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Cho KIK, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02597-3. [PMID: 38830974 DOI: 10.1038/s41380-024-02597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is suggested to represent atypical developmental or degenerative changes accompanying an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate into volume loss is crucial. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Of the CHR individuals, 33 developed psychosis (CHR-P), while 127 did not (CHR-NP). Among all participants, longitudinal data was available for 45 HCs, 17 CHR-P, and 66 CHR-NP. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the CHR-P from the CHR-NP. In addition, for completeness, we also investigated changes in cortical thickness and in white matter (WM) microstructure. At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in many brain areas, the CHR-P group demonstrated significantly accelerated changes (iFW increase and volume reduction) with time than the CHR-NP group. Cortical thickness provided similar results as volume, and there were no significant changes in WM microstructure. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes or microstructural WM changes, and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes, as reflected by the increased iFW, are thus an early pathology at the prodromal stage of psychosis that may be useful for a better mechanistic understanding of psychosis development.
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Affiliation(s)
- Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Matcheri Keshavan
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- The McGovern Institute for Brain Research and the Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Margaret Niznikiewicz
- The Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - William S Stone
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Klaassen AL, Michel C, Stüble M, Kaess M, Morishima Y, Kindler J. Reduced anterior callosal white matter in risk for psychosis associated with processing speed as a fundamental cognitive impairment. Schizophr Res 2024; 264:211-219. [PMID: 38157681 DOI: 10.1016/j.schres.2023.12.026] [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: 08/17/2023] [Revised: 11/29/2023] [Accepted: 12/17/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Previous research in psychotic disorders discovered associations between reduced integrity of white matter (WM) in the corpus callosum (CC) and impaired cognitive functions, suggesting processing speed as a central construct. However, it is still largely unexplored to what extent disruption in callosal WM is related to cognitive deficits during the risk stage prior to psychosis. METHODS To address this gap, we measured the WM integrity in CC by fractional anisotropy (FA) and assessed cognition in 60 clinical-high risk for psychosis (CHR) patients during adolescence/young adulthood and 38 healthy control (HC) subjects. We employed tract based spatial statistics to examine group differences and associations between CC-FA and processing speed, executive function, and spatial working memory. RESULTS We revealed deficits in processing speed, executive function, and spatial working memory of CHR patients, and reductions in FA of the genu and the body of the CC (p < 0.05, corrected for multiple comparisons) compared to HC. A mediation analysis using the combined sample (CHR + HC) showed that processing speed mediates the associations between the impaired CC structure and executive function and spatial working memory, respectively. Exploratory analyses between CC-FA and the cognitive domains located associations of processing speed in the genu and the body of CC with distinct spatial distributions of executive function and spatial working memory. CONCLUSION We suggest processing speed as a subordinate cognitive factor contributing to the associations between callosal WM, executive function and working memory. These results extend findings in psychotic disorders to the prior risk stage.
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Affiliation(s)
- Arndt-Lukas Klaassen
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland.
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland
| | - Miriam Stüble
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland; University Hospital Heidelberg, Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Germany
| | - Yosuke Morishima
- University Hospital of Psychiatry Bern, Department of Psychiatric Neurophysiology, University of Bern, Switzerland
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland
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Kang IC, Pasternak O, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrielli S, Niznikiewicz M, Stone W, Wang J, Shenton M. Microstructural Cortical Gray Matter Changes Preceding Accelerated Volume Changes in Individuals at Clinical High Risk for Psychosis. RESEARCH SQUARE 2023:rs.3.rs-3179575. [PMID: 37841868 PMCID: PMC10571628 DOI: 10.21203/rs.3.rs-3179575/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is thought to result from an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate is crucial, as volume reduction likely indicates an underlying neurodegenerative process. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the 33 individuals at CHR who developed psychosis (CHR-P) from the 127 individuals at CHR who did not (CHR-NP). At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in most brain areas, the CHR-P group demonstrated significantly accelerated iFW increase and volume reduction with time than the CHR-NP group. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes are thus an early pathology at the prodromal stage of psychosis that may be useful for early detection and a better mechanistic understanding of psychosis development.
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Affiliation(s)
| | | | | | | | - Johanna Seitz-Holland
- Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China
| | | | | | | | | | | | | | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
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Sefik E, Boamah M, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, Keshavan MS, Mathalon DH, Perkins DO, Stone WS, Tsuang MT, Woods SW, Cannon TD, Walker EF. Sex- and Age-Specific Deviations in Cerebellar Structure and Their Link With Symptom Dimensions and Clinical Outcome in Individuals at Clinical High Risk for Psychosis. Schizophr Bull 2023; 49:350-363. [PMID: 36394426 PMCID: PMC10016422 DOI: 10.1093/schbul/sbac169] [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] [Indexed: 11/18/2022]
Abstract
BACKGROUND The clinical high-risk (CHR) period offers a temporal window into neurobiological deviations preceding psychosis onset, but little attention has been given to regions outside the cerebrum in large-scale studies of CHR. Recently, the North American Prodrome Longitudinal Study (NAPLS)-2 revealed altered functional connectivity of the cerebello-thalamo-cortical circuitry among individuals at CHR; however, cerebellar morphology remains underinvestigated in this at-risk population, despite growing evidence of its involvement in psychosis. STUDY DESIGN In this multisite study, we analyzed T1-weighted magnetic resonance imaging scans obtained from N = 469 CHR individuals (61% male, ages = 12-36 years) and N = 212 healthy controls (52% male, ages = 12-34 years) from NAPLS-2, with a focus on cerebellar cortex and white matter volumes separately. Symptoms were rated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). The outcome by two-year follow-up was categorized as in-remission, symptomatic, prodromal-progression, or psychotic. General linear models were used for case-control comparisons and tests for volumetric associations with baseline SIPS ratings and clinical outcomes. STUDY RESULTS Cerebellar cortex and white matter volumes differed between the CHR and healthy control groups at baseline, with sex moderating the difference in cortical volumes, and both sex and age moderating the difference in white matter volumes. Baseline ratings for major psychosis-risk dimensions as well as a clinical outcome at follow-up had tissue-specific associations with cerebellar volumes. CONCLUSIONS These findings point to clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals and highlight the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.
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Affiliation(s)
- Esra Sefik
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Michelle Boamah
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
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5
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Liu X, Beheshti I, Zheng W, Li Y, Li S, Zhao Z, Yao Z, Hu B. Brain age estimation using multi-feature-based networks. Comput Biol Med 2022; 143:105285. [PMID: 35158116 DOI: 10.1016/j.compbiomed.2022.105285] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 12/17/2022]
Abstract
Studying brain aging improves our understanding in differentiating typical and atypical aging. Directly utilizing traditional morphological features for brain age estimation did not show significant performance in healthy controls (HCs), which may be due to the negligence of the information of structural similarities among cortical regions. For this issue, the multi-feature-based network (MFN) built upon morphological features can be employed to describe these similarities. Based on this, we hypothesized that the MFN is more efficient and robust than traditional morphological features in brain age estimating. In this work, we used six different types of morphological features (i.e., cortical volume, cortical thickness, curvature index, folding index, local gyrification index, and surface area) to build individual MFN for brain age estimation. The efficacy of MFN was estimated on 2501 HCs with T1-weighted structural magnetic resonance imaging (sMRI) data and compared with traditional morphological features. We attained a mean absolute error (MAE) of 3.73 years using the proposed method on an independent test set, whereas a mean absolute error of 5.30 years was derived from morphological features. Our experimental results demonstrated that the MFN is an efficient and robust metric for estimating brain age.
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Affiliation(s)
- Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Canada
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- School of Computer Science, Qinghai Normal University, Xining, Qinghai Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China.
