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Zhang X, Pines A, Stetz P, Goldstein-Piekarski AN, Xiao L, Lv N, Tozzi L, Lavori PW, Snowden MB, Venditti EM, Smyth JM, Suppes T, Ajilore O, Ma J, Williams LM. Adaptive cognitive control circuit changes associated with problem-solving ability and depression symptom outcomes over 24 months. Sci Transl Med 2024; 16:eadh3172. [PMID: 39231241 DOI: 10.1126/scitranslmed.adh3172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/06/2024] [Accepted: 08/14/2024] [Indexed: 09/06/2024]
Abstract
Mechanistically targeted behavioral interventions are a much-needed strategy for improving outcomes in depression, especially for vulnerable populations with comorbidities such as obesity. Such interventions may change behavior and outcome by changing underlying neural circuit function. However, it is unknown how these circuit-level modifications unfold over intervention and how individual differences in early circuit-level modifications may explain the heterogeneity of treatment effects. We addressed this need within a clinical trial of problem-solving therapy for participants with depression symptoms and comorbid obesity, focusing on the cognitive control circuit as a putative neural mechanism of action. Functional magnetic resonance imaging was applied to measure the cognitive control circuit activity at five time points over 24 months. Compared with participants who received usual care, those receiving problem-solving therapy showed that attenuations in cognitive control circuit activity were associated with enhanced problem-solving ability, which suggests that this circuit plays a key role in the mechanisms of problem-solving therapy. Attenuations in circuit activity were also associated with improved depression symptoms. Changes in cognitive control circuit activity at 2 months better predicted changes in problem-solving ability and depression symptoms at 6, 12, and 24 months, with predictive improvements ranging from 17.8 to 104.0%, exceeding baseline demographic and symptom characteristics. Our findings suggest that targeting the circuit mechanism of action could enhance the prediction of treatment outcomes, warranting future model refinement and improvement to pave the way for its clinical application.
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Affiliation(s)
- Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Patrick Stetz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Andrea N Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Lan Xiao
- Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA 94304, USA
| | - Nan Lv
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60608, USA
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Philip W Lavori
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Mark B Snowden
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98104, USA
| | - Elizabeth M Venditti
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Joshua M Smyth
- Department of Psychology, Ohio State University, Columbus, OH 43210, USA
| | - Trisha Suppes
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Jun Ma
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60608, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
- Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
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2
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Grogans SE, Hur J, Barstead MG, Anderson AS, Islam S, Kim HC, Kuhn M, Tillman RM, Fox AS, Smith JF, DeYoung KA, Shackman AJ. Neuroticism/Negative Emotionality Is Associated with Increased Reactivity to Uncertain Threat in the Bed Nucleus of the Stria Terminalis, Not the Amygdala. J Neurosci 2024; 44:e1868232024. [PMID: 39009438 PMCID: PMC11308352 DOI: 10.1523/jneurosci.1868-23.2024] [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: 09/29/2023] [Revised: 06/01/2024] [Accepted: 06/03/2024] [Indexed: 07/17/2024] Open
Abstract
Neuroticism/negative emotionality (N/NE)-the tendency to experience anxiety, fear, and other negative emotions-is a fundamental dimension of temperament with profound consequences for health, wealth, and well-being. Elevated N/NE is associated with a panoply of adverse outcomes, from reduced socioeconomic attainment to psychiatric illness. Animal research suggests that N/NE reflects heightened reactivity to uncertain threat in the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce), but the relevance of these discoveries to humans has remained unclear. Here we used a novel combination of psychometric, psychophysiological, and neuroimaging approaches to test this hypothesis in an ethnoracially diverse, sex-balanced sample of 220 emerging adults selectively recruited to encompass a broad spectrum of N/NE. Cross-validated robust-regression analyses demonstrated that N/NE is preferentially associated with heightened BST activation during the uncertain anticipation of a genuinely distressing threat (aversive multimodal stimulation), whereas N/NE was unrelated to BST activation during certain-threat anticipation, Ce activation during either type of threat anticipation, or BST/Ce reactivity to threat-related faces. It is often assumed that different threat paradigms are interchangeable assays of individual differences in brain function, yet this has rarely been tested. Our results revealed negligible associations between BST/Ce reactivity to the anticipation of threat and the presentation of threat-related faces, indicating that the two tasks are nonfungible. These observations provide a framework for conceptualizing emotional traits and disorders; for guiding the design and interpretation of biobank and other neuroimaging studies of psychiatric risk, disease, and treatment; and for refining mechanistic research.
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Affiliation(s)
- Shannon E Grogans
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Juyoen Hur
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea
| | | | - Allegra S Anderson
- Department of Psychological Sciences, Vanderbilt University, Nashville, Tennessee 37240
| | - Samiha Islam
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Hyung Cho Kim
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland 20742
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, Massachusetts 02478
| | | | - Andrew S Fox
- Department of Psychology, University of California, Davis, California 95616
- California National Primate Research Center, University of California, Davis, California 95616
| | - Jason F Smith
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Kathryn A DeYoung
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Alexander J Shackman
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland 20742
- Maryland Neuroimaging Center, University of Maryland, College Park, Maryland 20742
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3
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Tozzi L, Zhang X, Pines A, Olmsted AM, Zhai ES, Anene ET, Chesnut M, Holt-Gosselin B, Chang S, Stetz PC, Ramirez CA, Hack LM, Korgaonkar MS, Wintermark M, Gotlib IH, Ma J, Williams LM. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med 2024; 30:2076-2087. [PMID: 38886626 PMCID: PMC11271415 DOI: 10.1038/s41591-024-03057-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 05/09/2024] [Indexed: 06/20/2024]
Abstract
There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.
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Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Alisa M Olmsted
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Emily S Zhai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Esther T Anene
- Department of Counseling and Clinical Psychology, Teacher's College, Columbia University, New York, NY, USA
| | - Megan Chesnut
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Bailey Holt-Gosselin
- Interdepartmental Neuroscience Graduate Program, Yale University School of Medicine, New Haven, CT, USA
| | - Sarah Chang
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Patrick C Stetz
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Carolina A Ramirez
- Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia
- Department of Radiology, Westmead Hospital, Western Sydney Local Health District, Westmead, New South Wales, Australia
| | - Max Wintermark
- Department of Neuroradiology, the University of Texas MD Anderson Center, Houston, TX, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jun Ma
- Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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4
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Ganesan S, Misaki M, Zalesky A, Tsuchiyagaito A. Functional brain network dynamics of brooding in depression: insights from real-time fMRI neurofeedback. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.05.24306889. [PMID: 38766116 PMCID: PMC11100839 DOI: 10.1101/2024.05.05.24306889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD. Methods We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n=18/18, HC-active/sham: n=13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B). Results Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r=-0.5, p= 1.7E-3, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z=-2.09, p=0.037). Limitations The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback. Conclusion We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.
