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Bø R, Kraft B, Skilbrei A, Jonassen R, Harmer CJ, Landrø NI. Inhibition moderates the effect of attentional bias modification for reducing residual depressive symptoms: A randomized sham-controlled clinical trial. J Behav Ther Exp Psychiatry 2024; 85:101982. [PMID: 39111231 DOI: 10.1016/j.jbtep.2024.101982] [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: 01/23/2024] [Revised: 04/24/2024] [Accepted: 07/24/2024] [Indexed: 08/27/2024]
Abstract
OBJECTIVES Residual symptoms represent risk factor for relapse. Attention bias modification (ABM) may reduce clinical and sub-clinical depressive symptoms, indicating that is may be of relevance when preventing relapse. Current evidence suggests that executive functions may moderate the outcome of interventions targeting depressive symptoms. METHODS We assessed inhibition and shifting as indicators of executive functioning by means of the Color-Word Interference Test (i.e., "Stroop task"). These baseline characteristics were investigated as moderator of the effect of ABM on depression symptoms in a double-blinded randomized sham-controlled trial of ABM including patients with a history of recurrent depression (N = 301). Inclusion and follow-ups took place from January 2015 to October 2016. The trial was retrospectively registered #NCT02658682 January 2016. RESULTS The moderation analysis was based on the interaction term ABM x Stroop. Scaled inhibition scores ≤10.8, but not shifting ability, moderated the effect of ABM compared to sham on clinician-rated depression (HDRS). The difference from the 15th to the 85th percentile of the inhibition score was about 1 HDRS-point, indicating a small effect size. No moderation was found when self-reported depression and AB were the outcome. Post-hoc power calculation indicates risk of Type-II error. CONCLUSION When targeting depressive symptoms, ABM seems to be somewhat more effective in patients with weak inhibitory control. This suggests that evaluating the level of inhibition in individual patients could provide some information when making decisions about prescribing ABM to reduce residual symptoms, but the clinical implications of this is uncertain due to an overall small effect size attributable to ABM. Future studies should examine whether inhibitory control still is a relevant moderator when comparing ABM to treatment options other than the sham control condition.
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Affiliation(s)
- Ragnhild Bø
- Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Norway.
| | - Brage Kraft
- Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Norway; Division of Psychiatry, Diakonhjemmet Hospital, Norway; Department of Behavioural Sciences, Oslo Metropolitan University, Norway
| | - August Skilbrei
- Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Norway
| | - Rune Jonassen
- Faculty of Health Sciences, Oslo Metropolitan University, Norway
| | - Catherine J Harmer
- Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Norway; Department of Psychiatry, Oxford University, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX, United Kingdom
| | - Nils Inge Landrø
- Clinical Neuroscience Research Group, Department of Psychology, University of Oslo, Norway; Division of Psychiatry, Diakonhjemmet Hospital, Norway
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Vrijsen JN, Grafton B, Koster EHW, Lau J, Wittekind CE, Bar-Haim Y, Becker ES, Brotman MA, Joormann J, Lazarov A, MacLeod C, Manning V, Pettit JW, Rinck M, Salemink E, Woud ML, Hallion LS, Wiers RW. Towards implementation of cognitive bias modification in mental health care: State of the science, best practices, and ways forward. Behav Res Ther 2024; 179:104557. [PMID: 38797055 DOI: 10.1016/j.brat.2024.104557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/17/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Cognitive bias modification (CBM) has evolved from an experimental method testing cognitive mechanisms of psychopathology to a promising tool for accessible digital mental health care. While we are still discovering the conditions under which clinically relevant effects occur, the dire need for accessible, effective, and low-cost mental health tools underscores the need for implementation where such tools are available. Providing our expert opinion as Association for Cognitive Bias Modification members, we first discuss the readiness of different CBM approaches for clinical implementation, then discuss key considerations with regard to implementation. Evidence is robust for approach bias modification as an adjunctive intervention for alcohol use disorders and interpretation bias modification as a stand-alone intervention for anxiety disorders. Theoretical predictions regarding the mechanisms by which bias and symptom change occur await further testing. We propose that CBM interventions with demonstrated efficacy should be provided to the targeted populations. To facilitate this, we set a research agenda based on implementation frameworks, which includes feasibility and acceptability testing, co-creation with end-users, and collaboration with industry partners.
