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Zhang L, Ding Y, Li T, Li H, Liu F, Li P, Zhao J, Lv D, Lang B, Guo W. Similar imaging changes and their relations to genetic profiles in bipolar disorder across different clinical stages. Psychiatry Res 2024; 335:115868. [PMID: 38554494 DOI: 10.1016/j.psychres.2024.115868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/01/2024]
Abstract
Bipolar disorder (BD) across different clinical stages may present shared and distinct changes in brain activity. We aimed to reveal the neuroimaging homogeneity and heterogeneity of BD and its relationship with clinical variables and genetic variations. In present study, we conducted fractional amplitude of low-frequency fluctuations (fALFF), functional connectivity (FC) and genetic neuroimaging association analyses with 32 depressed, 26 manic, 35 euthymic BD patients and 87 healthy controls (HCs). Significant differences were found in the bilateral pre/subgenual anterior cingulate cortex (ACC) across the four groups, and all bipolar patients exhibited decreased fALFF values in the ACC when compared to HCs. Furthermore, positive associations were significantly observed between fALFF values in the pre/subgenual ACC and participants' cognitive functioning. No significant changes were found in ACC-based FC. We identified fALFF-alteration-related genes in BD, with enrichment in biological progress including synaptic and ion transmission. Taken together, abnormal activity in ACC is a characteristic change associated with BD, regardless of specific mood stages, serving as a potential neuroimaging feature in BD patients. Our genetic neuroimaging association analysis highlights possible heterogeneity in biological processes that could be responsible for different clinical stages in BD.
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Affiliation(s)
- Leyi Zhang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Yudan Ding
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Tingting Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang 161006, China
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Dongsheng Lv
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China; Center of Mental Health, Inner Mongolia Autonomous Region, Hohhot 010010, China.
| | - Bing Lang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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Wu YK, Su YA, Zhu LL, Li JT, Li Q, Dai YR, Lin JY, Li K, Si TM. Intrinsic functional connectivity correlates of cognitive deficits involving sustained attention and executive function in bipolar disorder. BMC Psychiatry 2023; 23:584. [PMID: 37568112 PMCID: PMC10416380 DOI: 10.1186/s12888-023-05083-2] [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: 04/13/2023] [Accepted: 08/07/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND The neural correlate of cognitive deficits in bipolar disorder (BD) is an issue that warrants further investigation. However, relatively few studies have examined the intrinsic functional connectivity (FC) underlying cognitive deficits involving sustained attention and executive function at both the region and network levels, as well as the different relationships between connectivity patterns and cognitive performance, in BD patients and healthy controls (HCs). METHODS Patients with BD (n = 59) and HCs (n = 52) underwent structural and resting-state functional magnetic resonance imaging and completed the Wisconsin Card Sorting Test (WCST), the continuous performance test and a clinical assessment. A seed-based approach was used to evaluate the intrinsic FC alterations in three core neurocognitive networks (the default mode network [DMN], the central executive network [CEN] and the salience network [SN]). Finally, we examined the relationship between FC and cognitive performance by using linear regression analyses. RESULTS Decreased FC was observed within the DMN, in the DMN-SN and DMN-CEN and increased FC was observed in the SN-CEN in BD. The alteration direction of regional FC was consistent with that of FC at the brain network level. Decreased FC between the left posterior cingulate cortex and right anterior cingulate cortex was associated with longer WCST completion time in BD patients (but not in HCs). CONCLUSIONS These findings emphasize the dominant role of the DMN in the psychopathology of BD and provide evidence that cognitive deficits in BD may be associated with aberrant FC between the anterior and posterior DMN.