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6
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Meller T, Schmitt S, Ettinger U, Grant P, Stein F, Brosch K, Grotegerd D, Dohm K, Meinert S, Förster K, Hahn T, Jansen A, Dannlowski U, Krug A, Kircher T, Nenadić I. Brain structural correlates of schizotypal signs and subclinical schizophrenia nuclear symptoms in healthy individuals. Psychol Med 2022; 52:342-351. [PMID: 32578531 PMCID: PMC8842196 DOI: 10.1017/s0033291720002044] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 04/23/2020] [Accepted: 05/27/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Subclinical psychotic-like experiences (PLE), resembling key symptoms of psychotic disorders, are common throughout the general population and possibly associated with psychosis risk. There is evidence that such symptoms are also associated with structural brain changes. METHODS In 672 healthy individuals, we assessed PLE and associated distress with the symptom-checklist-90R (SCL-90R) scales 'schizotypal signs' (STS) and 'schizophrenia nuclear symptoms' (SNS) and analysed associations with voxel- and surfaced-based brain structural parameters derived from structural magnetic resonance imaging at 3 T with CAT12. RESULTS For SNS, we found a positive correlation with the volume in the left superior parietal lobule and the precuneus, and a negative correlation with the volume in the right inferior temporal gyrus [p < 0.05 cluster-level Family Wise Error (FWE-corrected]. For STS, we found a negative correlation with the volume of the left and right precentral gyrus (p < 0.05 cluster-level FWE-corrected). Surface-based analyses did not detect any significant clusters with the chosen statistical threshold of p < 0.05. However, in exploratory analyses (p < 0.001, uncorrected), we found a positive correlation of SNS with gyrification in the left insula and rostral middle frontal gyrus and of STS with the left precuneus and insula, as well as a negative correlation of STS with gyrification in the left temporal pole. CONCLUSIONS Our results show that brain structures in areas implicated in schizophrenia are also related to PLE and its associated distress in healthy individuals. This pattern supports a dimensional model of the neural correlates of symptoms of the psychotic spectrum.
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Affiliation(s)
- Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
| | - Ulrich Ettinger
- Department of Psychology, University of Bonn, Kaiser-Karl-Ring 9, 53111Bonn, Germany
| | - Phillip Grant
- Psychology School, Fresenius University of Applied Sciences, Marienburgstr. 6, 60528Frankfurt am Main, Germany
- Faculty of Life Science Engineering, Technische Hochschule Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Katharina Dohm
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Susanne Meinert
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Katharina Förster
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Tim Hahn
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
- Core-Facility BrainImaging, Faculty of Medicine, Philipps-Universität, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039Marburg, Germany
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7
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Peng H, Ouyang L, Li D, Li Z, Yuan L, Fan L, Liao A, Li J, Wei Y, Yang Z, Ma X, Chen X, He Y. Short-chain fatty acids in patients with schizophrenia and ultra-high risk population. Front Psychiatry 2022; 13:977538. [PMID: 36578297 PMCID: PMC9790925 DOI: 10.3389/fpsyt.2022.977538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Individuals who experience the prodromal phase of schizophrenia (SCZ), a common and complex psychiatric disorder, are referred to as ultra-high-risk (UHR) individuals. Short-chain fatty acid (SCFA) is imperative in the microbiota-gut-brain axis and brain function. Accumulating amount of evidence shows the connections between psychiatric disorders and SCFAs. This study aims to explore the underlying roles SCFAs play in SCZ by investigating the association of alterations in SCFAs concentrations with common cognitive functions in both the SCZ and UHR populations. METHODS The study recruited 59 SCZ patients (including 15 participants converted from the UHR group), 51 UHR participants, and 40 healthy controls (HC) within a complete follow-up of 2 years. Results of cognitive functions, which were assessed by utilizing HVLT-R and TMT, and serum concentrations of SCFAs were obtained for all participants and for UHR individuals at the time of their conversion to SCZ. RESULTS Fifteen UHR participants converted to SCZ within a 2-year follow-up. Valeric acid concentration levels were lower in both the baseline of UHR individuals whom later converted to SCZ (p = 0.046) and SCZ patients (p = 0.036) than the HC group. Additionally, there were lower concentrations of caproic acid in the baseline of UHR individuals whom later transitioned to SCZ (p = 0.019) and the UHR group (p = 0.016) than the HC group. Furthermore, the caproic acid levels in the UHR group are significantly positively correlated with immediate memory (r = 0.355, p = 0.011) and negatively correlated with TMT-B (r = -0.366, p = 0.009). Significant differences in levels of acetic acid, butyric acid and isovaleric acid were absent among the three groups and in UHR individuals before and after transition to SCZ. CONCLUSION Our study suggests that alterations in concentrations of SCFAs may be associated with the pathogenesis and the cognitive impairment of schizophrenia. Further researches are warranted to explore this association. The clinical implications of our findings were discussed.
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Affiliation(s)
- Huiqing Peng
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Lijun Ouyang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - David Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zongchang Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Liu Yuan
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Lejia Fan
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Aijun Liao
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jinguang Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yisen Wei
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zihao Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoqian Ma
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaogang Chen
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ying He
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Psychiatry and Mental Health, China National Technology Institute on Mental Disorders, Institute of Mental Health, Hunan Medical Center for Mental Health, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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9
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Merritt K, Luque Laguna P, Irfan A, David AS. Longitudinal Structural MRI Findings in Individuals at Genetic and Clinical High Risk for Psychosis: A Systematic Review. Front Psychiatry 2021; 12:620401. [PMID: 33603688 PMCID: PMC7884337 DOI: 10.3389/fpsyt.2021.620401] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/08/2021] [Indexed: 01/18/2023] Open
Abstract
Background: Several cross-sectional studies report brain structure differences between healthy volunteers and subjects at genetic or clinical high risk of developing schizophrenia. However, longitudinal studies are important to determine whether altered trajectories of brain development precede psychosis onset. Methods: We conducted a systematic review to determine if brain trajectories differ between (i) those with psychotic experiences (PE), genetic (GHR) or clinical high risk (CHR), compared to healthy volunteers, and (ii) those who transition to psychosis compared to those who do not. Results: Thirty-eight studies measured gray matter and 18 studies measured white matter in 2,473 high risk subjects and 990 healthy volunteers. GHR, CHR, and PE subjects show an accelerated decline in gray matter primarily in temporal, and also frontal, cingulate and parietal cortex. In those who remain symptomatic or transition to psychosis, gray matter loss is more pronounced in these brain regions. White matter volume and fractional anisotropy, which typically increase until early adulthood, did not change or reduced in high risk subjects in the cingulum, thalamic radiation, cerebellum, retrolenticular part of internal capsule, and hippocampal-thalamic tracts. In those who transitioned, white matter volume and fractional anisotropy reduced over time in the inferior and superior fronto-occipital fasciculus, corpus callosum, anterior limb of the internal capsule, superior corona radiate, and calcarine cortex. Conclusion: High risk subjects show deficits in white matter maturation and an accelerated decline in gray matter. Gray matter loss is more pronounced in those who transition to psychosis, but may normalize by early adulthood in remitters.