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Affiliation(s)
- Saampras Ganesan
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
- Contemplative Studies Centre, Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Medical School, Carlton, Victoria 3053, Australia
- Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, USA
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
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5
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Williams LM, Carpenter WT, Carretta C, Papanastasiou E, Vaidyanathan U. Precision psychiatry and Research Domain Criteria: Implications for clinical trials and future practice. CNS Spectr 2024; 29:26-39. [PMID: 37675453 DOI: 10.1017/s1092852923002420] [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: 09/08/2023]
Abstract
Psychiatric disorders are associated with significant social and economic burdens, many of which are related to issues with current diagnosis and treatments. The coronavirus (COVID-19) pandemic is estimated to have increased the prevalence and burden of major depressive and anxiety disorders, indicating an urgent need to strengthen mental health systems globally. To date, current approaches adopted in drug discovery and development for psychiatric disorders have been relatively unsuccessful. Precision psychiatry aims to tailor healthcare more closely to the needs of individual patients and, when informed by neuroscience, can offer the opportunity to improve the accuracy of disease classification, treatment decisions, and prevention efforts. In this review, we highlight the growing global interest in precision psychiatry and the potential for the National Institute of Health-devised Research Domain Criteria (RDoC) to facilitate the implementation of transdiagnostic and improved treatment approaches. The need for current psychiatric nosology to evolve with recent scientific advancements and increase awareness in emerging investigators/clinicians of the value of this approach is essential. Finally, we examine current challenges and future opportunities of adopting the RDoC-associated translational and transdiagnostic approaches in clinical studies, acknowledging that the strength of RDoC is that they form a dynamic framework of guiding principles that is intended to evolve continuously with scientific developments into the future. A collaborative approach that recruits expertise from multiple disciplines, while also considering the patient perspective, is needed to pave the way for precision psychiatry that can improve the prognosis and quality of life of psychiatric patients.
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Affiliation(s)
- Leanne M Williams
- Department of 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
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Evangelos Papanastasiou
- Boehringer Ingelheim Pharma GmbH & Co, Ingelheim am Rhein, Rhineland-Palatinate, Germany
- HMNC Holding GmbH, Wilhelm-Wagenfeld-Strasse 20, 80807Munich, Bavaria, Germany
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6
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Jiao YC, Chang J, Liu C, Zhou SY, Ji Y, Meng Y. Factors influencing the help-seeking behavior in patients with mild cognitive impairment: a qualitative study. BMC Health Serv Res 2023; 23:1345. [PMID: 38042819 PMCID: PMC10693691 DOI: 10.1186/s12913-023-10281-5] [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: 04/09/2023] [Accepted: 11/06/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND The early diagnosis and intervention of mild cognitive impairment (MCI) patients is expected to delay the progression of AD. Delayed treatment will lead to MCI patients missing the best intervention expectation. At present, the medical help-seeking behavior of this group is not optimistic. This study aimed to explore influencing factors of help-seeking behavior among patients with MCI in China based on the help-seeking behavior model. METHODS Twenty-two patients with MCI were recruited to participate in semi-structured interviews via purposeful sampling with a qualitative, descriptive design. Data were analyzed by qualitative content analysis. RESULTS The study revealed the main influencing factors of help-seeking behavior among MCI patients in China included perceived disease threat, symptom attribution, disease knowledge, use of cognitive compensation strategies, sense of foreseeable burden, social support, economic condition, and accessibility of medical service. CONCLUSIONS The help-seeking behavior of patients with MCI is affected by multiple factors. There are some key factors in different stages of the help-seeking process. Healthcare providers can utilize these factors to design targeted interventions for promoting early help-seeking of patients with MCI.
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Affiliation(s)
- Yu-Chen Jiao
- School of Nursing, Nanjing Medical University, Nanjing, China
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Chang
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Chang Liu
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Shi-Yu Zhou
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Yan Ji
- School of Nursing, Nanjing Medical University, Nanjing, China.
| | - Yao Meng
- School of Nursing, Nanjing Medical University, Nanjing, China.
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7
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Jia F, Chen X, Du X, Tang Z, Ma X, Ning T, Zou S, Zuo S, Li H, Cui S, Deng Z, Fu J, Fu X, Huang Y, Li X, Lian T, Liao Y, Liu L, Lu B, Wang Y, Wang Y, Wang Z, Ye G, Zhang X, Zhu H, Quan C, Sun H, Yan C, Liu Y. Aberrant degree centrality profiles during rumination in major depressive disorder. Hum Brain Mapp 2023; 44:6245-6257. [PMID: 37837649 PMCID: PMC10619375 DOI: 10.1002/hbm.26510] [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: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/16/2023] Open
Abstract
Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.
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Affiliation(s)
- Feng‐Nan Jia
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao Chen
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Research InstituteCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Xiang‐Dong Du
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Zhen Tang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Yun Ma
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Tian‐Tian Ning
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Si‐Yun Zou
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Shang‐Fu Zuo
- Boston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Hui‐Xian Li
- The Third Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shi‐Xian Cui
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Zhao‐Yu Deng
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Jia‐Lin Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Qian Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yue‐Xiang Huang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xue‐Ying Li
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Tao Lian
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yi‐Fan Liao
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Li‐Li Liu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Bin Lu
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yan Wang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yu‐Wei Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Zi‐Han Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Gang Ye
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xin‐Zhu Zhang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Hong‐Liang Zhu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chuan‐Sheng Quan
- Department of PsychologyZhangjiagang Fourth People's HospitalZhangjiagangJiangsuChina
| | - Hong‐Yan Sun
- Department of RadiologySuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chao‐Gan Yan
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Yan‐Song Liu
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
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8
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Tuzhilina E, Tozzi L, Hastie T. Canonical correlation analysis in high dimensions with structured regularization. STAT MODEL 2023; 23:203-227. [PMID: 37334164 PMCID: PMC10274416 DOI: 10.1177/1471082x211041033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.
