bai T, Zhang T, Chen Y, Wang K, Yu S, Liu N, Tian Y. Electroconvulsive therapy modulates critical brain dynamics in major depressive disorder patients. Brain Stimul 2022; 15:214-225. [DOI: 10.1016/j.brs.2021.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/03/2021] [Accepted: 12/20/2021] [Indexed: 01/04/2023] Open
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16
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Bijsterbosch JD, Valk SL, Wang D, Glasser MF. Recent developments in representations of the connectome. Neuroimage 2021; 243:118533. [PMID: 34469814 PMCID: PMC8842504 DOI: 10.1016/j.neuroimage.2021.118533] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/16/2021] [Accepted: 08/28/2021] [Indexed: 02/03/2023] Open
Abstract
Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights.
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Affiliation(s)
- Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; INM-7, Forschungszentrum Jülich, Jülich, Germany
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri, 63110, USA
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17
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The Neurobiological Basis of Cognitive Side Effects of Electroconvulsive Therapy: A Systematic Review. Brain Sci 2021; 11:brainsci11101273. [PMID: 34679338 PMCID: PMC8534116 DOI: 10.3390/brainsci11101273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 11/26/2022] Open
Abstract
Decades of research have consistently demonstrated the efficacy of electroconvulsive therapy (ECT) for the treatment of major depressive disorder (MDD), but its clinical use remains somewhat restricted because of its cognitive side effects. The aim of this systematic review is to comprehensively summarize current evidence assessing potential biomarkers of ECT-related cognitive side effects. Based on our systematic search of human studies indexed in PubMed, Scopus, and Web of Knowledge, a total of 29 studies evaluating patients with MDD undergoing ECT were reviewed. Molecular biomarkers studies did not consistently identify concentration changes in plasma S-100 protein, neuron-specific enolase (NSE), or Aβ peptides significantly associated with cognitive performance after ECT. Importantly, these findings suggest that ECT-related cognitive side effects cannot be explained by mechanisms of neural cell damage. Notwithstanding, S-100b protein and Aβ40 peptide concentrations, as well as brain-derived neurotrophic factor (BDNF) polymorphisms, have been suggested as potential predictive biomarkers of cognitive dysfunction after ECT. In addition, recent advances in brain imaging have allowed us to identify ECT-induced volumetric and functional changes in several brain structures closely related to memory performance such as the hippocampus. We provide a preliminary framework to further evaluate neurobiological cognitive vulnerability profiles of patients with MDD treated with ECT.
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18
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Dini H, Sendi MSE, Sui J, Fu Z, Espinoza R, Narr KL, Qi S, Abbott CC, van Rooij SJH, Riva-Posse P, Bruni LE, Mayberg HS, Calhoun VD. Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder. Front Hum Neurosci 2021; 15:689488. [PMID: 34295231 PMCID: PMC8291148 DOI: 10.3389/fnhum.2021.689488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/31/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
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Affiliation(s)
- Hossein Dini
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Mohammad S E Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Luis Emilio Bruni
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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