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Amos TJ, Guragai B, Rao Q, Li W, Jin Z, Zhang J, Li L. Task functional networks predict individual differences in the speed of emotional facial discrimination. Neuroimage 2024; 297:120715. [PMID: 38945182 DOI: 10.1016/j.neuroimage.2024.120715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/21/2024] [Accepted: 06/28/2024] [Indexed: 07/02/2024] Open
Abstract
Every individual experiences negative emotions, such as fear and anger, significantly influencing how external information is perceived and processed. With the gradual rise in brain-behavior relationship studies, analyses investigating individual differences in negative emotion processing and a more objective measure such as the response time (RT) remain unexplored. This study aims to address this gap by establishing that the individual differences in the speed of negative facial emotion discrimination can be predicted from whole-brain functional connectivity when participants were performing a face discrimination task. Employing the connectome predictive modeling (CPM) framework, we demonstrated this in the young healthy adult group from the Human Connectome Project-Young Adults (HCP-YA) dataset and the healthy group of the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset. We identified distinct network contributions in the adult and adolescent predictive models. The highest represented brain networks involved in the adult model predictions included representations from the motor, visual association, salience, and medial frontal networks. Conversely, the adolescent predictive models showed substantial contributions from the cerebellum-frontoparietal network interactions. Finally, we observed that despite the successful within-dataset prediction in healthy adults and adolescents, the predictive models failed in the cross-dataset generalization. In conclusion, our study shows that individual differences in the speed of emotional facial discrimination can be predicted in healthy adults and adolescent samples using their functional connectivity during negative facial emotion processing. Future research is needed in the derivation of more generalizable models.
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Affiliation(s)
- Toluwani Joan Amos
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Bishal Guragai
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Qianru Rao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Wenjuan Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China.
| | - Ling Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China.
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Wu YK, Su YA, Zhu LL, Yan C, Li JT, Lin JY, Chen J, Chen L, Li K, Stein DJ, Si TM. A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder. Transl Psychiatry 2024; 14:136. [PMID: 38443354 PMCID: PMC10915152 DOI: 10.1038/s41398-024-02838-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.
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Affiliation(s)
- Yan-Kun Wu
- 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), Beijing, 100191, China
| | - Yun-Ai Su
- 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), Beijing, 100191, China.
| | - Lin-Lin Zhu
- 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), Beijing, 100191, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Ji-Tao Li
- 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), Beijing, 100191, China
| | - Jing-Yu 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), Beijing, 100191, China
| | - JingXu Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Lin Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, 100101, China
| | - Dan J Stein
- Neuroscience Institute, Department of Psychiatry and Mental Health, South African Medical Research Council (SAMRC), Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Tian-Mei Si
- 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), Beijing, 100191, China.
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Wu YK, Su YA, Li L, Zhu LL, Li K, Li JT, Mitchell PB, Yan CG, Si TM. Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective. Psychol Med 2024; 54:763-774. [PMID: 38084586 DOI: 10.1017/s0033291723002453] [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: 03/05/2024]
Abstract
BACKGROUND Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. METHODS Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. RESULTS A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. CONCLUSIONS Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.
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Affiliation(s)
- Yan-Kun Wu
- 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), Beijing, China
| | - Yun-Ai Su
- 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), Beijing, China
| | - Le Li
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China
| | - Lin-Lin Zhu
- 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), Beijing, China
| | - Ke Li
- PLA Strategic Support Force Characteristic Medical Center, Beijing, China
| | - Ji-Tao Li
- 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), Beijing, China
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Sydney, Australia
- Black Dog Institute, Prince of Wales Hospital, Sydney, Australia
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Tian-Mei Si
- 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), Beijing, China
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Short-Term Head-Out Whole-Body Cold-Water Immersion Facilitates Positive Affect and Increases Interaction between Large-Scale Brain Networks. BIOLOGY 2023; 12:biology12020211. [PMID: 36829490 PMCID: PMC9953392 DOI: 10.3390/biology12020211] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023]
Abstract
An emerging body of evidence indicates that short-term immersion in cold water facilitates positive affect and reduces negative affect. However, the neural mechanisms underlying these effects remain largely unknown. For the first time, we employed functional magnetic resonance imaging (fMRI) to identify topological clusters of networks coupled with behavioural changes in positive and negative affect after a 5 min cold-water immersion. Perceived changes in positive affect were associated with feeling more active, alert, attentive, proud, and inspired, whilst changes in negative affect reflected reductions in distress and nervousness. The increase in positive affect was supported by a unique component of interacting networks, including the medial prefrontal node of the default mode network, a posterior parietal node of the frontoparietal network, and anterior cingulate and rostral prefrontal parts of the salience network and visual lateral network. This component emerged as a result of a focal effect confined to few connections. Changes in negative affect were associated with a distributed component of interacting networks at a reduced threshold. Affective changes after cold-water immersion occurred independently, supporting the bivalence model of affective processing. Interactions between large-scale networks linked to positive affect indicated the integrative effects of cold-water immersion on brain functioning.