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Affiliation(s)
- Elena Tuzhilina
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Trevor Hastie
- Department of Statistics, Stanford University, Stanford, CA, USA
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9
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Zwir I, Arnedo J, Mesa A, Del Val C, de Erausquin GA, Cloninger CR. Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis. Mol Psychiatry 2023; 28:2238-2253. [PMID: 37015979 PMCID: PMC10611583 DOI: 10.1038/s41380-023-02039-6] [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: 11/05/2022] [Revised: 03/11/2023] [Accepted: 03/15/2023] [Indexed: 04/06/2023]
Abstract
The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).
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Affiliation(s)
- Igor Zwir
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- University of Granada, Department of Computer Science, Granada, Spain
- University of Texas, Rio Grande Valley School of Medicine, Institute of Neuroscience, Harlingen, TX, USA
| | - Javier Arnedo
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- University of Granada, Department of Computer Science, Granada, Spain
| | - Alberto Mesa
- University of Granada, Department of Computer Science, Granada, Spain
| | - Coral Del Val
- University of Granada, Department of Computer Science, Granada, Spain
| | - Gabriel A de Erausquin
- University of Texas, Long School of Medicine, Department of Neurology, San Antonio, TX, USA
- Laboratory of Brain Development, Modulation and Repair, Glenn Biggs Institute of Alzheimer's & Neurodegenerative Disorders, San Antonio, TX, USA
| | - C Robert Cloninger
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA.
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Dennison JB, Tepfer LJ, Smith DV. Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder. Hum Brain Mapp 2023; 44:2905-2920. [PMID: 36880638 PMCID: PMC10089091 DOI: 10.1002/hbm.26254] [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: 08/08/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 03/08/2023] Open
Abstract
Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting-state data, often ignore sources of between-subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)-which explicitly incorporates between-subject variability-to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward-based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task-based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity.
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Affiliation(s)
- Jeff B Dennison
- Department of Psychology & Neuroscience, Temple University, Philadelphia, Pennsylvania, USA
| | - Lindsey J Tepfer
- Department of Psychological and Brain Science, Dartmouth University, Hanover, New Hampshire, USA
| | - David V Smith
- Department of Psychology & Neuroscience, Temple University, Philadelphia, Pennsylvania, USA
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11
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Kim BH, Kim JJ, Oh J, Kim SH, Han C, Jeong HG, Lee MS, Kim J. Feasibility of the virtual reality-based assessments in patients with panic disorder. Front Psychiatry 2023; 14:1084255. [PMID: 36761868 PMCID: PMC9902717 DOI: 10.3389/fpsyt.2023.1084255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 01/06/2023] [Indexed: 01/25/2023] Open
Abstract
Introduction Recurrences and diagnostic instability of panic disorder (PD) are common and have a negative effect on its long-term course. Developing a novel assessment tool for anxiety that can be used in a multimodal approach may improve these problems in panic disorder patients. This study assessed the feasibility of virtual reality-based assessment in panic disorder (VRA-PD). Methods Twenty-five patients with PD (ANX group) and 28 healthy adults (CON group) participated in the study. VRA-PD consisted of four modules based on the key components of cognitive behavior therapy for an anxiety disorder: "Baseline evaluation module" (M0), "Daily environment exposure module" (M1), "Relaxation module" (M2), and "Interoceptive exposure module" (M3). Multiple evaluations, including self-rating anxiety scores (AS) and physiological responses [heart rate variability (HRV) index], were performed in three steps at M1, M2, and M3, and once at M0. Comparisons between patients with PD and healthy controls, factor analysis of variables in VRA-PD, changes in responses within modules, and correlation analysis between variables in VRA-PD and anxiety symptoms assessed by psychological scales were performed. Results All participants completed the VRA-PD without discontinuation. The ANX group reported significantly higher AS for all steps and a smaller HRV index in M1 (steps 1 and 2) and M2 (step 1). Repeated-measures analysis of covariance (ANCOVA) revealed significant interaction effects for AS in M1 (F = 4.09, p = 0.02) and M2 (F = 4.20, p = 0.02), and HRV index in M2 (F = 16.22, p < 0.001) and M3 (F = 21.22, p = 0.02). The HRV index only indicated a good model fit for the three-factor model, reflecting the construct of the VRA-PD. Both AS and HRV indexes were significantly correlated with anxiety and depression symptoms. Discussion The current study provides preliminary evidence that the VRA-PD could be a valid anxiety behavior assessment tool.
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Affiliation(s)
- Byung-Hoon Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Jin Kim
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jooyoung Oh
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute of Behavioral Sciences in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung-Hyun Kim
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Changsu Han
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hyun-Ghang Jeong
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Moon-Soo Lee
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Department of Life Sciences, Korea University, Seoul, Republic of Korea
| | - Junhyung Kim
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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12
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Blair NOP, Cohen AD, Ward BD, Claesges SA, Agarwal M, Wang Y, Reynolds CF, Goveas JS. Ventral striatal subregional dysfunction in late-life grief: Relationships with yearning and depressive symptoms. J Psychiatr Res 2022; 156:252-260. [PMID: 36272343 DOI: 10.1016/j.jpsychires.2022.10.031] [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: 06/09/2022] [Revised: 09/07/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Bereaved older adults experiencing high grief in the first year after an attachment loss is at increased risk for prolonged grief disorder (PGD) via unknown mechanisms. Yearning, a core grief symptom, is linked to the ventral striatal (VS) brain function, but the role of this neuronal system in late-life grief is poorly understood. As a first step, we examined the VS subregional abnormalities associated with multidimensional symptoms in bereaved elders during the first year post-loss. Sixty-five bereaved elders completed clinical assessments within 13 months post-loss. Ventral caudate (VCau) and nucleus accumbens (NAcc) functional connectivity (FC) was assessed using seed-based resting-state functional MRI. VCau and NAcc FC differences between high (inventory of complicated grief [ICG] score≥30; n = 35) and low (ICG score<30; n = 30) grief, and the relationships between ventral striatal subregional FC and clinical measures (yearning and depressive symptoms) were assessed after covariate adjustments (α < 0.05; 3dClustSim corrected). Relative to low grief participants, those with high grief showed higher FC between VCau and the medial prefrontal, orbitofrontal, and subgenual cingulate cortices. VCau FC abnormalities positively correlated with yearning (r2 = 0.24, p < 0.001). In contrast, FC between VCau and temporoparietal junction negatively correlated with depressive symptoms, a commonly co-occurring symptom (r2 = 0.37, p < 0.001). The FC between NAcc and insula/striatum positively correlated with yearning (r2 = 0.35, p < 0.001); no other NAcc FC findings were seen in the full sample. In women, higher FC between the NAcc and bilateral posterior cingulate, precuneus, and visual areas were found in those with high, relative to low grief symptoms. Distinct VS subregional abnormalities associate with yearning and depressive symptoms in bereaved elders. Whether ventral striatal dysfunction correlates with PGD development and/or worsening depression remains to be elucidated.
