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Guo Z, Tang X, Xiao S, Yan H, Sun S, Yang Z, Huang L, Chen Z, Wang Y. Systematic review and meta-analysis: multimodal functional and anatomical neural alterations in autism spectrum disorder. Mol Autism 2024; 15:16. [PMID: 38576034 PMCID: PMC10996269 DOI: 10.1186/s13229-024-00593-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 03/13/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND This meta-analysis aimed to explore the most robust findings across numerous existing resting-state functional imaging and voxel-based morphometry (VBM) studies on the functional and structural brain alterations in individuals with autism spectrum disorder (ASD). METHODS A whole-brain voxel-wise meta-analysis was conducted to compare the differences in the intrinsic functional activity and gray matter volume (GMV) between individuals with ASD and typically developing individuals (TDs) using Seed-based d Mapping software. RESULTS A total of 23 functional imaging studies (786 ASD, 710 TDs) and 52 VBM studies (1728 ASD, 1747 TDs) were included. Compared with TDs, individuals with ASD displayed resting-state functional decreases in the left insula (extending to left superior temporal gyrus [STG]), bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), left angular gyrus and right inferior temporal gyrus, as well as increases in the right supplementary motor area and precuneus. For VBM meta-analysis, individuals with ASD displayed decreased GMV in the ACC/mPFC and left cerebellum, and increased GMV in the left middle temporal gyrus (extending to the left insula and STG), bilateral olfactory cortex, and right precentral gyrus. Further, individuals with ASD displayed decreased resting-state functional activity and increased GMV in the left insula after overlapping the functional and structural differences. CONCLUSIONS The present multimodal meta-analysis demonstrated that ASD exhibited similar alterations in both function and structure of the insula and ACC/mPFC, and functional or structural alterations in the default mode network (DMN), primary motor and sensory regions. These findings contribute to further understanding of the pathophysiology of ASD.
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Affiliation(s)
- Zixuan Guo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinyue Tang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shu Xiao
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hong Yan
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shilin Sun
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zibin Yang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Li Huang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhuoming Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Ying Wang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.
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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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Demler VF, Sterner EF, Wilson M, Zimmer C, Knolle F. Association between increased anterior cingulate glutamate and psychotic-like experiences, but not autistic traits in healthy volunteers. Sci Rep 2023; 13:12792. [PMID: 37550354 PMCID: PMC10406950 DOI: 10.1038/s41598-023-39881-1] [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: 04/14/2023] [Accepted: 08/01/2023] [Indexed: 08/09/2023] Open
Abstract
Despite many differences, autism spectrum disorder and schizophrenia spectrum disorder share environmental risk factors, genetic predispositions as well as neuronal abnormalities, and show similar cognitive deficits in working memory, perspective taking, or response inhibition. These shared abnormalities are already present in subclinical traits of these disorders. The literature proposes that changes in the inhibitory GABAergic and the excitatory glutamatergic system could explain underlying neuronal commonalities and differences. Using magnetic resonance spectroscopy (1H-MRS), we investigated the associations between glutamate concentrations in the anterior cingulate cortex (ACC), the left/right putamen, and left/right dorsolateral prefrontal cortex and psychotic-like experiences (Schizotypal Personality Questionnaire) and autistic traits (Autism Spectrum Quotient) in 53 healthy individuals (26 women). To investigate the contributions of glutamate concentrations in different cortical regions to symptom expression and their interactions, we used linear regression analyses. We found that only glutamate concentration in the ACC predicted psychotic-like experiences, but not autistic traits. Supporting this finding, a binomial logistic regression predicting median-split high and low risk groups for psychotic-like experiences revealed ACC glutamate levels as a significant predictor for group membership. Taken together, this study provides evidence that glutamate levels in the ACC are specifically linked to the expression of psychotic-like experiences, and may be a potential candidate in identifying early risk individuals prone to developing psychotic-like experiences.