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The Role of Analytical Flexibility in Determining Mental Health Biomarkers. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:316-318. [PMID: 36324651 PMCID: PMC9616365 DOI: 10.1016/j.bpsgos.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 07/25/2022] [Indexed: 11/07/2022] Open
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Tozzi L, Anene ET, Gotlib IH, Wintermark M, Kerr AB, Wu H, Seok D, Narr KL, Sheline YI, Whitfield-Gabrieli S, Williams LM. Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders. Neuroimage 2021; 245:118694. [PMID: 34732328 PMCID: PMC8727513 DOI: 10.1016/j.neuroimage.2021.118694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/11/2021] [Accepted: 10/29/2021] [Indexed: 12/31/2022] Open
Abstract
In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, "Dimensional connectomics of anxious misery" (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, "Human connectome Project for disordered emotional states" (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety. The third study, "Perturbation of the treatment resistant depression connectome by fast-acting therapies" (HCP-MDD), quantifies alterations of the structural and functional connectome as a result of three fast-acting interventions: electroconvulsive therapy, serial ketamine therapy, and total sleep deprivation. Finally, the fourth study, "Connectomes related to anxiety and depression in adolescents" (HCP-ADA), investigates developmental trajectories of subtypes of anxiety and depression in adolescence. The four projects use comparable and standardized Human Connectome Project magnetic resonance imaging (MRI) protocols, including structural MRI, diffusion-weighted MRI, and both task and resting state functional MRI. All four projects also conducted comprehensive and convergent clinical and neuropsychological assessments, including (but not limited to) demographic information, clinical diagnoses, symptoms of mood and anxiety disorders, negative and positive affect, cognitive function, and exposure to early life stress. The first round of analyses conducted in the four projects offered novel methods to investigate relations between functional connectomes and self-reports in large datasets, identified new functional correlates of symptoms of mood and anxiety disorders, characterized the trajectory of connectome-symptom profiles over time, and quantified the impact of novel treatments on aberrant connectivity. Taken together, the data obtained and reported by the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders describe a rich constellation of convergent biological, clinical, and behavioral phenotypes that span the peak ages for the onset of emotional disorders. These data are being prepared for open sharing with the scientific community following screens for quality by the Connectome Coordinating Facility (CCF). The CCF also plans to release data from all projects that have been pre-processed using identical state-of-the-art pipelines. The resultant dataset will give researchers the opportunity to pool complementary data across the four projects to study circuit dysfunctions that may underlie mood and anxiety disorders, to map cohesive relations among circuits and symptoms, and to probe how these relations change as a function of age and acute interventions. This large and combined dataset may also be ideal for using data-driven analytic approaches to inform neurobiological targets for future clinical trials and interventions focused on clinical or behavioral outcomes.
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Affiliation(s)
- Leonardo Tozzi
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Esther T Anene
- Psychiatry, Neurology, Radiology, University of Pennsylvania, Philadelphia PA, USA
| | | | | | - Adam B Kerr
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA; Electrical Engineering, Stanford University, CA, USA
| | - Hua Wu
- Electrical Engineering, Stanford University, CA, USA
| | - Darsol Seok
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA, USA
| | - Katherine L Narr
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
| | - Yvette I Sheline
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA.
| | | | - Leanne M Williams
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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Wang F, Jiang Z, Li X, Bu L, Ji Y. Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving. Front Hum Neurosci 2021; 15:713692. [PMID: 34759806 PMCID: PMC8573420 DOI: 10.3389/fnhum.2021.713692] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/09/2021] [Indexed: 12/04/2022] Open
Abstract
As a complex cognitive activity, knowledge transfer is mostly correlated to cognitive processes such as working memory, behavior control, and decision-making in the human brain while engineering problem-solving. It is crucial to explain how the alteration of the functional brain network occurs and how to express it, which causes the alteration of the cognitive structure of knowledge transfer. However, the neurophysiological mechanisms of knowledge transfer are rarely considered in existing studies. Thus, this study proposed functional connectivity (FC) to describe and evaluate the dynamic brain network of knowledge transfer while engineering problem-solving. In this study, we adopted the modified Wisconsin Card-Sorting Test (M-WCST) reported in the literature. The neural activation of the prefrontal cortex was continuously recorded for 31 participants using functional near-infrared spectroscopy (fNIRS). Concretely, we discussed the prior cognitive level, knowledge transfer distance, and transfer performance impacting the wavelet amplitude and wavelet phase coherence. The paired t-test results showed that the prior cognitive level and transfer distance significantly impact FC. The Pearson correlation coefficient showed that both wavelet amplitude and phase coherence are significantly correlated to the cognitive function of the prefrontal cortex. Therefore, brain FC is an available method to evaluate cognitive structure alteration in knowledge transfer. We also discussed why the dorsolateral prefrontal cortex (DLPFC) and occipital face area (OFA) distinguish themselves from the other brain areas in the M-WCST experiment. As an exploratory study in NeuroManagement, these findings may provide neurophysiological evidence about the functional brain network of knowledge transfer while engineering problem-solving.
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Affiliation(s)
- Fuhua Wang
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Zuhua Jiang
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Li
- College of Mechanical Engineering, Donghua University, Shanghai, China.,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Lingguo Bu
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.,School of Software, Shandong University, Jinan, China
| | - Yongjun Ji
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
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