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Affiliation(s)
- Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lin-Lin Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Qian Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - You-Ran Dai
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing-Yu Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Ke Li
- PLA Strategic Support Force Characteristic Medical Center, Beijing, 100101, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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Cattarinussi G, Gugliotta AA, Sambataro F. The Risk for Schizophrenia-Bipolar Spectrum: Does the Apple Fall Close to the Tree? A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6540. [PMID: 37569080 PMCID: PMC10418911 DOI: 10.3390/ijerph20156540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric disorders that share clinical features and several risk genes. Important information about their genetic underpinnings arises from intermediate phenotypes (IPs), quantifiable biological traits that are more prevalent in unaffected relatives (RELs) of patients compared to the general population and co-segregate with the disorders. Within IPs, neuropsychological functions and neuroimaging measures have the potential to provide useful insight into the pathophysiology of SCZ and BD. In this context, the present narrative review provides a comprehensive overview of the available evidence on deficits in neuropsychological functions and neuroimaging alterations in unaffected relatives of SCZ (SCZ-RELs) and BD (BD-RELs). Overall, deficits in cognitive functions including intelligence, memory, attention, executive functions, and social cognition could be considered IPs for SCZ. Although the picture for cognitive alterations in BD-RELs is less defined, BD-RELs seem to present worse performances compared to controls in executive functioning, including adaptable thinking, planning, self-monitoring, self-control, and working memory. Among neuroimaging markers, SCZ-RELs appear to be characterized by structural and functional alterations in the cortico-striatal-thalamic network, while BD risk seems to be associated with abnormalities in the prefrontal, temporal, thalamic, and limbic regions. In conclusion, SCZ-RELs and BD-RELs present a pattern of cognitive and neuroimaging alterations that lie between patients and healthy individuals. Similar abnormalities in SCZ-RELs and BD-RELs may be the phenotypic expression of the shared genetic mechanisms underlying both disorders, while the specificities in neuropsychological and neuroimaging profiles may be associated with the differential symptom expression in the two disorders.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Alessio A. Gugliotta
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, 35131 Padova, Italy; (G.C.); (A.A.G.)
- Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
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5
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Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series. Schizophr Res 2022; 245:141-150. [PMID: 33676821 PMCID: PMC8413409 DOI: 10.1016/j.schres.2021.02.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging. METHODS In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification. RESULTS When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension). CONCLUSIONS The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.
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6
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Gershon ES, Lee SH, Zhou X, Sweeney JA, Tamminga C, Pearlson GA, Clementz BA, Keshavan MS, Alliey-Rodriguez N, Hudgens-Haney M, Keedy SK, Glahn DC, Asif H, Lencer R, Hill SK. An opportunity for primary prevention research in psychotic disorders. Schizophr Res 2022; 243:433-439. [PMID: 34315649 PMCID: PMC8784565 DOI: 10.1016/j.schres.2021.07.001] [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: 01/21/2021] [Revised: 04/29/2021] [Accepted: 07/01/2021] [Indexed: 10/20/2022]
Abstract
An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. METHODS: The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. RESULTS AND CONCLUSIONS: Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies.
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Affiliation(s)
- Elliot S Gershon
- University of Chicago, Department of Psychiatry, United States of America; University of Chicago, Department of Human Genetics, United States of America.
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia; UniSA: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia; South Australian Health and Medical Research Institute, Adelaide, South Australia 5000, Australia.
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia; UniSA: Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia; South Australian Health and Medical Research Institute, Adelaide, South Australia 5000, Australia.
| | - John A Sweeney
- University of Cincinnati, Department of Psychiatry United States of America, Sichuan University, Hauxi Center for MR Research, China.
| | - Carol Tamminga
- University of Texas Southwestern, United States of America.
| | | | | | | | | | | | | | - David C Glahn
- Harvard Medical School, Boston Children's Hospital, United States of America.
| | - Huma Asif
- University of Chicago, United States of America.
| | - Rebekka Lencer
- University of Muenster, Muenster, Germany; Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
| | - S Kristian Hill
- Rosalind Franklin University of Medicine and Science, United States of America.
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Marino M, Romeo Z, Angrilli A, Semenzato I, Favaro A, Magnolfi G, Padovan GB, Mantini D, Spironelli C. Default mode network shows alterations for low-frequency fMRI fluctuations in euthymic bipolar disorder. J Psychiatr Res 2021; 144:59-65. [PMID: 34600288 DOI: 10.1016/j.jpsychires.2021.09.051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/13/2021] [Accepted: 09/23/2021] [Indexed: 11/18/2022]
Abstract
Bipolar disorder (BD) is a psychiatric condition causing acute dysfunctional mood states and emotion regulation. Specific neuropsychological features are often present also among patients in euthymic phase, who do not show clear psychotic symptoms, and for whom the characterization from functional magnetic resonance imaging (fMRI) is very limited. This study aims at identifying the neural and behavioral correlates of the default mode network (DMN) using the fractional amplitude of low frequency fluctuations (fALFF). Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and sixteen healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a 1.5T fMRI scan at rest. The DMN was extracted through independent component analysis. Then, DMN time series was used to compute the fALFF, which was correlated with clinical scales. From the between-group comparison, no significant differences emerged in correspondence to regions belonging to the DMN. For fALFF analysis, we reported significant increase of low-frequency fluctuations for lower frequencies, and decreases for higher frequencies compared to HC. Correlations with clinical scales showed that an increase in higher frequency spectral content was associated with lower levels of mania and higher levels of anxious symptoms, while an increase in lower frequencies was linked to lower depressive symptoms. Starting from our findings on the DMN in euthymic BD patients, we suggest that the fALFF derived from network time series represents a viable approach to investigate the behavioral correlates of resting state networks, and the pathophysiological mechanisms of different psychiatric conditions.