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Affiliation(s)
- Verena F Demler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Elisabeth F Sterner
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Franziska Knolle
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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4
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Zhou Z, Li B, Jiang J, Li H, Cao L, Zhang S, Gao Y, Zhang L, Qiu C, Huang X, Gong Q. Abnormal resting-state functional connectivity of the insula in medication-free patients with obsessive-compulsive disorder. BMC Psychiatry 2022; 22:742. [PMID: 36447147 PMCID: PMC9710058 DOI: 10.1186/s12888-022-04341-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/13/2022] [Accepted: 10/26/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The function of the insula has been increasingly mentioned in neurocircuitry models of obsessive-compulsive disorder (OCD) for its role in affective processing and regulating anxiety and its wide interactions with the classic cortico-striato-thalamo-cortical circuit. However, the insular resting-state functional connectivity patterns in OCD remain unclear. Therefore, we aimed to investigate characteristic intrinsic connectivity alterations of the insula in OCD and their associations with clinical features. METHODS We obtained resting-state functional magnetic resonance imaging data from 85 drug-free OCD patients and 85 age- and sex-matched healthy controls (HCs). We performed a general linear model to compare the whole-brain intrinsic functional connectivity maps of the bilateral insula between the OCD and HC groups. In addition, we further explored the relationship between the intrinsic functional connectivity alterations of the insula and clinical features using Pearson or Spearman correlation analysis. RESULTS Compared with HCs, patients with OCD exhibited increased intrinsic connectivity between the bilateral insula and bilateral precuneus gyrus extending to the inferior parietal lobule and supplementary motor area. Decreased intrinsic connectivity was only found between the right insula and bilateral lingual gyrus in OCD patients relative to HC subjects, which was negatively correlated with the severity of depression symptoms in the OCD group. CONCLUSION In the current study, we identified impaired insular intrinsic connectivity in OCD patients and the dysconnectivity of the right insula and bilateral lingual gyrus associated with the depressive severity of OCD patients. These findings provide neuroimaging evidence for the involvement of the insula in OCD and suggest its potential role in the depressive symptoms of OCD.
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Affiliation(s)
- Zilin Zhou
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Bin Li
- grid.412901.f0000 0004 1770 1022Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Jiang
- grid.412901.f0000 0004 1770 1022Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China
| | - Hailong Li
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Lingxiao Cao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Suming Zhang
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Yingxue Gao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Lianqing Zhang
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China
| | - Changjian Qiu
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, 610041, Chengdu, China.
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041, Chengdu, China. .,Psychoradiology Research Unit of the Chinese Academy of Medical Science (2018RU011), West China Hospital of Sichuan University, Chengdu, China. .,Frontiers Science Center for Disease-related Molecular Network, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No.37 Guo Xue Xiang, 610041 Chengdu, China ,grid.412901.f0000 0004 1770 1022Psychoradiology Research Unit of the Chinese Academy of Medical Science (2018RU011), West China Hospital of Sichuan University, Chengdu, China
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5
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Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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6
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Mazza M, Pino MC, Keller R, Vagnetti R, Attanasio M, Filocamo A, Le Donne I, Masedu F, Valenti M. Qualitative Differences in Attribution of Mental States to Other People in Autism and Schizophrenia: What are the Tools for Differential Diagnosis? J Autism Dev Disord 2022; 52:1283-1298. [PMID: 33909212 PMCID: PMC8854268 DOI: 10.1007/s10803-021-05035-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2021] [Indexed: 11/26/2022]
Abstract
The differential diagnosis between schizophrenia spectrum disorders (SSD) and autism spectrum disorders (ASD) remains an important clinical question, because they have overlap in clinical diagnosis. This study explored the differences between ASD (n = 44) and SSD patients (n = 59), compared to typically developing peers (n = 63), in completing an advanced Theory of Mind (ToM) task. The outcome found several differences between groups. The SSD patients showed greater difficulty in understanding social scenarios, while ASD individuals understood the stories, but did not correctly identify the protagonist's intention. The interesting aspect of the results is that some ToM stories are more informative about the mentalistic reasoning of the two clinical groups, namely, the stories that investigate pretend, persuasion, double bluff and ironic joke constructs.