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Affiliation(s)
- Nutta-On P Blair
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Alexander D Cohen
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - B Douglas Ward
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Stacy A Claesges
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Mohit Agarwal
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Charles F Reynolds
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Joseph S Goveas
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA; Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA.
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Hu D, Liu J, Liu G, Hu S, Li Z, Wei Y, Zhang N, Wu R, Peng Y. Altered brain activity and functional networks in school-age boys with severe haemophilia A: A resting-state functional magnetic resonance imaging study. Haemophilia 2022; 28:578-587. [PMID: 35505587 DOI: 10.1111/hae.14567] [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: 05/06/2020] [Revised: 03/11/2022] [Accepted: 03/30/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION Microstructural alterations of brain structure in haemophilic boys were found in our previous study. AIM We investigated alterations of brain function in school-age boys with severe haemophilia A (HA) with resting-state functional magnetic resonance imaging (rs-fMRI). METHODS We obtained rs-fMRI scans from 24 boys with HA and 25 demographically matched healthy children. Spontaneous brain activity parameters were calculated. Graph theoretical analyses on rs-fMRI data at the global and regional levels were performed. Two-sample t tests were used to analyze differences, and correlation analyses identified relationships between altered neural properties and psychological characteristics. RESULTS Children with severe HA showed small-worldness organization but with an increased efficiency and compactness in functional segregation. The whole brain showed an overtight connection pattern. At the regional level, significantly increased nodal efficiency in the salience network (SN), default mode network (DMN) and executive control network was found. Social Anxiety Scale for Children (SASC) scores were positively correlated with these alterations. Spontaneous brain activity alterations in regions including the cerebellum, frontal gyrus (orbital part), temporal gyrus and thalamus were observed; some of these regions have been closely related to social anxiety and family or social support. CONCLUSION Our study is the first to evaluate the neurological functional changes in school-age boys with severe HA. Disruptions in topographic characteristics and abnormal activity were closely related to social conditions. These data could help us to understand early neurological alterations in haemophilic children, improve the traditional view of family support and strengthen normal school life at an early stage.
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Affiliation(s)
- Di Hu
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Department of Radiology, Beijing, China
| | - Jingran Liu
- Beijing Children's Hospital, National Center for Children's Health, Neurological Center, Capital Medical University, Beijing, China
| | - Guoqing Liu
- Beijing Children's Hospital, National Center for Children's Health, Hematology Center, Capital Medical University, Beijing, China
| | - Shasha Hu
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Department of Radiology, Beijing, China
| | - Zekun Li
- Beijing Children's Hospital, National Center for Children's Health, Hematology Center, Capital Medical University, Beijing, China
| | - Yunyun Wei
- Beijing Children's Hospital, National Center for Children's Health, Hematology Center, Capital Medical University, Beijing, China
| | - Ningning Zhang
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Department of Radiology, Beijing, China
| | - Runhui Wu
- Beijing Children's Hospital, National Center for Children's Health, Neurological Center, Capital Medical University, Beijing, China
| | - Yun Peng
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Department of Radiology, Beijing, China
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14
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Zeng R, Lv J, Wang H, Zhou L, Barnett M, Calamante F, Wang C. FOD-Net: A deep learning method for fiber orientation distribution angular super resolution. Med Image Anal 2022; 79:102431. [PMID: 35397471 DOI: 10.1016/j.media.2022.102431] [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: 03/22/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm2) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments. Our code is freely available at https://github.com/ruizengalways/FOD-Net.
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Affiliation(s)
- Rui Zeng
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - Jinglei Lv
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China
| | - Luping Zhou
- School of Computer Science, The University of Sydney, Sydney 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Imaging, The University of Sydney, Sydney 2050, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia.
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15
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Are Affective Temperaments, Emotional Abuse, and Neglect Involved in Mentalization Abilities in Patients With Psychiatric Disorders? J Nerv Ment Dis 2022; 210:276-281. [PMID: 34710896 DOI: 10.1097/nmd.0000000000001440] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Patients who have experienced emotional abuse and neglect often develop psychiatric disorders in adulthood. However, whether emotional abuse, neglect, and mentalization abilities relate to one another and the role of possible mediators of this relationship in psychiatric patients are still unknown. We evaluated the potential role of affective temperament as a mediator of the relationship between emotional abuse and neglect and mentalization. We performed a cross-sectional study of 252 adult psychiatric inpatients. The Childhood Trauma Questionnaire, Mentalization Questionnaire, and Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire (TEMPS-A) were administered. Results showed a significant indirect effect of emotional abuse and neglect on scores on the Mentalization Questionnaire through the TEMPS-A (b = 0.25, 95% confidence interval [0.143-0.375]), demonstrating that affective temperament mediates the relationship among emotional abuse, neglect, and mentalization impairment in psychiatric patients. A careful evaluation of mentalization abilities in patients with psychiatric disorders and who have a history of emotional abuse and neglect is necessary for a better understanding of psychopathology and for the choice of therapeutic strategies.