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Affiliation(s)
- Kate Merritt
- Division of Psychiatry, Institute of Mental Health, University College London, London, United Kingdom
| | - Pedro Luque Laguna
- The Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Ayela Irfan
- Division of Psychiatry, Institute of Mental Health, University College London, London, United Kingdom
| | - Anthony S David
- Division of Psychiatry, Institute of Mental Health, University College London, London, United Kingdom
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10
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Grey-matter abnormalities in clinical high-risk participants for psychosis. Schizophr Res 2020; 226:120-128. [PMID: 31740178 PMCID: PMC7774586 DOI: 10.1016/j.schres.2019.08.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 08/28/2019] [Accepted: 08/31/2019] [Indexed: 01/10/2023]
Abstract
The current study examined the presence of abnormalities in cortical grey-matter (GM) in a sample of clinical high-risk (CHR) participants and examined relationships with psychosocial functioning and neurocognition. CHR-participants (n = 114), participants who did not fulfil CHR-criteria (CHR-negative) (n = 39) as well as a group of healthy controls (HC) (n = 49) were recruited. CHR-status was assessed using the Comprehensive Assessment of At-Risk Mental State (CAARMS) and the Schizophrenia Proneness Interview, Adult Version (SPI-A). The Brief Assessment of Cognition in Schizophrenia Battery (BACS) as well as tests for emotion recognition, working memory and attention were administered. In addition, role and social functioning as well as premorbid adjustment were assessed. No significant differences in GM-thickness and intensity were observed in CHR-participants compared to CHR-negative and HC. Circumscribed abnormalities in GM-intensity were found in the visual and frontal cortex of CHR-participants. Moreover, small-to-moderate correlations were observed between GM-intensity and neuropsychological deficits in the CHR-group. The current data suggest that CHR-participants may not show comprehensive abnormalities in GM. We discuss the implications of these findings for the pathophysiological theories of early stage-psychosis as well as methodological issues and the impact of different recruitment strategies.
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11
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Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art. Biol Psychiatry 2020; 88:349-360. [PMID: 32305218 DOI: 10.1016/j.biopsych.2020.02.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/25/2020] [Accepted: 02/06/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied. METHODS We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. RESULTS A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects. CONCLUSIONS Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.
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12
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Buechler R, Wotruba D, Michels L, Theodoridou A, Metzler S, Walitza S, Hänggi J, Kollias S, Rössler W, Heekeren K. Cortical Volume Differences in Subjects at Risk for Psychosis Are Driven by Surface Area. Schizophr Bull 2020; 46:1511-1519. [PMID: 32463880 PMCID: PMC7846193 DOI: 10.1093/schbul/sbaa066] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In subjects at risk for psychosis, the studies on gray matter volume (GMV) predominantly reported volume loss compared with healthy controls (CON). However, other important morphological measurements such as cortical surface area (CSA) and cortical thickness (CT) were not systematically compared. So far, samples mostly comprised subjects at genetic risk or at clinical risk fulfilling an ultra-high risk (UHR) criterion. No studies comparing UHR subjects with at-risk subjects showing only basic symptoms (BS) investigated the differences in CSA or CT. Therefore, we aimed to unravel the contribution of the 2 morphometrical measures constituting the cortical volume (CV) and to test whether these groups inhere different morphometric features. We conducted a surface-based morphometric analysis in 34 CON, 46 BS, and 39 UHR to examine between-group differences in CV, CSA, and CT vertex-wise across the whole cortex. Compared with BS and CON, UHR individuals presented increased CV in frontal and parietal regions, which was driven by larger CSA. These groups did not differ in CT. Yet, at-risk subjects who later developed schizophrenia showed thinning in the occipital cortex. Furthermore, BS presented increased CSA compared with CON. Our results suggest that volumetric differences in UHR subjects are driven by CSA while CV loss in converters seems to be based on cortical thinning. We attribute the larger CSA in UHR to aberrant pruning representing a vulnerability to develop psychotic symptoms reflected in different levels of vulnerability for BS and UHR, and cortical thinning to a presumably stress-related cortical decomposition.
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Affiliation(s)
- Roman Buechler
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland,To whom correspondence should be addressed; UniversitätsSpital Zürich Klinik für Neuroradiologie Frauenklinikstrasse 10, Zurich 8091, Switzerland; tel: +41-44-255-56-00, fax +41-44-255-45-04, e-mail:
| | - Diana Wotruba
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Anastasia Theodoridou
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Sibylle Metzler
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland
| | - Jürgen Hänggi
- Department of Psychology, Division of Neuropsychology, University of Zurich, Zurich, Switzerland
| | - Spyros Kollias
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Wulf Rössler
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland,Laboratory of Neuroscience (LIM-27), Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil
| | - Karsten Heekeren
- The Zurich Program for Sustainable Development of Mental Health Services (ZInEP), University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Psychiatry and Psychotherapy I, LVR-Hospital, Cologne, Germany
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13
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Meisenzahl E, Walger P, Schmidt SJ, Koutsouleris N, Schultze-Lutter F. [Early recognition and prevention of schizophrenia and other psychoses]. DER NERVENARZT 2019; 91:10-17. [PMID: 31858162 DOI: 10.1007/s00115-019-00836-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The last two decades of clinical research have clearly demonstrated the comprehensive benefits of the early recognition and treatment of psychotic disorders. The attenuated and transient positive symptoms according to the ultrahigh risk criteria and the basic symptom criterion "Cognitive disturbances" are the main approaches for an indicated prevention. They have recently been recommended as criteria for a clinical high-risk (CHR) state of psychosis by the European Psychiatric Association (EPA) and, following these, in the German S3 guidelines for the treatment of schizophrenia by the German Association for Psychiatry, Psychotherapy and Psychosomatics (DGPPN); however, the efficacy of early treatment of patients with a CHR for psychoses critically depends on the development of prognostic instruments, which enable healthcare professionals to reliably identify these patients based on the individual objective risk profiles. An important goal is the treatment of functional deficits, which can be identified by an individual risk profile. The treatment of existing comorbid mental disorders, psychosocial problems and the prevention of potential future disorders also characterizes the recommendations of the EPA and DGPPN for early treatment, which favor psychotherapeutic, especially cognitive behavioral interventions over pharmacological treatment. The close interdisciplinary cross-sectoral cooperation between the disciplines of child and adolescent psychiatry, and adult psychiatry is of outstanding importance in this context.
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Affiliation(s)
- E Meisenzahl
- Klinik für Psychiatrie und Psychotherapie, medizinische Fakultät, Heinrich-Heine Universität/LVR Düsseldorf, Bergische Landstr. 2, 40629, Düsseldorf, Deutschland.
| | - P Walger
- Klinik für Psychiatrie und Psychotherapie, medizinische Fakultät, Heinrich-Heine Universität/LVR Düsseldorf, Bergische Landstr. 2, 40629, Düsseldorf, Deutschland
| | - S J Schmidt
- Abtlg. für Klinische Psychologie und Psychotherapie, Institut für Psychologie, Universität Bern, Bern, Schweiz
| | - N Koutsouleris
- Klinik für Psychiatrie und Psychotherapie, Klinikum der Universität München, München, Deutschland
| | - F Schultze-Lutter
- Klinik für Psychiatrie und Psychotherapie, medizinische Fakultät, Heinrich-Heine Universität/LVR Düsseldorf, Bergische Landstr. 2, 40629, Düsseldorf, Deutschland
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14
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Tunç B, Yankowitz LD, Parker D, Alappatt JA, Pandey J, Schultz RT, Verma R. Deviation from normative brain development is associated with symptom severity in autism spectrum disorder. Mol Autism 2019; 10:46. [PMID: 31867092 PMCID: PMC6907209 DOI: 10.1186/s13229-019-0301-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 11/29/2019] [Indexed: 12/13/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = - 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.