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Affiliation(s)
- Monica Mazza
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
| | - Maria Chiara Pino
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
| | - Roberto Keller
- Reference Centre for Adult Autism of the Piemonte Region, DSM Local Health Unit ASL Città di Torino, Turin, Italy
| | - Roberto Vagnetti
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
| | - Margherita Attanasio
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
| | - Angela Filocamo
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
| | - Ilenia Le Donne
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
| | - Francesco Masedu
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
| | - Marco Valenti
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio, Località Coppito, 67100 L’Aquila, Italy
- Reference Centre for Autism of the Abruzzo Region, Local Health Unit ASL 1, L’Aquila, Italy
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7
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Jutla A, Foss-Feig J, Veenstra-VanderWeele J. Autism spectrum disorder and schizophrenia: An updated conceptual review. Autism Res 2022; 15:384-412. [PMID: 34967130 PMCID: PMC8931527 DOI: 10.1002/aur.2659] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/08/2021] [Accepted: 12/12/2021] [Indexed: 12/19/2022]
Abstract
Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate disorders, with distinct clinical profiles and natural histories. ASD, typically diagnosed in childhood, is characterized by restricted or repetitive interests or behaviors and impaired social communication, and it tends to have a stable course. SCZ, typically diagnosed in adolescence or adulthood, is characterized by hallucinations and delusions, and tends to be associated with declining function. However, youth with ASD are three to six times more likely to develop SCZ than their neurotypical counterparts, and increasingly, research has shown that ASD and SCZ converge at several levels. We conducted a systematic review of studies since 2013 relevant to understanding this convergence, and present here a narrative synthesis of key findings, which we have organized into four broad categories: symptoms and behavior, perception and cognition, biomarkers, and genetic and environmental risk. We then discuss opportunities for future research into the phenomenology and neurobiology of overlap between ASD and SCZ. Understanding this overlap will allow for researchers, and eventually clinicians, to understand the factors that may make a child with ASD vulnerable to developing SCZ. LAY SUMMARY: Autism spectrum disorder and schizophrenia are distinct diagnoses, but people with autism and people with schizophrena share several characteristics. We review recent studies that have examined these areas of overlap, and discuss the kinds of studies we will need to better understand how these disorders are related. Understanding this will be important to help us identify which autistic children are at risk of developing schizophrenia.
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Affiliation(s)
- Amandeep Jutla
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
| | - Jennifer Foss-Feig
- Seaver Autism Center for Research and Treatment, Icahn
School of Medicine at Mount Sinai, Department of Psychiatry, 1 Gustave L. Levy
Place, Box 1230, New York, NY 10029, United States
| | - Jeremy Veenstra-VanderWeele
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
- Center for Autism and the Developing Brain, New
York-Presbyterian Westchester Behavioral Health Center, 21 Bloomingdale Road, White
Plains, NY 10605, United States
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8
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Rinaldi C, Attanasio M, Valenti M, Mazza M, Keller R. Autism spectrum disorder and personality disorders: Comorbidity and differential diagnosis. World J Psychiatry 2021; 11:1366-1386. [PMID: 35070783 PMCID: PMC8717043 DOI: 10.5498/wjp.v11.i12.1366] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/26/2021] [Accepted: 11/24/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Differential diagnosis, comorbidities and overlaps with other psychiatric disorders are common among adults with autism spectrum disorder (ASD), but clinical assessments often omit screening for personality disorders (PD), which are especially common in individuals with high-functioning ASD where there is less need for support. AIM To summarize the research findings on PD in adults with ASD and without intellectual disability, focusing on comorbidity and differential diagnosis. METHODS PubMed searches were performed using the key words "Asperger's Syndrome", "Autism", "Personality", "Personality disorder" and "comorbidity" in order to identify relevant articles published in English. Grey literature was identified through searching Google Scholar. The literature reviews and reference sections of selected papers were also examined for additional potential studies. The search was restricted to studies published up to April 2020. This review is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. RESULTS The search found 22 studies carried out on ASD adults without intellectual disability that met the inclusion criteria: 16 evaluated personality profiles or PD in ASD (comorbidity), five compared ASD and PD (differential diagnosis) and one performed both tasks. There were significant differences in the methodological approaches, including the ASD diagnostic instruments and personality measures. Cluster A and cluster C PD are the most frequent co-occurring PD, but overlapping features should be considered. Data on differential diagnosis were only found with cluster A and cluster B PD. CONCLUSION ASD in high-functioning adults is associated with a distinct personality profile even if variability exists. Further studies are needed to explore the complex relationship between ASD and PD.