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16
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Keller AS, Ling R, Williams LM. Spatial attention impairments are characterized by specific electro-encephalographic correlates and partially mediate the association between early life stress and anxiety. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2022; 22:414-428. [PMID: 34850363 DOI: 10.3758/s13415-021-00963-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 06/13/2023]
Abstract
Although impaired attention is a diagnostic feature of anxiety disorders, we lack an understanding of which aspects of attention are impaired, the neurobiological basis of these impairments, and the contribution of stressors. To address these gaps in knowledge, we developed and tested behavioral tasks designed to parse the subdomains of attention impairments associated with anxiety symptoms and used electro-encephalographic (EEG) recordings to probe the neural basis of attentional performance. Participants were n = 55 individuals aged 18-35 with mild-to-moderate mood and anxiety symptoms. We also assessed stressful life events that may impact mental health and attention abilities, including stressors that occurred in early life before age 18 years. Severity of anxiety was found to be specifically associated with impairments in spatial attention but not feature-based attention. These impairments in spatial attention also partially mediated the association between early-life stressors and anxiety symptoms. Impairments in spatial selective attention were associated with decreased posterior alpha oscillations in EEG recordings in a subsample of participants, whereas spatial divided attention impairments were associated with decreased frontocentral theta oscillations. Our results provide a thorough characterization of attention impairments associated with anxiety, their EEG correlates, and the impact of stressors both in early life and adulthood.
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Affiliation(s)
- Arielle S Keller
- Graduate Program in Neurosciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94134, USA
| | - Ruth Ling
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94134, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Stanford, CA, 94134, USA.
- MIRECC, VA Palo Alto Health Care System, Palo Alto, CA, USA.
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17
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Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum Brain Mapp 2022; 43:816-832. [PMID: 34708477 PMCID: PMC8720192 DOI: 10.1002/hbm.25690] [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: 04/02/2021] [Revised: 09/10/2021] [Accepted: 10/11/2021] [Indexed: 11/09/2022] Open
Abstract
The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.
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Affiliation(s)
- Rosie K Dutt
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kayla Hannon
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Ty O Easley
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Joseph C Griffis
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Wei Zhang
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Janine D Bijsterbosch
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
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18
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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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19
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Holt-Gosselin B, Tozzi L, Ramirez CA, Gotlib IH, Williams LM. Coping Strategies, Neural Structure, and Depression and Anxiety During the COVID-19 Pandemic: A Longitudinal Study in a Naturalistic Sample Spanning Clinical Diagnoses and Subclinical Symptoms. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:261-271. [PMID: 34604834 PMCID: PMC8479487 DOI: 10.1016/j.bpsgos.2021.06.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Although the COVID-19 pandemic has been shown to worsen anxiety and depression symptoms, we do not understand which behavioral and neural factors may mitigate this impact. To address this gap, we assessed whether adaptive and maladaptive coping strategies affect symptom trajectory during the pandemic. We also examined whether pre-pandemic integrity of brain regions implicated in depression and anxiety affect pandemic symptoms. METHODS In a naturalistic sample of 169 adults (66.9% female; age 19-74 years) spanning psychiatric diagnoses and subclinical symptoms, we assessed anhedonia, tension, and anxious arousal symptoms using validated components (21-item Depression, Anxiety, and Stress Scale), coping strategies (Brief-Coping Orientation to Problems Experienced), and gray matter volume (amygdala) and cortical thickness (hippocampus, insula, anterior cingulate cortex) from magnetic resonance imaging T1-weighted scans. We conducted general linear mixed-effects models to test preregistered hypotheses that 1) maladaptive coping pre-pandemic and 2) lower structural integrity pre-pandemic would predict more severe pandemic symptoms; and 3) coping would interact with neural structure to predict pandemic symptoms. RESULTS Greater use of maladaptive coping strategies was associated with more severe anxious arousal symptoms during the pandemic (p = .011, false discovery rate-corrected p [p FDR] = .035), specifically less self-distraction (p = .014, p FDR = .042) and greater self-blame (p = .002, p FDR = .012). Reduced insula thickness pre-pandemic predicted more severe anxious arousal symptoms (p = .001, p FDR = .027). Self-distraction interacted with amygdala volume to predict anhedonia symptoms (p = .005, p FDR = .020). CONCLUSIONS Maladaptive coping strategies and structural variation in brain regions may influence clinical symptoms during a prolonged stressful event (e.g., COVID-19 pandemic). Future studies that identify behavioral and neural factors implicated in responses to global health crises are warranted for fostering resilience.
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Affiliation(s)
- Bailey Holt-Gosselin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Interdepartmental Neuroscience Graduate Program, Yale University, New Haven, Connecticut
| | - Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Carolina A. Ramirez
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, California
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
- Mental Illness Research, Education and Clinical Center, Palo Alto VA Healthcare System, Palo Alto, California
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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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21
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Hypostability in the default mode network and hyperstability in the frontoparietal control network of dynamic functional architecture during rumination. Neuroimage 2021; 241:118427. [PMID: 34311069 DOI: 10.1016/j.neuroimage.2021.118427] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/19/2021] [Accepted: 07/22/2021] [Indexed: 11/22/2022] Open
Abstract
The neural underpinnings of rumination can be characterized by its specific dynamic nature. Temporal stability is the stable and consistent representation of information by a distributed neural activity and connectivity pattern across brain regions. Although stability is a key feature of the brain's functional architecture, its profiles supporting rumination remain elusive. We characterized the stability of the whole-brain functional architecture during an induced, continuous rumination state and compared it with a well-constrained distraction state as the control condition in a group of healthy participants (N = 40). We further examined the relationship between stability in regions showing a significant effect on the rumination vs. distraction contrast and rumination traits. The variability of dynamic functional connectivities (FCs) among these regions was also explored to determine the potential coupling regions that drove the altered stability pattern during rumination. The results showed that rumination was characterized by a similar but altered stability profile compared with distraction and resting states. Comparison between rumination and distraction revealed that key regions of the default mode network (DMN), such as the medial prefrontal cortex (MPFC) and bilateral parahippocampal gyrus (PHG), which showed decreased stability while frontoparietal control network (FPCN) regions, including the inferior parietal lobule (IPL) and dorsal lateral prefrontal cortex (DLPFC), showed significantly enhanced stability in rumination compared with distraction. Additionally, stability in the MPFC and IPL was related to individual differences in rumination traits. Exploratory analysis of the variation in dynamic FCs suggested that higher stability in the IPL may be related to its less variable FCs with the PHG. Together, these findings implicated that rumination may be supported by the dissociated dynamic nature of hypostability in the DMN and hyperstability in the FPCN.