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Affiliation(s)
- Birkan Tunç
- Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Lisa D. Yankowitz
- Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Drew Parker
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Jacob A. Alappatt
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Juhi Pandey
- Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Robert T. Schultz
- Center for Autism Research, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104 USA
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
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15
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Shan XX, Ou YP, Pan P, Ding YD, Zhao J, Liu F, Chen JD, Guo WB, Zhao JP. Increased frontal gray matter volume in individuals with prodromal psychosis. CNS Neurosci Ther 2019; 25:987-994. [PMID: 31129924 PMCID: PMC6698969 DOI: 10.1111/cns.13143] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 03/27/2019] [Accepted: 04/07/2019] [Indexed: 01/10/2023] Open
Abstract
Background Brain anatomical deficits associated with cognitive dysfunction have been reported in patients with schizophrenia. However, it remains unknown whether such anatomical deficits exist in individuals with prodromal psychosis. The present study is designed to investigate anatomical deficits in prodromal individuals and their associations with clinical/cognitive features. Methods Seventy‐four prodromal individuals and seventy‐six healthy controls were scanned using structural magnetic resonance imaging. Support vector machines were applied to test whether anatomical deficits might be used to discriminate prodromal individuals from healthy controls. Results Prodromal individuals showed significantly increased gray matter volume (GMV) in the right inferior frontal gyrus (IFG) and right rectus gyrus relative to healthy controls. No correlations were observed between increased GMV and clinical/cognitive characteristics. The combination of increased GMV in the right rectus gyrus and right IFG showed a sensitivity of 74.32%, a specificity of 67.11%, and an accuracy of 70.67% in differentiating prodromal individuals from healthy controls. Conclusion Our results provide evidence of increased frontal GMV in prodromal individuals. A combination of GMV values in the two frontal brain areas may serve as potential markers to discriminate prodromal individuals from healthy controls. The results thus highlight the importance of the frontal regions in the pathophysiology of psychosis.
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Affiliation(s)
- Xiao-Xiao Shan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Yang-Pan Ou
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Pan Pan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Yu-Dan Ding
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Jin Zhao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jin-Dong Chen
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Wen-Bin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
| | - Jing-Ping Zhao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center on Mental Disorders, Changsha, China
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16
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Rozycki M, Satterthwaite TD, Koutsouleris N, Erus G, Doshi J, Wolf DH, Fan Y, Gur RE, Gur RC, Meisenzahl EM, Zhuo C, Yin H, Yan H, Yue W, Zhang D, Davatzikos C. Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals. Schizophr Bull 2018; 44:1035-1044. [PMID: 29186619 PMCID: PMC6101559 DOI: 10.1093/schbul/sbx137] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case-control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification. Analyses were conducted at multiple scales, including regional volumes, voxelwise measures, and complex distributed patterns. Single-subject classification was tested for single-site, pooled-site, and leave-site-out generalizability. Regional and voxelwise analyses revealed a pattern of widespread reduced regional gray matter volume, particularly in the medial prefrontal, temporolimbic and peri-Sylvian cortex, along with ventricular and pallidum enlargement. Multivariate classification using pooled data achieved a cross-validated prediction accuracy of 76% (AUC = 0.84). Critically, the leave-site-out validation of the detected schizophrenia signature showed accuracy/AUC range of 72-77%/0.73-0.91, suggesting a robust generalizability across sites and patient cohorts. Finally, individualized patient classifications displayed significant correlations with clinical measures of negative, but not positive, symptoms. Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia. A web-accessible portal is offered to allow the community to obtain individualized classifications of magnetic resonance imaging scans using the methods described herein.
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Affiliation(s)
- Martin Rozycki
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA,Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA,Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Raquel E Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Ruben C Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Eva M Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University Dusseldorf, Dusseldorf, Germany
| | | | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Hao Yan
- Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Sixth Hospital, Peking University, Beijing, China
| | - Weihua Yue
- Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Sixth Hospital, Peking University, Beijing, China
| | - Dai Zhang
- Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Sixth Hospital, Peking University, Beijing, China
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA,To whom correspondence should be addressed; University of Pennsylvania, Richards Building, 7th Floor, 3700 Hamilton Walk, Philadelphia, PA 19104; tel: 215-746-4067, fax: 215-746-4060, e-mail:
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Structural and functional alterations in the brain during working memory in medication-naïve patients at clinical high-risk for psychosis. PLoS One 2018; 13:e0196289. [PMID: 29742121 PMCID: PMC5942777 DOI: 10.1371/journal.pone.0196289] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 04/10/2018] [Indexed: 12/18/2022] Open
Abstract
Several previous studies suggest that clinical high risk for psychosis (CHR) is associated with prefrontal functional abnormalities and more widespread reduced grey matter in prefrontal, temporal and parietal areas. We investigated neural correlates to CHR in medication-naïve patients. 41 CHR patients and 37 healthy controls were examined with 1.5 Tesla MRI, yielding functional scans while performing an N-back task and structural T1-weighted brain images. Functional and structural data underwent automated preprocessing steps in SPM and Freesurfer, correspondingly. The groups were compared employing mass-univariate strategy within the generalized linear modelling framework. CHR demonstrated reduced suppression of the medial temporal lobe (MTL) regions during n-back task. We also found that, consistent with previous findings, CHR subjects demonstrated thinning in prefrontal, cingulate, insular and inferior temporal areas, as well as reduced hippocampal volumes. The present findings add to the growing evidence of specific structural and functional abnormalities in the brain as potential neuroimaging markers of psychosis vulnerability.
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18
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Diagnosis of Schizophrenia Disorder in MR Brain Images Using Multi-objective BPSO Based Feature Selection with Fuzzy SVM. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0355-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Hasan A, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, Frank E, Cordes J, Wölwer W, Musso F, Winterer G, Gaebel W, Hajak G, Ohmann C, Verde PE, Rietschel M, Ahmed R, Honer WG, Dechent P, Malchow B, Castro MFU, Dwyer D, Cabral C, Kreuzer PM, Poeppl TB, Schneider-Axmann T, Falkai P, Koutsouleris N. Structural brain changes are associated with response of negative symptoms to prefrontal repetitive transcranial magnetic stimulation in patients with schizophrenia. Mol Psychiatry 2017; 22:857-864. [PMID: 27725655 DOI: 10.1038/mp.2016.161] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 07/06/2016] [Accepted: 08/04/2016] [Indexed: 12/13/2022]
Abstract
Impaired neural plasticity may be a core pathophysiological process underlying the symptomatology of schizophrenia. Plasticity-enhancing interventions, including repetitive transcranial magnetic stimulation (rTMS), may improve difficult-to-treat symptoms; however, efficacy in large clinical trials appears limited. The high variability of rTMS-related treatment response may be related to a comparably large variation in the ability to generate plastic neural changes. The aim of the present study was to determine whether negative symptom improvement in schizophrenia patients receiving rTMS to the left dorsolateral prefrontal cortex (DLPFC) was related to rTMS-related brain volume changes. A total of 73 schizophrenia patients with predominant negative symptoms were randomized to an active (n=34) or sham (n=39) 10-Hz rTMS intervention applied 5 days per week for 3 weeks to the left DLPFC. Local brain volume changes measured by deformation-based morphometry were correlated with changes in negative symptom severity using a repeated-measures analysis of covariance design. Volume gains in the left hippocampal, parahippocampal and precuneal cortices predicted negative symptom improvement in the active rTMS group (all r⩽-0.441, all P⩽0.009), but not the sham rTMS group (all r⩽0.211, all P⩾0.198). Further analyses comparing negative symptom responders (⩾20% improvement) and non-responders supported the primary analysis, again only in the active rTMS group (F(9, 207)=2.72, P=0.005, partial η 2=0.106). Heterogeneity in clinical response of negative symptoms in schizophrenia to prefrontal high-frequency rTMS may be related to variability in capacity for structural plasticity, particularly in the left hippocampal region and the precuneus.