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Affiliation(s)
- Camilla Rinaldi
- Adult Autism Center, Department of Mental Health, ASL Città di Torino, Turin 10138, Italy
| | - Margherita Attanasio
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila 67100, Italy
- Regional Centre for Autism, Abruzzo Region Health System, L’Aquila 67100, Italy
| | - Marco Valenti
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila 67100, Italy
- Regional Centre for Autism, Abruzzo Region Health System, L’Aquila 67100, Italy
| | - Monica Mazza
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila 67100, Italy
- Regional Centre for Autism, Abruzzo Region Health System, L’Aquila 67100, Italy
| | - Roberto Keller
- Adult Autism Center, Department of Mental Health, ASL Città di Torino, Turin 10138, Italy
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9
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Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder. Commun Biol 2021; 4:1073. [PMID: 34521980 PMCID: PMC8440519 DOI: 10.1038/s42003-021-02592-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/06/2021] [Indexed: 02/08/2023] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.
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10
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Interhemispheric co-alteration of brain homotopic regions. Brain Struct Funct 2021; 226:2181-2204. [PMID: 34170391 PMCID: PMC8354999 DOI: 10.1007/s00429-021-02318-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/07/2021] [Indexed: 11/11/2022]
Abstract
Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.
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11
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Moors A, Boddez Y. The goal-directed model as an alternative to reductionist and network approaches of psychopathology. Curr Opin Psychol 2021; 41:84-87. [PMID: 33990019 DOI: 10.1016/j.copsyc.2021.03.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/16/2021] [Accepted: 03/29/2021] [Indexed: 11/17/2022]
Abstract
As an alternative to biological reductionist and network approaches to psychopathology, we propose a nonreductionist mental-mechanistic approach. To illustrate this approach, we work out the implications of the goal-directed framework of Moors et al., which has the potential to explain the heterogeneous manifestations of psychopathology with a restricted set of broad theoretical principles.
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Affiliation(s)
- Agnes Moors
- Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium; Center for Social and Cultural Psychology, KU Leuven, Leuven, Belgium.
| | - Yannick Boddez
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium; Center for the Psychology of Learning and Experimental Psychopathology, KU Leuven, Belgium
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12
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Puricelli C, Rolla R, Gigliotti L, Boggio E, Beltrami E, Dianzani U, Keller R. The Gut-Brain-Immune Axis in Autism Spectrum Disorders: A State-of-Art Report. Front Psychiatry 2021; 12:755171. [PMID: 35185631 PMCID: PMC8850385 DOI: 10.3389/fpsyt.2021.755171] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 12/29/2021] [Indexed: 12/20/2022] Open
Abstract
The interest elicited by the large microbial population colonizing the human gut has ancient origins and has gone through a long evolution during history. However, it is only in the last decades that the introduction of high-throughput technologies has allowed to broaden this research field and to disentangle the numerous implications that gut microbiota has in health and disease. This comprehensive ecosystem, constituted mainly by bacteria but also by fungi, parasites, and viruses, is proven to be involved in several physiological and pathological processes that transcend the intestinal homeostasis and are deeply intertwined with apparently unrelated body systems, such as the immune and the nervous ones. In this regard, a novel speculation is the relationship between the intestinal microbial flora and the pathogenesis of some neurological and neurodevelopmental disorders, including the clinical entities defined under the umbrella term of autism spectrum disorders. The bidirectional interplay has led researchers to coin the term gut-brain-immune system axis, subverting the theory of the brain as an immune-privileged site and underscoring the importance of this reciprocal influence already from fetal life and especially during the pre- and post-natal neurodevelopmental process. This revolutionary theory has also unveiled the possibility to modify the gut microbiota as a way to treat and even to prevent different kinds of pathologies. In this sense, some attempts have been made, ranging from probiotic administration to fecal microbiota transplantation, with promising results that need further elaboration. This state-of-art report will describe the main aspects regarding the human gut microbiome and its specific role in the pathogenesis of autism and its related disorders, with a final discussion on the therapeutic and preventive strategies aiming at creating a healthy intestinal microbial environment, as well as their safety and ethical implications.