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22
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Keshmiri S. Conditional Entropy: A Potential Digital Marker for Stress. ENTROPY (BASEL, SWITZERLAND) 2021; 23:286. [PMID: 33652891 PMCID: PMC7996836 DOI: 10.3390/e23030286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
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Affiliation(s)
- Soheil Keshmiri
- Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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23
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Xu T, Gu Q, Zhao Q, Wang P, Liu Q, Fan Q, Chen J, Wang Z. Impaired cortico-striatal functional connectivity is related to trait impulsivity in unmedicated patients with obsessive-compulsive disorder. J Affect Disord 2021; 281:899-907. [PMID: 33229018 DOI: 10.1016/j.jad.2020.11.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/23/2020] [Accepted: 11/07/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES Convergent evidence has demonstrated that trait impulsivity, a key feature in obsessive-compulsive disorder (OCD), involves dysregulated frontal-striatal circuits. The present study aims to explore relationships between frontal-striatal circuits, trait impulsivity, and obsessive-compulsive symptoms. METHODS Thirty-six unmedicated patients with OCD and 50 healthy controls (HCs) matched for age, sex, and years of education underwent a magnetic resonance imaging (MRI) procedure. Voxel-wise statistical parametric analysis was used to investigate the differences in resting-state functional connectivity between brain regions functionally connected to six pairs of a-priori defined striatal seed regions, between patients with OCD and HCs. Associations between frontal-striatal connectivity and both trait impulsivity and symptom severity of OCD were analyzed. RESULTS The results showed altered striatal functional connectivity in OCD group compared to HCs, including increased connectivity of dorsal caudate (DC)-orbitofrontal cortex (OFC), ventral striatum (VS)-OFC, VS-medial prefrontal cortex, and putamen-sensorimotor area, and decreased functional connectivity of DC-anterior cingulate cortex (ACC), putamen-ACC, and putamen-dorsolateral prefrontal cortex (DLPFC). Furthermore, the putamen-DLPFC connectivity was negatively correlated with attentional impulsivity in the OCD group, but showed a positive correlation in HCs. CONCLUSIONS The present findings suggested that dorsal cognitive circuits could reflect the level of inhibitory control, which is balanced with the impulsive drive in healthy controls, but breakdown in OCD. Our findings supported that DLPFC-putamen connectivity underlying trait impulsivity, which were involved in the pathophysiology of OCD. The findings have provided new insights into the neurobiological mechanisms of OCD.
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Affiliation(s)
- Tingting Xu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Qiumeng Gu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Qing Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Pei Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Qiang Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Jue Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, PR China; Shanghai Key Laboratory of Psychotic Disorders, PR China.
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Tozzi L, Zhang X, Chesnut M, Holt-Gosselin B, Ramirez CA, Williams LM. Reduced functional connectivity of default mode network subsystems in depression: Meta-analytic evidence and relationship with trait rumination. Neuroimage Clin 2021; 30:102570. [PMID: 33540370 PMCID: PMC7856327 DOI: 10.1016/j.nicl.2021.102570] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 11/30/2022]
Abstract
Resting-state functional connectivity changes in the default mode network (DMN) of patients with major depressive disorder (MDD) have been linked to rumination. The DMN is divided into three subsystems: a midline Core, a dorsal medial prefrontal cortex (DMPFC) subsystem, and a medial temporal lobe (MTL) subsystem. We examined resting-state functional connectivity within and between DMN subsystems in MDD and its association with rumination. First, we conducted a meta-analysis on a large multi-site dataset of 618 MDD and 683 controls to quantify the differences in DMN subsystem functional connectivity between MDD and controls. Second, we tested the association of DMN subsystem functional connectivity and rumination in a sample of 115 unmedicated participants with symptoms of anxiety/depression and 48 controls. In our meta-analysis, only functional connectivity in the DMN Core was significantly reduced in MDD compared to controls (g = -0.246, CI = [-0.417; -0.074], pFDR = 0.048). Functional connectivity in the DMPFC subsystem and between the Core and DMPFC subsystems was slightly reduced but not significantly (g = -0.162, CI = [-0.310; -0.013], pFDR = 0.096; g = -0.249, CI = [-0.464; -0.034], pFDR = 0.084). Results were heterogeneous across sites for connectivity in the Core and between Core and DMPFC (I2 = 0.348 and I2 = 0.576 respectively). Prediction intervals consistently encompassed 0. In the independent sample we collected, functional connectivity within the DMN Core, DMPFC and between Core and DMPFC was not reduced in MDD compared to controls (all pFDR > 0.05). Trait rumination did not predict connectivity within and between DMN subsystems (all pFDR > 0.05). We conclude that MDD as a diagnostic category shows slightly reduced functional connectivity within the DMN Core, independent of illness duration, treatment, symptoms and trait rumination. However, this effect is small, highly variable and heterogeneous across samples, so that we could only detect it at the meta-analytic level, with a sample size of several hundreds. Our results indicate that reduced Core DMN connectivity has significant limitations as a potential clinical or prognostic marker for the diagnosis of MDD and might be more relevant to consider as a characteristic distinguishing a subgroup of individuals within this diagnostic category.