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Affiliation(s)
- Janna N Vrijsen
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Depression Expertise Center, Pro Persona Mental Health Care, Nijmegen, the Netherlands.
| | - Ben Grafton
- Centre for the Advancement of Research on Emotion, School of Psychological Science, University of Western Australia, Australia
| | - Ernst H W Koster
- Department of Experimental-Clinical and Health Psychology, Ghent University, Belgium
| | - Jennifer Lau
- Youth Resilience Unit, Queen Mary University of London, UK
| | - Charlotte E Wittekind
- Department of Psychology, Clinical Psychology and Psychotherapy, LMU Munich, Germany
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel-Aviv University, Tel Aviv-Yafo, Israel; School of Neuroscience, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Eni S Becker
- Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | - Melissa A Brotman
- Emotion and Development Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, Conneticut, USA
| | - Amit Lazarov
- School of Neuroscience, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Colin MacLeod
- Centre for the Advancement of Research on Emotion, School of Psychological Science, University of Western Australia, Australia
| | - Victoria Manning
- Monash Addiction Research Centre, Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia; Turning Point, Eastern Health, Melbourne, Victoria, Australia
| | - Jeremy W Pettit
- Department of Psychology and Center for Children and Families, Florida International University, Miami, FL, USA
| | - Mike Rinck
- Emotion and Development Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Elske Salemink
- Department of Clinical Psychology, Faculty of Social and Behavioural Sciences, Utrecht University, the Netherlands
| | - Marcella L Woud
- Clinical Psychology and Experimental Psychopathology, Georg-Elias-Mueller-Institute of Psychology, University of Göttingen, Göttingen, Germany; Mental Health Research and Treatment Center, Ruhr-University Bochum, Bochum, Germany
| | | | - Reinout W Wiers
- Addiction Development and Psychopathology (ADAPT) Lab, Department of Psychology, and Centre for Urban Mental Health, University of Amsterdam, Amsterdam, the Netherlands
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3
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Pierpaoli C, Nayak A, Hafiz R, Irfanoglu MO, Chen G, Taylor P, Hallett M, Hoa M, Pham D, Chou YY, Moses AD, van der Merwe AJ, Lippa SM, Brewer CC, Zalewski CK, Zampieri C, Turtzo LC, Shahim P, Chan L. Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents. JAMA 2024; 331:1122-1134. [PMID: 38497822 PMCID: PMC10949155 DOI: 10.1001/jama.2024.2424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/13/2024] [Indexed: 03/19/2024]
Abstract
Importance US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms. Objective To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group. Design, Setting, and Participants This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit. Exposure AHIs. Main Outcomes and Measures Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling. Results Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs. Conclusions and Relevance In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.
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Affiliation(s)
- Carlo Pierpaoli
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - Amritha Nayak
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Rakibul Hafiz
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - M. Okan Irfanoglu
- Laboratory on Quantitative Medical Imaging, National Institute of Biomedical Imaging and Bioengineering, Bethesda, Maryland
| | - Gang Chen
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland
| | - Paul Taylor
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland
| | - Mark Hallett
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, Maryland
| | - Michael Hoa
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Dzung Pham
- The Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland
| | - Yi-Yu Chou
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Anita D. Moses
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - André J. van der Merwe
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland
| | - Sara M. Lippa
- National Intrepid Center of Excellence Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Carmen C. Brewer
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Chris K. Zalewski
- Military Traumatic Brain Injury Initiative (MTBI2—formerly known as the Center for Neuroscience and Regenerative Medicine [CNRM])
| | - Cris Zampieri
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - L. Christine Turtzo
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, Maryland
| | - Pashtun Shahim
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Leighton Chan
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
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Kim H, Zhu X, Zhao Y, Bell SA, Gehrman PR, Cohen D, Devanand DP, Goldberg TE, Lee S. Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden. Mol Psychiatry 2023; 28:4399-4406. [PMID: 37596355 PMCID: PMC10842478 DOI: 10.1038/s41380-023-02214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/20/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using the linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β = 0.11, P = 0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
- Hyun Kim
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA.
| | - Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Anxiety, Mood, Eating, and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Yiming Zhao
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Sophie A Bell
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Philip R Gehrman
- Department of Psychiatry, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
- Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Daniel Cohen
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
| | - D P Devanand
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Terry E Goldberg
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Area Brain Aging and Mental Health, New York State Psychiatric Institute, New York, NY, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
- Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
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5
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Monferrer M, García AS, Ricarte JJ, Montes MJ, Fernández-Caballero A, Fernández-Sotos P. Facial emotion recognition in patients with depression compared to healthy controls when using human avatars. Sci Rep 2023; 13:6007. [PMID: 37045889 PMCID: PMC10097677 DOI: 10.1038/s41598-023-31277-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/09/2023] [Indexed: 04/14/2023] Open
Abstract
The negative, mood-congruent cognitive bias described in depression, as well as excessive rumination, have been found to interfere with emotional processing. This study focuses on the assessment of facial recognition of emotions in patients with depression through a new set of dynamic virtual faces (DVFs). The sample consisted of 54 stable patients compared to 54 healthy controls. The experiment consisted in an emotion recognition task using non-immersive virtual reality (VR) with DVFs of six basic emotions and neutral expression. Patients with depression showed a worst performance in facial affect recognition compared to healthy controls. Age of onset was negatively correlated with emotion recognition and no correlation was observed for duration of illness or number of lifetime hospitalizations. There was no correlation for the depression group between emotion recognition and degree of psychopathology, excessive rumination, degree of functioning, or quality of life. Hence, it is important to improve and validate VR tools for emotion recognition to achieve greater methodological homogeneity of studies and to be able to establish more conclusive results.
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Affiliation(s)
- Marta Monferrer
- Servicio de Salud de Castilla-La Mancha, Complejo Hospitalario Universitario de Albacete, Servicio de Salud Mental, 02004, Albacete, Spain
| | - Arturo S García
- Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071, Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain
| | - Jorge J Ricarte
- Departmento de Psicología, Universidad de Castilla-La Mancha, 02071, Albacete, Spain
| | - María J Montes
- Servicio de Salud de Castilla-La Mancha, Complejo Hospitalario Universitario de Albacete, Servicio de Salud Mental, 02004, Albacete, Spain
| | - Antonio Fernández-Caballero
- Departmento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071, Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, 02071, Albacete, Spain
- CIBERSAM-ISCIII (Biomedical Research Networking Centre in Mental Health, Instituto de Salud Carlos III), 28016, Madrid, Spain
| | - Patricia Fernández-Sotos
- Servicio de Salud de Castilla-La Mancha, Complejo Hospitalario Universitario de Albacete, Servicio de Salud Mental, 02004, Albacete, Spain.
- CIBERSAM-ISCIII (Biomedical Research Networking Centre in Mental Health, Instituto de Salud Carlos III), 28016, Madrid, Spain.
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6
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Gao Y, Guo X, Zhong Y, Liu X, Tian S, Deng J, Lin X, Bao Y, Lu L, Wang G. Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder. J Affect Disord 2023; 332:136-142. [PMID: 36990286 DOI: 10.1016/j.jad.2023.03.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Gaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals. METHODS This study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time. RESULTS Relative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients. CONCLUSIONS These results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.
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Affiliation(s)
- Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China
| | - Yi Zhong
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiaoxin Liu
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Shanshan Tian
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Jiahui Deng
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Xiao Lin
- Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Yanpin Bao
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China.
| | - Lin Lu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China; Peking University, Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Peking University, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University, Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430000, China.
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7
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Kim H, Zhu X, Zhao Y, Bell S, Gehrman P, Cohen D, Devanand D, Goldberg T, Lee S. Resting-State Functional Connectivity Changes in Older Adults with Sleep Disturbance and the Role of Amyloid Burden. RESEARCH SQUARE 2023:rs.3.rs-2547880. [PMID: 36798352 PMCID: PMC9934741 DOI: 10.21203/rs.3.rs-2547880/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β=0.11, P=0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.
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Affiliation(s)
| | - Xi Zhu
- Columbia University Medical Center
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8
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Lee S, Bijsterbosch JD, Almagro FA, Elliott L, McCarthy P, Taschler B, Sala-Llonch R, Beckmann CF, Duff EP, Smith SM, Douaud G. Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties. Neuroimage 2023; 265:119779. [PMID: 36462729 PMCID: PMC10933815 DOI: 10.1016/j.neuroimage.2022.119779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/31/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.