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Affiliation(s)
- Marco Marino
- Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU, Leuven, Belgium; IRCCS San Camillo Hospital, Venice, Italy.
| | - Zaira Romeo
- Department of General Psychology, University of Padova, Italy
| | - Alessandro Angrilli
- Department of General Psychology, University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | | | - Angela Favaro
- Padova Neuroscience Center, University of Padova, Italy; Psychiatric Clinic, Neuroscience Department, University of Padova, Italy
| | - Gianna Magnolfi
- Psychiatric Clinic, Neuroscience Department, University of Padova, Italy
| | - Giordano Bruno Padovan
- Psychiatric Clinic, Neuroscience Department, University of Padova, Italy; Unit of Penitentiary Medicine, ULSS6, Padova, Italy
| | - Dante Mantini
- Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU, Leuven, Belgium; IRCCS San Camillo Hospital, Venice, Italy
| | - Chiara Spironelli
- Department of General Psychology, University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy.
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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9
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Alamian G, Pascarella A, Lajnef T, Knight L, Walters J, Singh KD, Jerbi K. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin 2020; 28:102485. [PMID: 33395976 PMCID: PMC7691748 DOI: 10.1016/j.nicl.2020.102485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/22/2020] [Accepted: 10/24/2020] [Indexed: 12/19/2022]
Abstract
Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.
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Affiliation(s)
- Golnoush Alamian
- CoCo Lab, Department of Psychology, Université de Montréal, Canada.
| | | | - Tarek Lajnef
- CoCo Lab, Department of Psychology, Université de Montréal, Canada
| | - Laura Knight
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - James Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Karim Jerbi
- CoCo Lab, Department of Psychology, Université de Montréal, Canada; MEG Center, University of Montreal, Canada; UNIQUE Centre (Unifying AI and Neuroscience - Québec), Quebec, Canada; Mila (Quebec AI Institute), Montreal, QC, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada
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10
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Rokham H, Pearlson G, Abrol A, Falakshahi H, Plis S, Calhoun VD. Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:819-832. [PMID: 32771180 PMCID: PMC7760893 DOI: 10.1016/j.bpsc.2020.05.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 05/04/2020] [Accepted: 05/06/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. METHODS We worked with T1 structural magnetic resonance imaging data collected from 1493 individuals comprising healthy control subjects, patients with psychosis, and their unaffected first-degree relatives. Specifically, the dataset included 176 bipolar disorder probands, 134 schizoaffective disorder probands, 240 schizophrenia probands, 362 control subjects, and 581 patient relatives. We assumed that there might be noise in the diagnostic labeling process. We detected label noise by classifying the data multiple times using a support vector machine classifier, and then we flagged those individuals in which all classifiers unanimously mislabeled those subjects. Next, we assigned a new diagnostic label to these individuals, based on the biological data (magnetic resonance imaging), using an iterative data cleansing approach. RESULTS Simulation results showed that our method was highly accurate in identifying label noise. Both diagnostic and biotype categories showed about 65% and 63% of noisy labels, respectively, with the largest amount of relabeling occurring between the healthy control subjects and individuals with bipolar disorder and schizophrenia as well as in unaffected close relatives. The extraction of imaging features highlighted regional brain changes associated with each group. CONCLUSIONS This approach represents an initial step toward developing strategies that need not assume that existing mental health diagnostic categories are always valid but rather allows us to leverage this information while also acknowledging that there are misassignments.