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Affiliation(s)
- Leonardo Tozzi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Xue Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Megan Chesnut
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Bailey Holt-Gosselin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Carolina A Ramirez
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, 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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25
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Williams LM, Coman JT, Stetz PC, Walker NC, Kozel FA, George MS, Yoon J, Hack LM, Madore MR, Lim KO, Philip NS, Holtzheimer PE. Identifying response and predictive biomarkers for Transcranial magnetic stimulation outcomes: protocol and rationale for a mechanistic study of functional neuroimaging and behavioral biomarkers in veterans with Pharmacoresistant depression. BMC Psychiatry 2021; 21:35. [PMID: 33435926 PMCID: PMC7805238 DOI: 10.1186/s12888-020-03030-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 12/22/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although repetitive transcranial magnetic stimulation ('TMS') is becoming a gold standard treatment for pharmacoresistant depression, we lack neural target biomarkers for identifying who is most likely to respond to TMS and why. To address this gap in knowledge we evaluate neural targets defined by activation and functional connectivity of the dorsolateral prefrontal cortex-anchored cognitive control circuit, regions of the default mode network and attention circuit, and interactions with the subgenual anterior cingulate. We evaluate whether these targets and interactions between them change in a dose-dependent manner, whether changes in these neural targets correspond to changes in cognitive behavioral performance, and whether baseline and early change in neural target and cognitive behavioral performance predict subsequent symptom severity, suicidality, and quality of life outcomes. This study is designed as a pragmatic, mechanistic trial partnering with the National Clinical TMS Program of the Veteran's Health Administration. METHODS Target enrollment consists of 100 veterans with pharmacoresistant Major Depressive Disorder (MDD). All veterans will receive a clinical course of TMS and will be assessed at 'baseline' pre-TMS commencement, 'first week' after initiation of TMS (targeting five sessions) and 'post-treatment' at the completion of TMS (targeting 30 sessions). Veterans will be assessed using functional magnetic resonance imaging (fMRI), a cognitive behavioral performance battery, and established questionnaires. Multivariate linear mixed models will be used to assess whether neural targets change with TMS as a function of dose (Aim 1), whether extent and change of neural target relates to and predicts extent of behavioral performance (Aim 3), and whether extent of neural target change predicts improvement in symptom severity, suicidality, and quality of life (Aim 3). For all three aims, we will also assess the contribution of baseline moderators such as biological sex and age. DISCUSSION To our knowledge, our study will be the first pragmatic, mechanistic observational trial to use fMRI imaging and cognitive-behavioral performance as biomarkers of TMS treatment response in pharmacoresistant MDD. The results of this trial will allow providers to select suitable candidates for TMS treatment and better predict treatment response by assessing circuit connectivity and cognitive-behavioral performance at baseline and during early treatment. TRIAL REGISTRATION ClinicalTrials.gov NCT04663481 , December 5th, 2020, retrospectively registered. The first veteran was enrolled October 30th, 2020.
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Affiliation(s)
- Leanne M. Williams
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - John T. Coman
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - Patrick C. Stetz
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - Nicole C. Walker
- grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - F. Andrew Kozel
- grid.255986.50000 0004 0472 0419Department of Behavioral Sciences and Social Medicine, Florida State University, 1115 W Call St, Tallahassee, FL 32304 USA ,grid.170693.a0000 0001 2353 285XDepartment of Psychiatry and Behavioral Neurosciences, University of South Florida, 3515 E Fletcher Ave, Tampa, FL 33613 USA
| | - Mark S. George
- grid.259828.c0000 0001 2189 3475Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, 96 Jonathan Lucas St. Ste. 601, MSC 617, Charleston, SC 29425 USA ,grid.280644.c0000 0000 8950 3536Ralph H. Johnson VA Medical Center, Charleston, SC USA
| | - Jong Yoon
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - Laura M. Hack
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - Michelle R. Madore
- grid.168010.e0000000419368956Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94304 USA ,grid.280747.e0000 0004 0419 2556Mental Illness Research, Education and Clinical Center, VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304 USA
| | - Kelvin O. Lim
- grid.17635.360000000419368657Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, 420 Delaware St SE, Minneapolis, MN 55455 USA ,grid.410394.b0000 0004 0419 8667Minneapolis VA Health Care System, 1 Veterans Dr, Minneapolis, MN 55417 USA
| | - Noah S. Philip
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, 345 Blackstone Boulevard, Providence, RI 02908 USA ,grid.413904.b0000 0004 0420 4094VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI 02908 USA
| | - Paul E. Holtzheimer
- grid.413480.a0000 0004 0440 749XDepartments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH 03756 USA ,grid.413726.50000 0004 0420 6436Executive Division, National Center for PTSD, White River Junction VA Medical Center, 215 North Main St., White River Junction, VT 05009 USA
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Chesnut M, Harati S, Paredes P, Khan Y, Foudeh A, Kim J, Bao Z, Williams LM. Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis. CHRONIC STRESS (THOUSAND OAKS, CALIF.) 2021; 5:24705470211000338. [PMID: 33997582 PMCID: PMC8076775 DOI: 10.1177/24705470211000338] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 02/16/2021] [Indexed: 11/17/2022]
Abstract
Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.
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Affiliation(s)
- Megan Chesnut
- Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Sahar Harati
- Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Pablo Paredes
- Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Yasser Khan
- Chemical Engineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Amir Foudeh
- Chemical Engineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Jayoung Kim
- Chemical Engineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Zhenan Bao
- Chemical Engineering, Stanford University School of Engineering, Stanford, CA, USA
| | - Leanne M. Williams
- Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
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Calderón-Garcidueñas L, González-Maciel A, Reynoso-Robles R, Hammond J, Kulesza R, Lachmann I, Torres-Jardón R, Mukherjee PS, Maher BA. Quadruple abnormal protein aggregates in brainstem pathology and exogenous metal-rich magnetic nanoparticles (and engineered Ti-rich nanorods). The substantia nigrae is a very early target in young urbanites and the gastrointestinal tract a key brainstem portal. ENVIRONMENTAL RESEARCH 2020; 191:110139. [PMID: 32888951 DOI: 10.1016/j.envres.2020.110139] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/21/2020] [Accepted: 08/22/2020] [Indexed: 06/11/2023]
Abstract
Fine particulate air pollution (PM2.5) exposures are linked with Alzheimer's and Parkinson's diseases (AD,PD). AD and PD neuropathological hallmarks are documented in children and young adults exposed lifelong to Metropolitan Mexico City air pollution; together with high frontal metal concentrations (especially iron)-rich nanoparticles (NP), matching air pollution combustion- and friction-derived particles. Here, we identify aberrant hyperphosphorylated tau, ɑ synuclein and TDP-43 in the brainstem of 186 Mexico City 27.29 ± 11.8y old residents. Critically, substantia nigrae (SN) pathology seen in mitochondria, endoplasmic reticulum and neuromelanin (NM) is co-associated with the abundant presence of exogenous, Fe-, Al- and Ti-rich NPs.The SN exhibits early and progressive neurovascular unit damage and mitochondria and NM are associated with metal-rich NPs including exogenous engineered Ti-rich nanorods, also identified in neuroenteric neurons. Such reactive, cytotoxic and magnetic NPs may act as catalysts for reactive oxygen species formation, altered cell signaling, and protein misfolding, aggregation and fibril formation. Hence, pervasive, airborne and environmental, metal-rich and magnetic nanoparticles may be a common denominator for quadruple misfolded protein neurodegenerative pathologies affecting urbanites from earliest childhood. The substantia nigrae is a very early target and the gastrointestinal tract (and the neuroenteric system) key brainstem portals. The ultimate neural damage and neuropathology (Alzheimer's, Parkinson's and TDP-43 pathology included) could depend on NP characteristics and the differential access and targets achieved via their portals of entry. Thus where you live, what air pollutants you are exposed to, what you are inhaling and swallowing from the air you breathe,what you eat, how you travel, and your occupational longlife history are key. Control of NP sources becomes critical.