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Affiliation(s)
- A Hasan
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - T Wobrock
- Department of Psychiatry and Psychotherapy, Georg-August-University Göttingen, Göttingen, Germany.,Department of Psychiatry and Psychotherapy, County Hospitals Darmstadt-Dieburg, Groß-Umstadt, Germany
| | - B Guse
- Department of Psychiatry and Psychotherapy, Georg-August-University Göttingen, Göttingen, Germany
| | - B Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - M Landgrebe
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.,Department of Psychiatry, Psychosomatics and Psychotherapy, kbo-Lech-Mangfall-Klinik Agatharied, Agatharied, Germany
| | - P Eichhammer
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - E Frank
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - J Cordes
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - W Wölwer
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - F Musso
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - G Winterer
- Experimental and Clinical Research Centre, The Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - W Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - G Hajak
- Department of Psychiatry, Psychosomatics and Psychotherapy, Sozialstiftung Bamberg, Bamberg, Germany
| | - C Ohmann
- European Clinical Research Network, Düsseldorf, Germany
| | - P E Verde
- Coordination Centre for Clinical Trials, Heinrich-Heine University, Düsseldorf, Germany
| | - M Rietschel
- Department of Genetic Epidemiology in Psychiatry, Institute of Central Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - R Ahmed
- Institut für anwendungsorientierte Forschung und klinische Studien GmbH, Göttingen, Germany
| | - W G Honer
- Department of Genetic Epidemiology in Psychiatry, Institute of Mental Health, The University of British Columbia, Vancouver, BC, Canada
| | - P Dechent
- Department of Cognitive Neurology, Georg-August-University Goettingen, Goettingen, Germany
| | - B Malchow
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - M F U Castro
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - D Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - C Cabral
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - P M Kreuzer
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - T B Poeppl
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - T Schneider-Axmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - P Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - N Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
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20
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Kambeitz-Ilankovic L, Meisenzahl EM, Cabral C, von Saldern S, Kambeitz J, Falkai P, Möller HJ, Reiser M, Koutsouleris N. Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophr Res 2016; 173:159-165. [PMID: 25819936 DOI: 10.1016/j.schres.2015.03.005] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 03/05/2015] [Accepted: 03/08/2015] [Indexed: 01/11/2023]
Abstract
To date, research into the biomarker-aided early recognition of psychosis has focused on predicting the transition likelihood of clinically defined individuals with different at-risk mental states (ARMS) based on structural (and functional) brain changes. However, it is currently unknown whether neuroimaging patterns could be identified to facilitate the individualized prediction of symptomatic and functional recovery. Therefore, we investigated whether cortical surface alterations analyzed by means of multivariate pattern recognition methods could enable the single-subject identification of functional outcomes in twenty-seven ARMS individuals. Subjects were dichotomized into 'good' vs. 'poor' outcome groups on average 4years after the baseline MRI scan using a Global Assessment of Functioning (GAF) threshold of 70. Cortical surface-based pattern classification predicted good (N=14) vs. poor outcome status (N=13) at follow-up with an accuracy of 82% as determined by nested leave-one-cross-validation. Neuroanatomical prediction involved cortical area reductions in superior temporal, inferior frontal and inferior parietal areas and was not confounded by functional impairment at baseline, or antipsychotic medication and transition status over the follow-up period. The prediction model's decision scores were correlated with positive and general symptom scores in the ARMS group at follow-up, whereas negative symptoms were not linked to predicted poorer functional outcome. These findings suggest that poorer functional outcomes are associated with non-resolving attenuated psychosis and could be predicted at the single-subject level using multivariate neuroanatomical risk stratification methods. However, the generalizability and specificity of the suggested prediction model should be thoroughly investigated in future large-scale and cross-diagnostic MRI studies.
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Affiliation(s)
| | - Eva M Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Carlos Cabral
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Sebastian von Saldern
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Maximilian Reiser
- Department of Radiology, Ludwig-Maximilian-University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
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21
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Woodberry KA, Shapiro DI, Bryant C, Seidman LJ. Progress and Future Directions in Research on the Psychosis Prodrome: A Review for Clinicians. Harv Rev Psychiatry 2016; 24:87-103. [PMID: 26954594 PMCID: PMC4870599 DOI: 10.1097/hrp.0000000000000109] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
LEARNING OBJECTIVES After participating in this activity, learners should be better able to: ABSTRACT The psychosis prodrome, or period of clinical and functional decline leading up to acute psychosis, offers a unique opportunity for identifying mechanisms of psychosis onset and for testing early-intervention strategies. We summarize major findings and emerging directions in prodromal research and provide recommendations for clinicians working with individuals suspected to be at high risk for psychosis. The past two decades of research have led to three major advances. First, tools and criteria have been developed that can reliably identify imminent risk for a psychotic disorder. Second, longitudinal clinical and psychobiological data from large multisite studies are strengthening individual risk assessment and offering insights into potential mechanisms of illness onset. Third, psychosocial and pharmacological interventions are demonstrating promise for delaying or preventing the onset of psychosis in help-seeking, high-risk individuals. The dynamic psychobiological processes implicated in both risk and onset of psychosis, including altered gene expression, cognitive dysfunction, inflammation, gray and white matter brain changes, and vulnerability-stress interactions suggest a wide range of potential treatment targets and strategies. The expansion of resources devoted to early intervention and prodromal research worldwide raises hope for investigating them. Future directions include identifying psychosis-specific risk and resilience factors in children, adolescents, and non-help-seeking community samples, improving study designs to test hypothesized mechanisms of change, and intervening with strategies that, in order to improve functional outcomes, better engage youth, address their environmental contexts, and focus on evidence-based neurodevelopmental targets. Prospective research on putatively prodromal samples has the potential to substantially reshape our understanding of mental illness and our efforts to combat it.
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Affiliation(s)
- Kristen A Woodberry
- From Harvard Medical School (Drs. Woodberry, Shapiro, and Seidman) and Beth Israel Deaconess Medical Center (Drs. Woodberry, Shapiro, and Seidman, and Ms. Bryant)
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22
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Valli I, Marquand AF, Mechelli A, Raffin M, Allen P, Seal ML, McGuire P. Identifying Individuals at High Risk of Psychosis: Predictive Utility of Support Vector Machine using Structural and Functional MRI Data. Front Psychiatry 2016; 7:52. [PMID: 27092086 PMCID: PMC4824756 DOI: 10.3389/fpsyt.2016.00052] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 03/23/2016] [Indexed: 01/20/2023] Open
Abstract
The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is, therefore, increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an at-risk mental state for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analyzed using a standard univariate analysis, and with support vector machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p < 0.001). Univariate volumetric between-group differences did not reach statistical significance. By contrast, the univariate fMRI analysis identified between-group differences (p < 0.05 corrected), while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis.
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Affiliation(s)
- Isabel Valli
- Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands; Centre for Neuroimaging Sciences, King's College London, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , UK
| | - Marie Raffin
- Service de Psychiatrie de l'enfant et de l'adolescent, CHU Pitié-Salpêtrière , Paris , France
| | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , UK
| | - Marc L Seal
- Developmental Imaging Research Group, Murdoch Children Research Institute, University of Melbourne , Melbourne, VIC , Australia
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , UK
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23
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Pawełczyk A, Kotlicka-Antczak M, Rabe-Jabłońska J, Pawełczyk T, Ruszpel A, Łojek E. Figural fluency and immediate visual memory in patients with at-risk mental state for psychosis: empirical study. Early Interv Psychiatry 2015; 9:324-30. [PMID: 24373200 DOI: 10.1111/eip.12116] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 10/30/2013] [Indexed: 11/27/2022]
Abstract
AIM Although a number of cognitive functions have been assessed in the ultra-high risk (UHR) population, only one study has reported on figural fluency. Visual memory was measured by different tests providing inconsistent results. The aim of the present study was to compare figural fluency and visual immediate memory performance in UHR patients and normal subjects. METHODS The UHR sample consisted of 55 help-seeking individuals meeting CAARMS criteria. The control group consisted of 65 subjects. They were matched as a group by age, gender and education level. Figural fluency (RFFT) and immediate visual memory (BVRT) were assessed within 2 weeks after inclusion in the study in the UHR patient group. RESULTS Significant differences were obtained in RFFT and BVRT results. In BVRT, UHR patients scored lower in number of correct designs (P < 0.001) and higher in number of errors (P < 0.0001), especially omissions (P < 0.001) and distortions (P < 0.0001). UHR subjects accurately recalled fewer designs, omitted and distorted more test figures. In RFFT, they scored lower in production of novel designs (P < 0.0001) and higher in the error ratio index (P < 0.008). They produced fewer novel designs and made more preservative errors. CONCLUSIONS The current study concerns non-verbal cognitive functions in UHR samples. Our results suggest that figural fluency and visual immediate memory are impaired in help-seeking UHR individuals as compared with matched controls.