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Affiliation(s)
- Chiara Puricelli
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy.,Clinical Biochemistry Laboratory, Ospedale Maggiore della Carità, Novara, Italy
| | - Roberta Rolla
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy.,Clinical Biochemistry Laboratory, Ospedale Maggiore della Carità, Novara, Italy
| | - Luca Gigliotti
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy
| | - Elena Boggio
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy
| | - Eleonora Beltrami
- Clinical Biochemistry Laboratory, Ospedale Maggiore della Carità, Novara, Italy
| | - Umberto Dianzani
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy.,Clinical Biochemistry Laboratory, Ospedale Maggiore della Carità, Novara, Italy
| | - Roberto Keller
- Mental Health Department, Adult Autism Center, ASL Città di Torino, Turin, Italy
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13
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Nani A, Manuello J, Mancuso L, Liloia D, Costa T, Vercelli A, Duca S, Cauda F. The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting. Neuroimage 2020; 225:117481. [PMID: 33122115 DOI: 10.1016/j.neuroimage.2020.117481] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/14/2022] Open
Abstract
Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.
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Affiliation(s)
- Andrea Nani
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy.
| | - Alessandro Vercelli
- Neuroscience Institute of Turin, Turin, Italy; Neuroscience Institute Cavalieri Ottolenghi, Turin, Italy; Department of Neuroscience, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Via Verdi, 10, Turin 10124, Italy; Neuroscience Institute of Turin, Turin, Italy
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14
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Cauda F, Nani A, Liloia D, Manuello J, Premi E, Duca S, Fox PT, Costa T. Finding specificity in structural brain alterations through Bayesian reverse inference. Hum Brain Mapp 2020; 41:4155-4172. [PMID: 32829507 PMCID: PMC7502845 DOI: 10.1002/hbm.25105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.
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Affiliation(s)
- Franco Cauda
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Donato Liloia
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali CiviliSpedali Civili HospitalBresciaItaly
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Sergio Duca
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging InstituteUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Tommaso Costa
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
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15
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Cauda F, Mancuso L, Nani A, Ficco L, Premi E, Manuello J, Liloia D, Gelmini G, Duca S, Costa T. Hubs of long-distance co-alteration characterize brain pathology. Hum Brain Mapp 2020; 41:3878-3899. [PMID: 32562581 PMCID: PMC7469792 DOI: 10.1002/hbm.25093] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 05/06/2020] [Accepted: 05/26/2020] [Indexed: 12/14/2022] Open
Abstract
It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.
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Affiliation(s)
- Franco Cauda
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Lorenzo Mancuso
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Linda Ficco
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Enrico Premi
- Stroke Unit, Azienda Socio‐Sanitaria Territoriale Spedali CiviliSpedali Civili HospitalBresciaItaly
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Jordi Manuello
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Donato Liloia
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Gabriele Gelmini
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Sergio Duca
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Tommaso Costa
- GCS‐fMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- FOCUS Lab, Department of PsychologyUniversity of TurinTurinItaly
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16
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Mancuso L, Fornito A, Costa T, Ficco L, Liloia D, Manuello J, Duca S, Cauda F. A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders. Neuroimage 2020; 222:117220. [PMID: 32777357 DOI: 10.1016/j.neuroimage.2020.117220] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/24/2020] [Accepted: 07/28/2020] [Indexed: 12/14/2022] Open
Abstract
Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.
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Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University,Victoria, Australia; Monash Biomedical Imaging, Monash University,Victoria, Australia
| | - Tommaso Costa
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
| | - Linda Ficco
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
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17
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Kaczkurkin AN, Moore TM, Sotiras A, Xia CH, Shinohara RT, Satterthwaite TD. Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth. Biol Psychiatry 2020; 88:51-62. [PMID: 32087950 PMCID: PMC7305976 DOI: 10.1016/j.biopsych.2019.12.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/07/2019] [Accepted: 12/11/2019] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.