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Affiliation(s)
- Hooman Rokham
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University, New Haven, Connecticut; Department of Neuroscience, Yale University, New Haven, Connecticut; Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, Connecticut
| | - Anees Abrol
- Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia
| | - Haleh Falakshahi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia
| | - Sergey Plis
- Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia; Tri-institutional Center of Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia; Department of Psychology, Georgia State University, Atlanta, Georgia; Department of Psychiatry, Yale University, New Haven, Connecticut
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11
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Abrol A, Rokham H, Calhoun VD. Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4084-4088. [PMID: 31946769 DOI: 10.1109/embc.2019.8857902] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two large, independent multi-group (ADNI and BSNIP) neuroimaging datasets. We conduct several binary classification tasks to assess the diagnostic/prognostic performance of the ResNet architecture through a rigorous, repeated and stratified k-fold cross-validation procedure for each of the classification tasks independently. We obtained better than state of the art performance for the clinically most important task in the ADNI dataset analysis, and significantly higher classification accuracies over a standard machine learning method (linear SVM) in each of the ADNI and BSNIP classification tasks. Overall, our results indicate the high potential of this architecture to learn effectual feature representations from structural brain imaging data.
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12
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Finotelli P, Forlim CG, Klock L, Pini A, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Dulio P, Gallinat J, Kühn S. New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia. Brain Connect 2019; 9:760-769. [PMID: 31232080 DOI: 10.1089/brain.2018.0654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive, and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modeled independently: Functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural, and topological information of magnetic resonance imaging (MRI) measurements (structural and resting-state imaging) to patients with schizophrenia (n = 35) and matched healthy individuals (n = 41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole-brain connectivity, including functional, structural, and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity, resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting-state functional MRI, and it can be applied to electro-encephalography, magneto-encephalography, etc.
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Affiliation(s)
- Paolo Finotelli
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Caroline Garcia Forlim
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Leonie Klock
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Alessia Pini
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Johanna Bächle
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Laura Stoll
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Patrick Giemsa
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Marie Fuchs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Nikola Schoofs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Christiane Montag
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Paolo Dulio
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Jürgen Gallinat
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
- Lise-Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
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13
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Kotzalidis GD, Rapinesi C, Savoja V, Cuomo I, Simonetti A, Ambrosi E, Panaccione I, Gubbini S, De Rossi P, De Chiara L, Janiri D, Sani G, Koukopoulos AE, Manfredi G, Napoletano F, Caloro M, Pancheri L, Puzella A, Callovini G, Angeletti G, Del Casale A. Neurobiological Evidence for the Primacy of Mania Hypothesis. Curr Neuropharmacol 2018; 15:339-352. [PMID: 28503105 PMCID: PMC5405607 DOI: 10.2174/1570159x14666160708231216] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2016] [Revised: 05/23/2016] [Accepted: 05/24/2016] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Athanasios Koukopoulos proposed the primacy of mania hypothesis (PoM) in a 2006 book chapter and later, in two peer-reviewed papers with Nassir Ghaemi and other collaborators. This hypothesis supports that in bipolar disorder, mania leads to depression, while depression does not lead to mania. OBJECTIVE To identify evidence in literature that supports or falsifies this hypothesis. METHOD We searched the medical literature (PubMed, Embase, PsycINFO, and the Cochrane Library) for peer-reviewed papers on the primacy of mania, the default mode function of the brain in normal people and in bipolar disorder patients, and on illusion superiority until 6 June, 2016. Papers resulting from searches were considered for appropriateness to our objective. We adopted the PRISMA method for our review. The search for consistency with PoM was filtered through the neurobiological results of superiority illusion studies. RESULTS Out of a grand total of 139 records, 59 were included in our analysis. Of these, 36 were of uncertain value as to the primacy of mania hypothesis, 22 favoured it, and 1 was contrary, but the latter pooled patients in their manic and depressive phases, so to invalidate possible conclusions about its consistency with regard to PoM. All considered studies were not focused on PoM or superiority illusion, hence most of their results were, as expected, unrelated to the circuitry involved in superiority illusion. A considerable amount of evidence is consistent with the hypothesis, although indirectly so. LIMITATIONS Only few studies compared manic with depressive phases, with the majority including patients in euthymia. CONCLUSION It is possible that humans have a natural tendency for elation/optimism and positive self-consideration, that are more akin to mania; the depressive state could be a consequence of frustrated or unsustainable mania. This would be consistent with PoM.