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Affiliation(s)
- Soojin Lee
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Pacific Parkinson's Research Institute, University of British Columbia, Canada.
| | - Janine D Bijsterbosch
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Mallinckrodt Institute of Radiology, Washington University Medical School, Washington University in St Louis, USA
| | - Fidel Alfaro Almagro
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Lloyd Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University (SFU), Canada
| | - Paul McCarthy
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Bernd Taschler
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Roser Sala-Llonch
- Department of Biomedicine, Institute of Neurosciences, University of Barcelona, Spain
| | - Christian F Beckmann
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Brain Sciences, Imperial College London, UK Dementia Research Institute, London UK
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Gwenaëlle Douaud
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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9
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Sheng W, Cui Q, Jiang K, Chen Y, Tang Q, Wang C, Fan Y, Guo J, Lu F, He Z, Chen H. Individual variation in brain network topology is linked to course of illness in major depressive disorder. Cereb Cortex 2022; 32:5301-5310. [PMID: 35152289 DOI: 10.1093/cercor/bhac015] [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: 09/05/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.
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Affiliation(s)
- Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Kexing Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuyan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yunshuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation, High Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, China
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10
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Carlson JM, Fang L, Koster EH, Andrzejewski JA, Gilbertson H, Elwell KA, Zuidema TR. Neuroplastic changes in anterior cingulate cortex gray matter volume and functional connectivity following attention bias modification in high trait anxious individuals. Biol Psychol 2022; 172:108353. [DOI: 10.1016/j.biopsycho.2022.108353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/11/2022] [Accepted: 05/09/2022] [Indexed: 12/21/2022]
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11
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Javaheripour N, Li M, Chand T, Krug A, Kircher T, Dannlowski U, Nenadić I, Hamilton JP, Sacchet MD, Gotlib IH, Walter H, Frodl T, Grimm S, Harrison BJ, Wolf CR, Olbrich S, van Wingen G, Pezawas L, Parker G, Hyett MP, Sämann PG, Hahn T, Steinsträter O, Jansen A, Yuksel D, Kämpe R, Davey CG, Meyer B, Bartova L, Croy I, Walter M, Wagner G. Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium. Transl Psychiatry 2021; 11:511. [PMID: 34620830 PMCID: PMC8497531 DOI: 10.1038/s41398-021-01619-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Bonn, 53127, Bonn, Germany
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149, Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Matthew D Sacchet
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Bldg. 420, Jordan Hall, Stanford, CA, 94305, USA
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Campus Charité Mitte, Charitéplatz 1, 10117, Berlin, Germany
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Leipzigerstr. 44, 39120, Magdeburg, Germany
| | - Simone Grimm
- Department of Psychiatry and Psychotherapy, CBF, Charité Universitätsmedizin Berlin, 12203, Berlin, Germany
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | - Christian Robert Wolf
- Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - Sebastian Olbrich
- Department of Psychiatry, Psychotherapy and Psychosomatic, University Zürich, Zürich, Switzerland
| | - Guido van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Lukas Pezawas
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gordon Parker
- School of Psychiatry, AGSM Building, University of New South Wales, Sydney, Australia
| | - Matthew P Hyett
- School of Psychological Sciences, University of Western Australia, Perth, Australia
| | | | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy & Marburg Center for Mind, Brain and Behavior - MCMBB, Philipps- Universität Marburg, Marburg, Germany
| | - Dilara Yuksel
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, USA
| | - Robin Kämpe
- Center for Social and Affective Neuroscience, Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | | | - Bernhard Meyer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Lucie Bartova
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Ilona Croy
- Department of Psychology, Friedrich Schiller University Jena, Jena, Germany
- Department of Psychotherapy and Psychosomatic Medicine, TU, Dresden, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Leipziger Str. 44, Building 65, 39120, Magdeburg, Germany
- Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Tuebingen, Calwerstraße 14, 72076, Tuebingen, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743, Jena, Germany.
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12
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Guha A, Yee CM, Heller W, Miller GA. Alterations in the default mode-salience network circuit provide a potential mechanism supporting negativity bias in depression. Psychophysiology 2021; 58:e13918. [PMID: 34403515 DOI: 10.1111/psyp.13918] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 11/28/2022]
Abstract
Aberrant effective connectivity between default mode (DMN) and salience (SAL) networks may support the tendency of depressed individuals to find it difficult to disengage from self-focused, negatively-biased thinking and may contribute to the onset and maintenance of depression. Assessment of effective connectivity, which can statistically characterize the direction of influence between regions within neural circuits, may provide new insights into the nature of DMN-SAL connectivity disruptions in depression. Functional magnetic resonance imaging (fMRI) was collected from 38 individuals with a history of major depression and 50 healthy comparison participants during completion of an emotion-word Stroop task. Activation within DMN and SAL networks and effective connectivity between DMN and SAL, assessed via Granger causality, were examined. Individuals with a history of depression exhibited greater overall network activation, greater directed connectivity from DMN to SAL, and less directed connectivity from SAL to DMN than healthy comparison participants during negative-word trials. Among individuals with a history of depression, greater DMN-to-SAL connectivity was associated with lower overall network activation and worse task performance during positive-word trials; this pattern was not observed among healthy participants. Present findings indicate that greater network activation and, specifically, influence of DMN on SAL, support negativity bias among previously depressed individuals.