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Affiliation(s)
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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18
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Keller R, Chieregato S, Bari S, Castaldo R, Rutto F, Chiocchetti A, Dianzani U. Autism in Adulthood: Clinical and Demographic Characteristics of a Cohort of Five Hundred Persons with Autism Analyzed by a Novel Multistep Network Model. Brain Sci 2020; 10:brainsci10070416. [PMID: 32630229 PMCID: PMC7407178 DOI: 10.3390/brainsci10070416] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/26/2020] [Accepted: 06/27/2020] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in communication and relational skills, associated with repetitive verbal and motor behaviors, restricted patterns of interest, need for a predictable and stable environment, and hypo- or hypersensitivity to sensory inputs. Due to the challenging diagnosis and the paucity of specific interventions, persons with autism (PWA) reaching the adult age often display a severe functional regression. In this scenario, the Regional Center for Autism in Adulthood in Turin seeks to develop a personalized rehabilitation and enablement program for PWA who received a diagnosis of autism in childhood/adolescence or for individuals with suspected adulthood ASD. This program is based on a Multistep Network Model involving PWA, family members, social workers, teachers, and clinicians. Our initial analysis of 500 PWA shows that delayed autism diagnosis and a lack of specific interventions at a young age are largely responsible for the creation of a “lost generation” of adults with ASD, now in dire need of effective psychosocial interventions. As PWA often present with psychopathological co-occurrences or challenging behaviors associated with lack of adequate communication and relational skills, interventions for such individuals should be mainly aimed to improve their self-reliance and social attitude. In particular, preparing PWA for employment, whenever possible, should be regarded as an essential part of the intervention program given the social value of work. Overall, our findings indicate that the development of public centers specialized in assisting and treating PWA can improve the accuracy of ASD diagnosis in adulthood and foster specific habilitative interventions aimed to improve the quality of life of both PWA and their families.
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Affiliation(s)
- Roberto Keller
- Adult Autism Center, Mental Health Department, Health Unit ASL Città di Torino, 10138 Turin, Italy; (R.K.); (S.C.); (S.B.); (R.C.)
| | - Silvia Chieregato
- Adult Autism Center, Mental Health Department, Health Unit ASL Città di Torino, 10138 Turin, Italy; (R.K.); (S.C.); (S.B.); (R.C.)
| | - Stefania Bari
- Adult Autism Center, Mental Health Department, Health Unit ASL Città di Torino, 10138 Turin, Italy; (R.K.); (S.C.); (S.B.); (R.C.)
| | - Romina Castaldo
- Adult Autism Center, Mental Health Department, Health Unit ASL Città di Torino, 10138 Turin, Italy; (R.K.); (S.C.); (S.B.); (R.C.)
| | - Filippo Rutto
- Department of Psychology, University of Turin, 10100 Turin, Italy;
| | - Annalisa Chiocchetti
- Department of Health Sciences, Universita’ del Piemonte Orientale, 28100 Novara, Italy;
| | - Umberto Dianzani
- Department of Health Sciences, Universita’ del Piemonte Orientale, 28100 Novara, Italy;
- Correspondence:
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19
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Cauda F, Nani A, Manuello J, Premi E, Palermo S, Tatu K, Duca S, Fox PT, Costa T. Brain structural alterations are distributed following functional, anatomic and genetic connectivity. Brain 2019; 141:3211-3232. [PMID: 30346490 DOI: 10.1093/brain/awy252] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 08/22/2018] [Indexed: 12/18/2022] Open
Abstract
The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.
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Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy.,Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Sara Palermo
- Department of Neuroscience, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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20
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Corps J, Rekik I. Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants. Sci Rep 2019; 9:9676. [PMID: 31273275 PMCID: PMC6609705 DOI: 10.1038/s41598-019-46145-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 06/24/2019] [Indexed: 11/16/2022] Open
Abstract
Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the ‘data-driven’ brain age using functional MRI (fMRI) and diffusion MRI (dMRI). Particularly, predicting the ‘brain age’ from connectomic data might help identify relevant connectional biomarkers of neurological disorders that emerge early or late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional connectomic data, here we unprecedentedly propose to predict the morphological age of the brain by solely using morphological brain networks (derived from T1-weighted images) in both healthy and disordered populations. Besides, although T1-weighted MRI was widely used for brain age prediction, it was leveraged from an image-based analysis perspective not from a connectomic perspective. Our method includes the following steps: (i) building multi-view morphological brain networks (M-MBN), (ii) feature extraction and selection, (iii) training a machine-learning regression model to predict age from M-MBN data, and (iv) utilizing our model to identify connectional brain features related to age in both autistic and healthy populations. We demonstrate that our method significantly outperforms existing approaches and discovered brain connectional morphological features that fingerprint the age of brain cortical morphology in both autistic and healthy individuals. In particular, we discovered that the connectional cortical thickness best predicts the morphological age of the autistic brain.