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Affiliation(s)
- Georgios D Kotzalidis
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy
| | - Chiara Rapinesi
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy
| | - Valeria Savoja
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,ASL Roma 3, Rome, Italy
| | - Ilaria Cuomo
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Clinica Neuropsichiatrica Villa von Siebenthal, Genzano di Roma (Rome), Italy
| | - Alessio Simonetti
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Baylor College of Medicine, Houston, Texas, USA.,Centro Lucio Bini, Rome, Italy
| | - Elisa Ambrosi
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Baylor College of Medicine, Houston, Texas, USA
| | - Isabella Panaccione
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Silvia Gubbini
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy.,USL Umbria 2, Terni, Italy
| | - Pietro De Rossi
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Lavinia De Chiara
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Delfina Janiri
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy
| | - Gabriele Sani
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Alexia E Koukopoulos
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Giovanni Manfredi
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Flavia Napoletano
- Core Trainee in Psychiatry, NELFT (North East London Foundation Trust), London, UK.,King's College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, 16 De Crespigny Park, London SE5 8AF London, UK
| | - Matteo Caloro
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy
| | | | | | - Gemma Callovini
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Department of Psychiatry, Federico II University, Naples, Italy
| | - Gloria Angeletti
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Centro Lucio Bini, Rome, Italy
| | - Antonio Del Casale
- NESMOS Department, Sapienza University - Rome, School of Medicine and Psychology, Sant'Andrea Hospital, Via di Grottarossa 1035-1039, 00189 Rome, Italy.,Department of Psychiatric Rehabilitation, Father A. Mileno Onlus Foundation, San Francesco Institute, Vasto (Chieti), Italy
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14
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Misiak B, Stramecki F, Gawęda Ł, Prochwicz K, Sąsiadek MM, Moustafa AA, Frydecka D. Interactions Between Variation in Candidate Genes and Environmental Factors in the Etiology of Schizophrenia and Bipolar Disorder: a Systematic Review. Mol Neurobiol 2018; 55:5075-5100. [PMID: 28822116 PMCID: PMC5948257 DOI: 10.1007/s12035-017-0708-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 08/01/2017] [Indexed: 12/29/2022]
Abstract
Schizophrenia and bipolar disorder (BD) are complex and multidimensional disorders with high heritability rates. The contribution of genetic factors to the etiology of these disorders is increasingly being recognized as the action of multiple risk variants with small effect sizes, which might explain only a minor part of susceptibility. On the other site, numerous environmental factors have been found to play an important role in their causality. Therefore, in recent years, several studies focused on gene × environment interactions that are believed to bridge the gap between genetic underpinnings and environmental insults. In this article, we performed a systematic review of studies investigating gene × environment interactions in BD and schizophrenia spectrum phenotypes. In the majority of studies from this field, interacting effects of variation in genes encoding catechol-O-methyltransferase (COMT), brain-derived neurotrophic factor (BDNF), and FK506-binding protein 5 (FKBP5) have been explored. Almost consistently, these studies revealed that polymorphisms in COMT, BDNF, and FKBP5 genes might interact with early life stress and cannabis abuse or dependence, influencing various outcomes of schizophrenia spectrum disorders and BD. Other interactions still require further replication in larger clinical and non-clinical samples. In addition, future studies should address the direction of causality and potential mechanisms of the relationship between gene × environment interactions and various categories of outcomes in schizophrenia and BD.
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Affiliation(s)
- Błażej Misiak
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368, Wroclaw, Poland.