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Affiliation(s)
- Anika Guha
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Cindy M Yee
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Wendy Heller
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA
| | - Gregory A Miller
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA
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13
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Differential patterns of dynamic functional connectivity variability in major depressive disorder treated with cognitive behavioral therapy. J Affect Disord 2021; 291:322-328. [PMID: 34082217 DOI: 10.1016/j.jad.2021.05.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/06/2021] [Accepted: 05/14/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Numerous studies have shown that major depressive disorder (MDD) is characterized by a range of impairments in emotional and cognitive functions that are closely related to abnormalities in brain structure and function. Cognitive behavioral therapy (CBT) can be used as treatment for mild to moderate MDD, which can assist with ameliorating the symptoms. Previous studies have assumed that the internal fluctuations throughout the entire scan are static. However, it has recently been suggested that the brain connectivity is dynamic and relative to continuous rhythmic activity. The effect of dynamic changes in CBT on MDD patients is unknown. METHODS Nineteen first-episode, unmedicated MDD patients and twenty-two healthy controls (HC) participated in the study. The patients received early CBT treatment once a week for 6 weeks. Symptom examinations and magnetic resonance imaging (MRI) scans were performed pre and post treatment. Degree centrality (DC) was used to investigate the whole-brain connectivity differences between patients with MDD and healthy controls, and sliding window correlation analysis was applied to investigate the dynamic changes of functional connectivity among MDD patients treated with CBT. The variance of dynamic functional connectivity (dFC) was calculated to evaluate the temporal variability along the time. RESULTS Patients with MDD showed abnormal DC in dorsolateral prefrontal cortex (dlPFC), insula and postcentral gyrus. Correlation analysis revealed that degree centrality of dlPFC was negatively correlated with the course of disease in patients with MDD. Results of dynamic functional connectivity showed that, compared to HC, MDD patients-remained excessively stable in dlPFC and precuneus connectivity, which is associated with emotional cognitive symptoms. After CBT, patients showed increased dFC variability in dlPFC and precuneus (p < 0.01, GRF corrected). CONCLUSION DLPFC plays an important role in pathophysiological mechanism of MDD. CBT helped patients suppress redundant thoughts and negative self-focus. As a connecting node, dlPFC participates in the mechanism of action of CBT.
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14
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Elton A, Faulkner ML, Robinson DL, Boettiger CA. Acute depletion of dopamine precursors in the human brain: effects on functional connectivity and alcohol attentional bias. Neuropsychopharmacology 2021; 46:1421-1431. [PMID: 33727642 PMCID: PMC8209208 DOI: 10.1038/s41386-021-00993-9] [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: 10/02/2020] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 02/07/2023]
Abstract
Individuals who abuse alcohol often show exaggerated attentional bias (AB) towards alcohol-related cues, which is thought to reflect reward conditioning processes. Rodent studies indicate that dopaminergic pathways play a key role in conditioned responses to reward- and alcohol-associated cues. However, investigation of the dopaminergic circuitry mediating this process in humans remains limited. We hypothesized that depletion of central dopamine levels in adult alcohol drinkers would attenuate AB and that these effects would be mediated by altered function in frontolimbic circuitry. Thirty-four male participants (22-38 years, including both social and heavy drinkers) underwent a two-session, placebo-controlled, double-blind dopamine precursor depletion procedure. At each visit, participants consumed either a balanced amino acid (control) beverage or an amino acid beverage lacking dopamine precursors (order counterbalanced), underwent resting-state fMRI, and completed behavioral testing on three AB tasks: an alcohol dot-probe task, an alcohol attentional blink task, and a task measuring AB to a reward-conditioned cue. Dopamine depletion significantly diminished AB in each behavioral task, with larger effects among subjects reporting higher levels of binge drinking. The depletion procedure significantly decreased resting-state functional connectivity among ventral tegmental area, striatum, amygdala, and prefrontal regions. Beverage-related AB decreases were mediated by decreases in functional connectivity between the fronto-insular cortex and striatum and, for alcohol AB only, between anterior cingulate cortex and amygdala. The results support a substantial role for dopamine in AB, and suggest specific dopamine-modulated functional connections between frontal, limbic, striatal, and brainstem regions mediate general reward AB versus alcohol AB.