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Affiliation(s)
- Joshua Corps
- BASIRA lab, School of Science and Engineering, Computing, University of Dundee, Dundee, UK
| | - Islem Rekik
- BASIRA lab, School of Science and Engineering, Computing, University of Dundee, Dundee, UK. .,Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
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21
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Cauda F, Mancuso L, Nani A, Costa T. Heterogeneous neuroimaging findings, damage propagation and connectivity: an integrative view. Brain 2019; 142:e17. [DOI: 10.1093/brain/awz080] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- Department of Psychology, University of Turin, Turin, Italy
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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22
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Cauda F, Nani A, Manuello J, Liloia D, Tatu K, Vercelli U, Duca S, Fox PT, Costa T. The alteration landscape of the cerebral cortex. Neuroimage 2019; 184:359-371. [PMID: 30237032 PMCID: PMC7384593 DOI: 10.1016/j.neuroimage.2018.09.036] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/24/2018] [Accepted: 09/14/2018] [Indexed: 01/12/2023] Open
Abstract
Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the "structural alteration variety" of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.
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Affiliation(s)
- Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Ugo Vercelli
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, USA; South Texas Veterans Health Care System, USA
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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23
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Zheng Z, Zheng P, Zou X. Association between schizophrenia and autism spectrum disorder: A systematic review and meta-analysis. Autism Res 2018; 11:1110-1119. [PMID: 30284394 DOI: 10.1002/aur.1977] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 01/27/2018] [Accepted: 05/28/2018] [Indexed: 12/27/2022]
Affiliation(s)
- Zhen Zheng
- Department of Pediatrics, The Third Affiliated Hospital; Sun Yat-sen University; Guangzhou Guangdong 510630 China
| | - Peng Zheng
- Department of Science and Technology; South China Agricultural University; Guangzhou Guangdong 510642 China
| | - Xiaobing Zou
- Department of Pediatrics, The Third Affiliated Hospital; Sun Yat-sen University; Guangzhou Guangdong 510630 China
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24
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Cauda F, Nani A, Costa T, Palermo S, Tatu K, Manuello J, Duca S, Fox PT, Keller R. The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders. Hum Brain Mapp 2018; 39:1898-1928. [PMID: 29349864 PMCID: PMC5895505 DOI: 10.1002/hbm.23952] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 12/19/2017] [Accepted: 12/28/2017] [Indexed: 12/13/2022] Open
Abstract
By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.
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Affiliation(s)
- Franco Cauda
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Andrea Nani
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
- Michael Trimble Neuropsychiatry Research Group, University of Birmingham and BSMHFTBirminghamUK
| | - Tommaso Costa
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Sara Palermo
- Department of NeuroscienceUniversity of TurinTurinItaly
| | - Karina Tatu
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Jordi Manuello
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
- Focus Lab, Department of PsychologyUniversity of TurinTurinItaly
| | - Sergio Duca
- GCS‐FMRI, Koelliker Hospital and Department of PsychologyUniversity of TurinTurinItaly
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center At San AntonioSan AntonioTexas
- South Texas Veterans Health Care SystemSan AntonioTexas
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit ASL Citta’ Di TorinoTurinItaly
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25
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Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K, Liloia D, Duca S, Cauda F. The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis. Front Neurol 2018; 8:739. [PMID: 29472885 PMCID: PMC5810291 DOI: 10.3389/fneur.2017.00739] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.
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Affiliation(s)
- Jordi Manuello
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Michael Trimble Neuropsychiatry Research Group, Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Tommaso Costa
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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26
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Morris C, Rekik I. Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks. GRAPHS IN BIOMEDICAL IMAGE ANALYSIS, COMPUTATIONAL ANATOMY AND IMAGING GENETICS 2017. [DOI: 10.1007/978-3-319-67675-3_2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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