| | - Filip Stramecki
- Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367, Wroclaw, Poland
| | - Łukasz Gawęda
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- II Department of Psychiatry, Medical University of Warsaw, Warsaw, Poland
| | | | - Maria M Sąsiadek
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368, Wroclaw, Poland
| | - Ahmed A Moustafa
- School of Social Sciences and Psychology, Marcs Institute of Brain and Behaviour, Western Sydney University, Penrith, NSW, Australia
| | - Dorota Frydecka
- Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367, Wroclaw, Poland
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15
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Lottman KK, White DM, Kraguljac NV, Reid MA, Calhoun VD, Catao F, Lahti AC. Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia. Hum Brain Mapp 2018; 39:1475-1488. [PMID: 29315951 DOI: 10.1002/hbm.23906] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/06/2017] [Accepted: 11/26/2017] [Indexed: 01/05/2023] Open
Abstract
Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (nSZ =19) and matched controls (nHC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
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Affiliation(s)
- Kristin K Lottman
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, Alabama
| | - David M White
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Meredith A Reid
- Department of Electrical and Computer Engineering, MRI Research Center, Auburn University, Auburn, Alabama
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Fabio Catao
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
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16
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Shinn AK, Roh YS, Ravichandran CT, Baker JT, Öngür D, Cohen BM. Aberrant cerebellar connectivity in bipolar disorder with psychosis. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:438-448. [PMID: 28730183 DOI: 10.1016/j.bpsc.2016.07.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND The cerebellum, which modulates affect and cognition in addition to motor functions, may contribute substantially to the pathophysiology of mood and psychotic disorders, such as bipolar disorder. A growing literature points to cerebellar abnormalities in bipolar disorder. However, no studies have investigated the topographic representations of resting state cerebellar networks in bipolar disorder, specifically their functional connectivity to cerebral cortical networks. METHODS Using a well-defined cerebral cortical parcellation scheme as functional connectivity seeds, we compared ten cerebellar resting state networks in 49 patients with bipolar disorder and a lifetime history of psychotic features and 55 healthy control participants matched for age, sex, and image signal-to-noise ratio. RESULTS Patients with psychotic bipolar disorder showed reduced cerebro-cerebellar functional connectivity in somatomotor A, ventral attention, salience, and frontoparietal control A and B networks relative to healthy control participants. These findings were not significantly correlated with current symptoms. CONCLUSIONS Patients with psychotic bipolar disorder showed evidence of cerebro-cerebellar dysconnectivity in selective networks. These disease-related changes were substantial and not explained by medication exposure or substance use. Therefore, they may be mechanistically relevant to the underlying susceptibility to mood dysregulation and psychosis. Cerebellar mechanisms deserve further exploration in psychiatric conditions, and this study's findings may have value in guiding future studies on pathophysiology and treatment of mood and psychotic disorders, in particular.
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Affiliation(s)
- Ann K Shinn
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Youkyung S Roh
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA
| | - Caitlin T Ravichandran
- Program for Neuropsychiatric Research, McLean Hospital, Belmont, MA, USA.,Lurie Center for Autism, Massachusetts General Hospital for Children, Boston, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Justin T Baker
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Dost Öngür
- Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Bruce M Cohen
- Program for Neuropsychiatric Research, McLean Hospital, Belmont, MA, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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17
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Yao N, Winkler AM, Barrett J, Book GA, Beetham T, Horseman R, Leach O, Hodgson K, Knowles EE, Mathias S, Stevens MC, Assaf M, van Erp TGM, Pearlson GD, Glahn DC. Inferring pathobiology from structural MRI in schizophrenia and bipolar disorder: Modeling head motion and neuroanatomical specificity. Hum Brain Mapp 2017; 38:3757-3770. [PMID: 28480992 DOI: 10.1002/hbm.23612] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 03/26/2017] [Accepted: 04/03/2017] [Indexed: 12/28/2022] Open
Abstract
Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Nailin Yao
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Anderson M Winkler
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Oxford Centre for Functional MRI of the Brain, University of Oxford, OX3 9DU, United Kingdom
| | - Jennifer Barrett
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Gregory A Book
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Tamara Beetham
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Rachel Horseman
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Olivia Leach
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Karen Hodgson
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Emma E Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Samuel Mathias
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Michael C Stevens
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Michal Assaf
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Connecticut
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.,Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
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18
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Gkintoni E, Pallis EG, Bitsios P, Giakoumaki SG. Neurocognitive performance, psychopathology and social functioning in individuals at high risk for schizophrenia or psychotic bipolar disorder. J Affect Disord 2017; 208:512-520. [PMID: 27810272 DOI: 10.1016/j.jad.2016.10.032] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 09/05/2016] [Accepted: 10/22/2016] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Although cognitive deficits are consistent endophenotypes of schizophrenia and bipolar disorder, findings in psychotic bipolar disorder (BDP) are inconsistent. In this study we compared adult unaffected first-degree relatives of schizophrenia and BDP patients on cognition, psychopathology, social functioning and quality of life. METHODS Sixty-six unaffected first-degree relatives of schizophrenia patients (SUnR), 36 unaffected first-degree relatives of BDP patients (BDPUnR) and 102 controls participated in the study. Between-group differences were examined and Discriminant Function Analysis (DFA) predicted group membership. RESULTS Visual memory, control inhibition, working memory, cognitive flexibility and abstract reasoning were linearly impaired in the relatives' groups. Poorer verbal fluency and processing speed were evident only in the SUnR group. The SUnR group had higher depressive and somatization symptoms while the BDPUnR group had higher anxiety and lower social functioning compared with the controls. Individuals with superior cognition were more likely to be classified as controls; those with higher social functioning, prolonged processing speed and lower anxiety were more likely to be classified as SUnR. LIMITATIONS The relatives' sample is quite heterogeneous; the effects of genetic or environmental risk-factors were not examined. CONCLUSIONS Cognitive functions mediated by a fronto-parietal network, show linear impairments in unaffected relatives of BDP and schizophrenia patients; processing speed and verbal fluency impairments were evident only in schizophrenia relatives. Self-perceived symptomatology and social functioning also differ between schizophrenia and BDP relatives. The continuum seen in patients in several indices was also seen in the cognitive impairments in unaffected relatives of schizophrenia and BDP patients.