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Affiliation(s)
- Agnieszka Pawełczyk
- Department of Affective and Psychotic Disorders, Medical University of Lodz, Lodz
| | | | | | - Tomasz Pawełczyk
- Department of Affective and Psychotic Disorders, Medical University of Lodz, Lodz
| | - Anna Ruszpel
- Department of Cognitive Neuropsychology, University of Warsaw, Warsaw, Poland
| | - Emila Łojek
- Department of Cognitive Neuropsychology, University of Warsaw, Warsaw, Poland
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24
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Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. BRAIN : A JOURNAL OF NEUROLOGY 2015. [PMID: 25935725 DOI: 10.1093/brain/awv111)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.
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Affiliation(s)
| | - Eva M Meisenzahl
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | | | | | - Thomas Frodl
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany 3 Department of Psychiatry and Psychotherapy, University of Regensburg, Germany 4 Department of Psychiatry, University Dublin, Trinity College Dublin, Ireland
| | - Joseph Kambeitz
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Yanis Köhler
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Peter Falkai
- 2 Department of Psychiatry, University of Basel, Switzerland
| | - Hans-Jürgen Möller
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Maximilian Reiser
- 5 Department of Radiology, Ludwig-Maximilian-University, Munich, Germany
| | - Christos Davatzikos
- 6 Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, USA
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25
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Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 2015; 138:2059-73. [PMID: 25935725 DOI: 10.1093/brain/awv111] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 02/28/2015] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.
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Affiliation(s)
| | - Eva M Meisenzahl
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | | | | | - Thomas Frodl
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany 3 Department of Psychiatry and Psychotherapy, University of Regensburg, Germany 4 Department of Psychiatry, University Dublin, Trinity College Dublin, Ireland
| | - Joseph Kambeitz
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Yanis Köhler
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Peter Falkai
- 2 Department of Psychiatry, University of Basel, Switzerland
| | - Hans-Jürgen Möller
- 1 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Germany
| | - Maximilian Reiser
- 5 Department of Radiology, Ludwig-Maximilian-University, Munich, Germany
| | - Christos Davatzikos
- 6 Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, USA
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26
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Saybani MR, Shamshirband S, Golzari Hormozi S, Wah TY, Aghabozorgi S, Pourhoseingholi MA, Olariu T. Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system. IRANIAN RED CRESCENT MEDICAL JOURNAL 2015; 17:e24557. [PMID: 26023340 PMCID: PMC4443397 DOI: 10.5812/ircmj.17(4)2015.24557] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 01/28/2015] [Accepted: 02/07/2015] [Indexed: 12/04/2022]
Abstract
Background: Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. Objectives: In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patients and Methods: Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden’s Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. Results: With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden’s Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. Conclusions: There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.
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Affiliation(s)
- Mahmoud Reza Saybani
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia
- Corresponding Author: Mahmoud Reza Saybani, Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia. E-mail:
| | | | - Shahram Golzari Hormozi
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Hormozgan, Bandar Abbas, IR Iran
| | - Teh Ying Wah
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia
| | - Saeed Aghabozorgi
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
| | - Teodora Olariu
- Department of Intensive Care, Faculty of Medicine, Western University of Arad, Arad, Romania
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27
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Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, Falkai P, Riecher-Rössler A, Möller HJ, Reiser M, Pantelis C, Meisenzahl E. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull 2014; 40:1140-53. [PMID: 24126515 PMCID: PMC4133663 DOI: 10.1093/schbul/sbt142] [Citation(s) in RCA: 286] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany;
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Christian Gaser
- Structural Brain Imaging Group, Department of Psychiatry and Neurology, FriedrichSchillerUniversity, Jena, Germany
| | - Ronald Bottlender
- Department of Psychiatry and Psychotherapy, Ludwig-MaximilianUniversity, Munich, Germany
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Ludwig-MaximilianUniversity, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-MaximilianUniversity, Munich, Germany
| | | | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, Ludwig-MaximilianUniversity, Munich, Germany
| | - Maximilian Reiser
- Department of Radiology, Ludwig-MaximilianUniversity, Munich, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Victoria, Australia
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-MaximilianUniversity, Munich, Germany
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[Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis]. DER NERVENARZT 2014; 85:714-9. [PMID: 24849118 DOI: 10.1007/s00115-014-4022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
BACKGROUND Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. OBJECTIVES Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. MATERIAL AND METHODS Literature review of current studies. RESULTS Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. CONCLUSION The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.
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Scariati E, Schaer M, Richiardi J, Schneider M, Debbané M, Van De Ville D, Eliez S. Identifying 22q11.2 Deletion Syndrome and Psychosis Using Resting-State Connectivity Patterns. Brain Topogr 2014; 27:808-21. [DOI: 10.1007/s10548-014-0356-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Accepted: 02/12/2014] [Indexed: 11/30/2022]
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Wang Q, Cheung C, Deng W, Li M, Huang C, Ma X, Wang Y, Jiang L, Sham PC, Collier DA, Gong Q, Chua SE, McAlonan GM, Li T. White-matter microstructure in previously drug-naive patients with schizophrenia after 6 weeks of treatment. Psychol Med 2013; 43:2301-2309. [PMID: 23442742 DOI: 10.1017/s0033291713000238] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND It is not clear whether the progressive changes in brain microstructural deficits documented in previous longitudinal magnetic resonance imaging (MRI) studies might be due to the disease process or to other factors such as medication. It is important to explore the longitudinal alterations in white-matter (WM) microstructure in antipsychotic-naive patients with first-episode schizophrenia during the very early phase of treatment when relatively 'free' from chronicity. METHOD Thirty-five patients with first-episode schizophrenia and 22 healthy volunteers were recruited. High-resolution diffusion tensor imaging (DTI) was obtained from participants at baseline and after 6 weeks of treatment. A 'difference map' for each individual was calculated from the 6-week follow-up fractional anisotropy (FA) of DTI minus the baseline FA. Differences in Positive and Negative Syndrome Scale (PANSS) scores and Global Assessment of Functioning (GAF) scores between baseline and 6 weeks were also evaluated and expressed as a 6-week/baseline ratio. RESULTS Compared to healthy controls, there was a significant decrease in absolute FA of WM around the bilateral anterior cingulate gyrus and the right anterior corona radiata of the frontal lobe in first-episode drug-naive patients with schizophrenia following 6 weeks of treatment. Clinical symptoms improved during this period but the change in FA did not correlate with the changes in clinical symptoms or the dose of antipsychotic medication. CONCLUSIONS During the early phase of treatment, there is an acute reduction in WM FA that may be due to the effects of antipsychotic medications. However, it is not possible to entirely exclude the effects of underlying progression of illness.