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Affiliation(s)
| | | | | | - Jessica Hammond
- Centre for Environmental Magnetism and Paleomagnetism, Lancaster Environment Centre, University of Lancaster, Lancaster, LA1 4YQ, UK
| | - Randy Kulesza
- Auditory Research Center, Lake Erie College of Osteopathic Medicine, Erie, PA, USA
| | | | - Ricardo Torres-Jardón
- Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, UNAM, Mexico City, 04510, Mexico
| | | | - Barbara A Maher
- Centre for Environmental Magnetism and Paleomagnetism, Lancaster Environment Centre, University of Lancaster, Lancaster, LA1 4YQ, UK
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Seok D, Smyk N, Jaskir M, Cook P, Elliott M, Girelli T, Scott JC, Balderston N, Beer J, Stock J, Makhoul W, Gur RC, Davatzikos C, Shinohara R, Sheline Y. Dimensional connectomics of anxious misery, a human connectome study related to human disease: Overview of protocol and data quality. NEUROIMAGE-CLINICAL 2020; 28:102489. [PMID: 33395980 PMCID: PMC7708855 DOI: 10.1016/j.nicl.2020.102489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/09/2020] [Accepted: 10/27/2020] [Indexed: 11/19/2022]
Abstract
We present a new imaging study of 200 adults experiencing depression and anxiety. Quantitative measures of image quality indicate comparable quality to the HCP-YA. In addition, a comprehensive set of assessments measured patients’ symptom profiles. Data will be publicly available through the NIMH Data Archive starting fall 2020.
Disparate diagnostic categories from the Diagnostic and Statistical Manual of Mental Disorders (DSM), including generalized anxiety disorder, major depressive disorder and post-traumatic stress disorder, share common behavioral and phenomenological dysfunctions. While high levels of comorbidity and common features across these disorders suggest shared mechanisms, past research in psychopathology has largely proceeded based on the syndromal taxonomy established by the DSM rather than on a biologically-informed framework of neural, cognitive and behavioral dysfunctions. In line with the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework, we present a Human Connectome Study Related to Human Disease that is intentionally designed to generate and test novel, biologically-motivated dimensions of psychopathology. The Dimensional Connectomics of Anxious Misery study is collecting neuroimaging, cognitive and behavioral data from a heterogeneous population of adults with varying degrees of depression, anxiety and trauma, as well as a set of healthy comparators (to date, n = 97 and n = 24, respectively). This sample constitutes a dataset uniquely situated to elucidate relationships between brain circuitry and dysfunctions of the Negative Valence construct of the RDoC framework. We present a comprehensive overview of the eligibility criteria, clinical procedures and neuroimaging methods of our project. After describing our protocol, we present group-level activation maps from task fMRI data and independent components maps from resting state data. Finally, using quantitative measures of neuroimaging data quality, we demonstrate excellent data quality relative to a subset of the Human Connectome Project of Young Adults (n = 97), as well as comparable profiles of cortical thickness from T1-weighted imaging and generalized fractional anisotropy from diffusion weighted imaging. This manuscript presents results from the first 121 participants of our full target 250 participant dataset, timed with the release of this data to the National Institute of Mental Health Data Archive in fall 2020, with the remaining half of the dataset to be released in 2021.
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Affiliation(s)
- Darsol Seok
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Nathan Smyk
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Marc Jaskir
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Philip Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Mark Elliott
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Tommaso Girelli
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - J Cobb Scott
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Nicholas Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Joanne Beer
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Janet Stock
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Walid Makhoul
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Russell Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Yvette Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States.
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Keshmiri S. Stress Changes the Resting-State Cortical Flow of Information from Distributed to Frontally Directed Patterns. BIOLOGY 2020; 9:E236. [PMID: 32824879 PMCID: PMC7464349 DOI: 10.3390/biology9080236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 11/16/2022]
Abstract
Despite converging evidence on the involvement of large-scale distributed brain networks in response to stress, the effect of stress on the components of these networks is less clear. Although some studies identify higher regional activities in response to stress, others observe an opposite effect in the similar regions. Studies based on synchronized activities and coactivation of these components also yield similar differing results. However, these differences are not necessarily contradictory once we observe the effect of stress on these functional networks in terms of the change in information processing capacity of their components. In the present study, we investigate the utility of such a shift in the analysis of the effect of stress on distributed cortical regions through quantification of the flow of information among them. For this purpose, we use the self-assessed responses of 216 individuals to stress-related questionnaires and systematically select 20 of them whose responses showed significantly higher and lower susceptibility to stress. We then use these 20 individuals' resting-state multi-channel electroencephalography (EEG) recordings (both Eyes-Closed (EC) and Eyes-Open (EO) settings) and compute the distributed flow of information among their cortical regions using transfer entropy (TE). The contribution of the present study is three-fold. First, it identifies that the stress-susceptibility is characterized by the change in flow of information in fronto-parietal brain network. Second, it shows that these regions are distributed bi-hemispherically and are sufficient to significantly differentiate between the individuals with high versus low stress-susceptibility. Third, it verifies that the high stress-susceptibility is markedly associated with a higher parietal-to-frontal flow of information. These results provide further evidence for the viewpoint in which the brain's modulation of information is not necessarily accompanied by the change in its regional activity. They further construe the effect of stress in terms of a disturbance that disrupts the flow of information among the brain's distributed cortical regions. These observations, in turn, suggest that some of the differences in the previous findings perhaps reflect different aspects of impaired distributed brain information processing in response to stress. From a broader perspective, these results posit the use of TE as a potential diagnostic/prognostic tool in identification of the effect of stress on distributed brain networks that are involved in stress-response.
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Affiliation(s)
- Soheil Keshmiri
- The Thomas N. Sato BioMEC-X Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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