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Affiliation(s)
- Evgenia Gkintoni
- Department of Psychology, Gallos University campus, University of Crete, Rethymno, Crete, Greece
| | - Eleftherios G Pallis
- Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece
| | - Panos Bitsios
- Department of Psychiatry & Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Stella G Giakoumaki
- Department of Psychology, Gallos University campus, University of Crete, Rethymno, Crete, Greece.
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19
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Pan Y, Dou WB, Wang YH, Luo HW, Ge YX, Yan SY, Xu Q, Tu YY, Xiao YQ, Wu Q, Zheng ZZ, Zhao HL. Non-concomitant cortical structural and functional alterations in sensorimotor areas following incomplete spinal cord injury. Neural Regen Res 2017; 12:2059-2066. [PMID: 29323046 PMCID: PMC5784355 DOI: 10.4103/1673-5374.221165] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Brain plasticity, including anatomical changes and functional reorganization, is the physiological basis of functional recovery after spinal cord injury (SCI). The correlation between brain anatomical changes and functional reorganization after SCI is unclear. This study aimed to explore whether alterations of cortical structure and network function are concomitant in sensorimotor areas after incomplete SCI. Eighteen patients with incomplete SCI (mean age 40.94 ± 14.10 years old; male:female, 7:11) and 18 healthy subjects (37.33 ± 11.79 years old; male:female, 7:11) were studied by resting state functional magnetic resonance imaging. Gray matter volume (GMV) and functional connectivity were used to evaluate cortical structure and network function, respectively. There was no significant alteration of GMV in sensorimotor areas in patients with incomplete SCI compared with healthy subjects. Intra-hemispheric functional connectivity between left primary somatosensory cortex (BA1) and left primary motor cortex (BA4), and left BA1 and left somatosensory association cortex (BA5) was decreased, as well as inter-hemispheric functional connectivity between left BA1 and right BA4, left BA1 and right BA5, and left BA4 and right BA5 in patients with SCI. Functional connectivity between both BA4 areas was also decreased. The decreased functional connectivity between the left BA1 and the right BA4 positively correlated with American Spinal Injury Association sensory score in SCI patients. The results indicate that alterations of cortical anatomical structure and network functional connectivity in sensorimotor areas were non-concomitant in patients with incomplete SCI, indicating the network functional changes in sensorimotor areas may not be dependent on anatomic structure. The strength of functional connectivity within sensorimotor areas could serve as a potential imaging biomarker for assessment and prediction of sensory function in patients with incomplete SCI. This trial was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-ROC-17013566).