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Affiliation(s)
- Q Wang
- Mental Health Centre and Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Ziegler G, Dahnke R, Winkler A, Gaser C. Partial least squares correlation of multivariate cognitive abilities and local brain structure in children and adolescents. Neuroimage 2013; 82:284-94. [DOI: 10.1016/j.neuroimage.2013.05.088] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Revised: 05/02/2013] [Accepted: 05/21/2013] [Indexed: 11/25/2022] Open
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Brent BK, Thermenos HW, Keshavan MS, Seidman LJ. Gray Matter Alterations in Schizophrenia High-Risk Youth and Early-Onset Schizophrenia: A Review of Structural MRI Findings. Child Adolesc Psychiatr Clin N Am 2013; 22:689-714. [PMID: 24012081 PMCID: PMC3767930 DOI: 10.1016/j.chc.2013.06.003] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This article reviews the literature on structural magnetic resonance imaging findings in pediatric and young adult populations at clinical or genetic high-risk for schizophrenia and early-onset schizophrenia. The implications of this research are discussed for understanding the pathophysiology of schizophrenia and for early intervention strategies. The evidence linking brain structural changes in prepsychosis development and early-onset schizophrenia with disruptions of normal neurodevelopmental processes during childhood or adolescence is described. Future directions are outlined for research to address knowledge gaps regarding the neurobiological basis of brain structural abnormalities in schizophrenia and to improve the usefulness of these abnormalities for preventative interventions.
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Affiliation(s)
- Benjamin K Brent
- Harvard Medical School, Boston, MA 02115, USA; Division of Public Psychiatry, Massachusetts Mental Health Center, 75 Fenwood Road, Boston, MA 02115, USA; Department of Psychiatry, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA.
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Zarogianni E, Moorhead TW, Lawrie SM. Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage Clin 2013; 3:279-89. [PMID: 24273713 PMCID: PMC3814947 DOI: 10.1016/j.nicl.2013.09.003] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2013] [Revised: 09/05/2013] [Accepted: 09/06/2013] [Indexed: 12/23/2022]
Abstract
Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.
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Affiliation(s)
- Eleni Zarogianni
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, Scotland, UK
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Yao L, Lui S, Liao Y, Du MY, Hu N, Thomas JA, Gong QY. White matter deficits in first episode schizophrenia: an activation likelihood estimation meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:100-6. [PMID: 23648972 DOI: 10.1016/j.pnpbp.2013.04.019] [Citation(s) in RCA: 103] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 04/26/2013] [Accepted: 04/26/2013] [Indexed: 02/05/2023]
Abstract
BACKGROUND Diffusion tensor imaging (DTI) has been widely used in psychiatric research and has provided evidence of white matter abnormalities in first episode schizophrenia (FES). The goal of the present meta-analysis was to identify white matter deficits by DTI in FES. METHODS A systematic search was conducted to collect DTI studies with voxel-wised analysis of the fractional anisotropy (FA) in FES. The coordinates of regions with FA changes were meta-analyzed using the activation likelihood estimation (ALE) method which weighs each study on the basis of its sample size. RESULTS A total of 8 primary studies were selected, including 271 FES patients and 297 healthy controls. Among these studies, 52 regions showed reductions in the FA in FES while 2 regions had increased FA. Consistent FA reductions in the white matter of the right deep frontal and left deep temporal lobes were identified in all FES patients relative to healthy controls. Fiber tracking showed that the main tracts involved were the cingulum bundle, the left inferior longitudinal fasciculus, the left inferior fronto-occipital fasciculus and the interhemispheric fibers running through the corpus callosum. CONCLUSIONS The current findings provide evidence confirming the lack of connection in the fronto-limbic circuitry at the early stages of the schizophrenia. Because the coordinates reported in the primary literature were highly variable, future investigations with large samples would be required to support the identified white matter changes in FES.
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Affiliation(s)
- Li Yao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, PR China
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Gaudiano BA, Zimmerman M. Prevalence of attenuated psychotic symptoms and their relationship with DSM-IV diagnoses in a general psychiatric outpatient clinic. J Clin Psychiatry 2013; 74:149-55. [PMID: 23146173 PMCID: PMC4036523 DOI: 10.4088/jcp.12m07788] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2012] [Accepted: 08/02/2012] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Attenuated psychosis syndrome (APS) is being proposed for inclusion in Section III of DSM-5 for those impaired by subthreshold psychotic symptoms that are not better accounted for by another diagnosis and not meeting criteria for a psychotic disorder. The rationale is to identify patients who are at high risk for transition to a psychotic disorder in the near future. However, the potential impact of using this new diagnosis in routine clinical practice settings has not been carefully examined. METHOD As part of the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) project, a treatment-seeking psychiatric outpatient sample (n = 1,257) recruited from June 1997 to June 2002 completed a self-report measure of psychiatric symptoms and afterward were administered structured clinical interviews. For the current post hoc study, we investigated the prevalence rate of endorsing attenuated psychotic experiences to identify patients who could potentially meet criteria for APS. RESULTS After the exclusion of those with lifetime DSM-IV psychotic disorders, psychotic experiences remained highly prevalent in the sample (28% reported at least 1 psychotic experience during the past 2 weeks), and rates were similar across all major DSM-IV diagnostic categories. Only 1 patient (0.08%) reported psychotic experiences but did not meet criteria for another current DSM disorder; however, this individual endorsed other nonpsychotic symptoms of greater severity. Psychotic experience endorsement was positively correlated with nearly all other nonpsychotic symptom domains, and multivariate analysis showed that general clinical severity predicted endorsement of psychotic experiences (P values < .001). CONCLUSIONS We could not identify any patients who clearly met criteria for APS alone in our sample. Psychotic experiences appear to be common in outpatients and represent nonspecific indicators of psychopathology. Diagnosing APS in the community could result in high rates of false-positives or high rates of APS "comorbidity" with other nonpsychotic disorders, leading to the increased use of antipsychotic medications without clear need. Therefore, the clinical utility of adding APS to the diagnostic system remains highly questionable.
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Affiliation(s)
| | - Mark Zimmerman
- Rhode Island Hospital & Alpert Medical School of Brown University
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Wood SJ, Reniers RLEP, Heinze K. Neuroimaging findings in the at-risk mental state: a review of recent literature. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2013; 58:13-8. [PMID: 23327751 DOI: 10.1177/070674371305800104] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The at-risk mental state (ARMS) has been the subject of much interest during the past 15 years. A great deal of effort has been expended to identify neuroimaging markers that can inform our understanding of the risk state and to help predict who will transition to frank psychotic illness. Recently, there has been an explosion of neuroimaging literature from people with an ARMS, which has meant that reviews and meta-analyses lack currency. Here we review papers published in the past 2 years, and contrast their findings with previous reports. While it is clear that people in the ARMS do show brain alterations when compared with healthy control subjects, there is an overall lack of consistency as to which of these alterations predict the development of psychosis. This problem arises because of variations in methodology (in patient recruitment, region of interest, method of analysis, and functional task employed), but there has also been too little effort put into replicating previous research. Nonetheless, there are areas of promise, notably that activation of the stress system and increased striatal dopamine synthesis seem to mark out patients in the ARMS most at risk for later transition. Future studies should focus on these areas, and on network-level analysis, incorporating graph theoretical approaches and intrinsic connectivity networks.
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Affiliation(s)
- Stephen J Wood
- Professor of Adolescent Brain Development and Mental Health, School of Psychology, University of Birmingham, Edgbaston, England.
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Predicting first episode psychosis in those at high risk for genetic or cognitive reasons. Epidemiol Psychiatr Sci 2012; 21:323-8. [PMID: 22967956 PMCID: PMC6998226 DOI: 10.1017/s2045796012000509] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Structural and functional magnetic resonance imaging (MRI) of patients with psychosis has advanced to the point where there are clear abnormalities at a group level between patients and groups of healthy controls, and suggestions of different patterns of abnormalities between groups of patients. A major area of research endeavour is being able to translate these group differences into clinically relevant predictions at an individual patient level. Here, we briefly summarize our main findings in cohorts at high risk of psychosis because they come from families in which several members have schizophrenia or bipolar disorder, or have educational impairments. We highlight consistent predictors of psychosis in those at high risk of schizophrenia for genetic or cognitive reasons, as compared with quite distinct profiles between those at genetic high risk of schizophrenia v. bipolar disorder on functional MRI during an executive language task. We also consider future research directions and ethical issues in the early diagnostic testing of people at high risk of psychosis.