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Affiliation(s)
- Amanda Elton
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
- Bowles Center for Alcohol Studies, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Monica L Faulkner
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Donita L Robinson
- Bowles Center for Alcohol Studies, University of North Carolina, Chapel Hill, NC, USA
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Charlotte A Boettiger
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA.
- Bowles Center for Alcohol Studies, University of North Carolina, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
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15
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Differential Alterations in Resting State Functional Connectivity Associated with Depressive Symptoms and Early Life Adversity. Brain Sci 2021; 11:brainsci11050591. [PMID: 34063232 PMCID: PMC8147478 DOI: 10.3390/brainsci11050591] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 12/19/2022] Open
Abstract
Depression and early life adversity (ELA) are associated with aberrant resting state functional connectivity (FC) of the default mode (DMN), salience (SN), and central executive networks (CEN). However, the specific and differential associations of depression and ELA with FC of these networks remain unclear. Applying a dimensional approach, here we analyzed associations of FC between major nodes of the DMN, SN, and CEN with severity of depressive symptoms and ELA defined as childhood abuse and neglect in a sample of 83 healthy and depressed subjects. Depressive symptoms were linked to increased FC within the SN and decreased FC of the SN with the DMN and CEN. Childhood abuse was associated with increased FC within the SN, whereas childhood neglect was associated with decreased FC within the SN and increased FC between the SN and the DMN. Our study thus provides evidence for differential associations of depressive symptoms and ELA with resting state FC and contributes to a clarification of previously contradictory findings. Specific FC abnormalities may underlie specific cognitive and emotional impairments. Future research should link specific clinical symptoms resulting from ELA to FC patterns thereby characterizing depression subtypes with specific neurobiological signatures.
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16
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Faustino B. Neurocognition applied to psychotherapy: A brief theoretical proposal based on the complex neural network perspective. APPLIED NEUROPSYCHOLOGY-ADULT 2021; 29:1626-1633. [PMID: 33645346 DOI: 10.1080/23279095.2021.1883615] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Impairments on executive functions, attention, memory, and self-perception had been systematically associated and document across several psychological disorders. Individuals with anxiety, depression, and schizophrenia spectrum disorders tend to manifest difficulties in response modulation/inhibition, cognitive flexibility, selective attention, updating autobiographical memory patterns, and maintenance in the sense of self and boundaries of others. Difficulties in cognitive, emotional, behavioral, and interpersonal functions in intrapsychic and interpsychic mental domains may be theoretically related to the maladaptive functioning of several neural networks. Frontal-Parietal Executive Network (FPEN), Salience Network (SN), Amygdaloid-Hippocampal Memory Network (AHMN), and Default Mode-Network (DMN) are four major complex neural pathways associated with these neurocognitive processes, sharing some neuroanatomical elements. These shared elements may support a latent factor that accounts for the common neurocognitive symptomatology across several psychopathological conditions. Based on these preliminary observations a new theoretical neurocognitive syndrome is hypothesized, potentially a productive target for clinical case conceptualization. Several articulations bettween neurocognition and psychotherapy are discussed and a new assessment measure is proposed.
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Affiliation(s)
- Bruno Faustino
- Faculdade de Psicologia, Universidade de Lisboa, Alameda da Universidade, Lisboa, Portugal
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17
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Yun JY, Kim YK. Phenotype Network and Brain Structural Covariance Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:3-18. [PMID: 33834391 DOI: 10.1007/978-981-33-6044-0_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea. .,Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea
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18
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Yan B, Xu X, Liu M, Zheng K, Liu J, Li J, Wei L, Zhang B, Lu H, Li B. Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach. Front Neurosci 2020; 14:191. [PMID: 32292322 PMCID: PMC7118554 DOI: 10.3389/fnins.2020.00191] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 02/24/2020] [Indexed: 01/14/2023] Open
Abstract
Introduction Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. Methods MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. Results The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. Conclusion The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
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Affiliation(s)
- Baoyu Yan
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Xiaopan Xu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Mengwan Liu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Kaizhong Zheng
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi'an, China
| | - Jianming Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Lei Wei
- Network Center, Air Force Medical University, Xi'an, China
| | - Binjie Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Hongbing Lu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
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