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Affiliation(s)
- Yu Pan
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital; School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Wei-Bei Dou
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yue-Heng Wang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Hui-Wen Luo
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yun-Xiang Ge
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Shu-Yu Yan
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Quan Xu
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Yuan-Yuan Tu
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Yan-Qing Xiao
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Qiong Wu
- Department of Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Zhuo-Zhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Hong-Liang Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, Beijing, China
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20
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Martinez‐Murcia FJ, Lai M, Górriz JM, Ramírez J, Young AMH, Deoni SCL, Ecker C, Lombardo MV, Baron‐Cohen S, Murphy DGM, Bullmore ET, Suckling J. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis. Hum Brain Mapp 2016; 38:1208-1223. [PMID: 27774713 PMCID: PMC5324567 DOI: 10.1002/hbm.23449] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 10/07/2016] [Accepted: 10/13/2016] [Indexed: 02/02/2023] Open
Abstract
Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large-scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi-modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site-related variance, statistically significant group differences were found, including Broca's area and the temporo-parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208-1223, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Francisco Jesús Martinez‐Murcia
- Department of Signal Theory Networking and Communications, C/Periodista Daniel Saucedo Aranda S/NE‐18071, University of GranadaGranadaSpain
| | - Meng‐Chuan Lai
- Child and Youth Mental Health Collaborative at The Centre for Addiction and Mental Health and The Hospital for Sick ChildrenTorontoOntarioCanada,Department of PsychiatryUniversity of TorontoTorontoOntarioCanada,Department of Psychiatry, Autism Research CentreUniversity of CambridgeCambridgeUnited Kingdom,Department of PsychiatryNational Taiwan University Hospital and College of MedicineTaipeiTaiwan
| | - Juan Manuel Górriz
- Department of Signal Theory Networking and Communications, C/Periodista Daniel Saucedo Aranda S/NE‐18071, University of GranadaGranadaSpain
| | - Javier Ramírez
- Department of Signal Theory Networking and Communications, C/Periodista Daniel Saucedo Aranda S/NE‐18071, University of GranadaGranadaSpain
| | - Adam M. H. Young
- Department of Psychiatry, Autism Research CentreUniversity of CambridgeCambridgeUnited Kingdom
| | - Sean C. L. Deoni
- Advanced Baby Imaging Laboratory, School of EngineeringBrown UniversityProvidenceRhode Island
| | - Christine Ecker
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational NeurodevelopmentLondonUnited Kingdom,Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Michael V. Lombardo
- Department of Psychiatry, Autism Research CentreUniversity of CambridgeCambridgeUnited Kingdom,Department of Psychology and Center for Applied NeuroscienceUniversity of CyprusNicosiaCyprus
| | | | - Simon Baron‐Cohen
- Department of Psychiatry, Autism Research CentreUniversity of CambridgeCambridgeUnited Kingdom,Cambridgeshire and Peterborough NHS Foundation TrustCambridgeUnited Kingdom
| | - Declan G. M. Murphy
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational NeurodevelopmentLondonUnited Kingdom,Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Edward T. Bullmore
- Cambridgeshire and Peterborough NHS Foundation TrustCambridgeUnited Kingdom,Department of Psychiatry, Brain Mapping UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - John Suckling
- Cambridgeshire and Peterborough NHS Foundation TrustCambridgeUnited Kingdom,Department of Psychiatry, Brain Mapping UnitUniversity of CambridgeCambridgeUnited Kingdom
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21
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Cariaga-Martinez A, Saiz-Ruiz J, Alelú-Paz R. From Linkage Studies to Epigenetics: What We Know and What We Need to Know in the Neurobiology of Schizophrenia. Front Neurosci 2016; 10:202. [PMID: 27242407 PMCID: PMC4862989 DOI: 10.3389/fnins.2016.00202] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2016] [Accepted: 04/25/2016] [Indexed: 01/15/2023] Open
Abstract
Schizophrenia is a complex psychiatric disorder characterized by the presence of positive, negative, and cognitive symptoms that lacks a unifying neuropathology. In the present paper, we will review the current understanding of molecular dysregulation in schizophrenia, including genetic and epigenetic studies. In relation to the latter, basic research suggests that normal cognition is regulated by epigenetic mechanisms and its dysfunction occurs upon epigenetic misregulation, providing new insights into missing heritability of complex psychiatric diseases, referring to the discrepancy between epidemiological heritability and the proportion of phenotypic variation explained by DNA sequence difference. In schizophrenia the absence of consistently replicated genetic effects together with evidence for lasting changes in gene expression after environmental exposures suggest a role of epigenetic mechanisms. In this review we will focus on epigenetic modifications as a key mechanism through which environmental factors interact with individual's genetic constitution to affect risk of psychotic conditions throughout life.
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Affiliation(s)
- Ariel Cariaga-Martinez
- Laboratory for Neuroscience of Mental Disorders Elena Pessino, Department of Medicine and Medical Specialties, School of Medicine, Alcalá University Madrid, Spain
| | - Jerónimo Saiz-Ruiz
- Department of Psychiatry, Ramón y Cajal Hospital, IRYCISMadrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)Madrid, Spain
| | - Raúl Alelú-Paz
- Laboratory for Neuroscience of Mental Disorders Elena Pessino, Department of Medicine and Medical Specialties, School of Medicine, Alcalá UniversityMadrid, Spain; Department of Psychiatry, Ramón y Cajal Hospital, IRYCISMadrid, Spain
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