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Koutsouleris N, Davatzikos C, Bottlender R, Patschurek-Kliche K, Scheuerecker J, Decker P, Gaser C, Möller HJ, Meisenzahl EM. Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophr Bull 2012; 38:1200-15. [PMID: 21576280 PMCID: PMC3494049 DOI: 10.1093/schbul/sbr037] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition. METHODS First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation. RESULTS The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments. CONCLUSIONS Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany.
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | - Ronald Bottlender
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
| | - Katja Patschurek-Kliche
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
| | - Johanna Scheuerecker
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
| | - Petra Decker
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
| | - Christian Gaser
- Department of Psychiatry, Friedrich-Schiller-University, Jena, Germany
| | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
| | - Eva M. Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany
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Klöppel S, Abdulkadir A, Jack CR, Koutsouleris N, Mourão-Miranda J, Vemuri P. Diagnostic neuroimaging across diseases. Neuroimage 2012; 61:457-63. [PMID: 22094642 PMCID: PMC3420067 DOI: 10.1016/j.neuroimage.2011.11.002] [Citation(s) in RCA: 198] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2011] [Accepted: 11/02/2011] [Indexed: 12/18/2022] Open
Abstract
Fully automated classification algorithms have been successfully applied to diagnose a wide range of neurological and psychiatric diseases. They are sufficiently robust to handle data from different scanners for many applications and in specific cases outperform radiologists. This article provides an overview of current applications taking structural imaging in Alzheimer's disease and schizophrenia as well as functional imaging to diagnose depression as examples. In this context, we also report studies aiming to predict the future course of the disease and the response to treatment for the individual. This has obvious clinical relevance but is also important for the design of treatment studies that may aim to include a cohort with a predicted fast disease progression to be more sensitive to detect treatment effects. In the second part, we present our own opinions on i) the role these classification methods can play in the clinical setting; ii) where their limitations are at the moment and iii) how those can be overcome. Specifically, we discuss strategies to deal with disease heterogeneity, diagnostic uncertainties, a probabilistic framework for classification and multi-class classification approaches.
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Affiliation(s)
- Stefan Klöppel
- Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry and Neuropsychology, Freiburg Brain Imaging, University Medical Center Freiburg, Hauptstrasse 5, Freiburg, Germany.
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Jung WH, Borgwardt S, Fusar-Poli P, Kwon JS. Gray matter volumetric abnormalities associated with the onset of psychosis. Front Psychiatry 2012; 3:101. [PMID: 23227013 PMCID: PMC3512053 DOI: 10.3389/fpsyt.2012.00101] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2012] [Accepted: 11/06/2012] [Indexed: 01/15/2023] Open
Abstract
Patients with psychosis display structural brain abnormalities in multiple brain regions. The disorder is characterized by a putative prodromal period called ultra-high-risk (UHR) status, which precedes the onset of full-blown psychotic symptoms. Recent studies on psychosis have focused on this period. Neuroimaging studies of UHR individuals for psychosis have revealed that the structural brain changes observed during the established phases of the disorder are already evident prior to the onset of the illness. Moreover, certain brain regions show extremely dynamic changes during the transition to psychosis. These neurobiological features may be used as prognostic and predictive biomarkers for psychosis. With advances in neuroimaging techniques, neuroimaging studies focusing on gray matter abnormalities provide new insights into the pathophysiology of psychosis, as well as new treatment strategies. Some of these novel approaches involve antioxidants administration, because it is suggested that this treatment may delay the progression of UHR to a full-blown psychosis and prevent progressive structural changes. The present review includes an update on the most recent developments in early intervention strategies for psychosis and potential therapeutic treatments for schizophrenia. First, we provide the basic knowledge of the brain regions associated with structural abnormalities in individuals at UHR. Next, we discuss the feasibility on the use of magnetic resonance imaging (MRI)-biomarkers in clinical practice. Then, we describe potential etiopathological mechanisms underlying structural brain abnormalities in prodromal psychosis. Finally, we discuss the potentials and limitations related to neuroimaging studies in individuals at UHR.
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Affiliation(s)
- Wi Hoon Jung
- Interdisciplinary Program in Neuroscience, Seoul National University Seoul, South Korea ; Institute of Human Behavioral Medicine, Seoul National University-MRC Seoul, South Korea
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Fervaha G, Remington G. Interpreting a multivariate analysis of functional neuroimaging data. Front Psychiatry 2012; 3:52. [PMID: 22661958 PMCID: PMC3361683 DOI: 10.3389/fpsyt.2012.00052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2012] [Accepted: 05/10/2012] [Indexed: 11/23/2022] Open
Affiliation(s)
- Gagan Fervaha
- Institute of Medical Science, University of Toronto Toronto, ON, Canada
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42
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Borgwardt S, McGuire P, Fusar-Poli P. Gray matters!--mapping the transition to psychosis. Schizophr Res 2011; 133:63-7. [PMID: 21943556 DOI: 10.1016/j.schres.2011.08.021] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2011] [Revised: 08/28/2011] [Accepted: 08/28/2011] [Indexed: 10/17/2022]
Abstract
Despite many neuroimaging studies on schizophrenia showing brain abnormalities the exact time course of their occurrence is unknown. Studies of gray matter are a powerful tool in biological psychiatry and provide an unprecedented opportunity for brain structure investigations. Here we compared cross-sectional and longitudinal structural neuroimaging studies distinguishing high-risk subjects developing psychosis from those who did not. These investigations on gray matter volumes in the prodromal phase potentially identify core structural markers of impending psychoses and clarify dynamic changes underlying the transition. Subjects at high risk of psychosis show qualitatively similar albeit less severe gray matter abnormalities as patients with psychosis.
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Affiliation(s)
- Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland.
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Agartz I, Brown AA, Rimol LM, Hartberg CB, Dale AM, Melle I, Djurovic S, Andreassen OA. Common sequence variants in the major histocompatibility complex region associate with cerebral ventricular size in schizophrenia. Biol Psychiatry 2011; 70:696-8. [PMID: 21514568 DOI: 10.1016/j.biopsych.2011.02.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2010] [Revised: 02/25/2011] [Accepted: 02/25/2011] [Indexed: 02/02/2023]
Abstract
BACKGROUND Because of evidence from genetic linkage and genome-wide association studies, as well as suggested involvement of infection, the major histocompatibility complex (MHC) region on chromosome 6p21.3-22.1 has been implicated in the development of schizophrenia. METHODS Here, we investigated how gene variants across the MHC region are associated with brain structure in a large ethnically homogenous sample (n = 420), including patients with schizophrenia spectrum disorders and other severe mental illness and healthy control subjects. RESULTS We demonstrate highly significant associations between common gene markers in the MHC region and cerebral ventricular volume specifically in schizophrenia spectrum patients (uncorrected p values between 1.16 × 10⁻⁴ and 2.00 × 10⁻⁷). One single nucleotide polymorphism, rs2596532, survives Bonferroni correction for multiple testing across all single nucleotide polymorphisms and brain structure measures (adjusted p value 5.59 × 10⁻⁴). CONCLUSIONS The results indicate that MHC variants are implicated in characteristic brain abnormalities of schizophrenia.
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Affiliation(s)
- Ingrid Agartz
- Institute of Clinical Medicine, Department of Biostatistics, University of Oslo, Oslo, Norway.
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Egerton A, Borgwardt SJ, Tognin S, Howes OD, McGuire P, Allen P. An overview of functional, structural and neurochemical imaging studies in individuals with a clinical high risk for psychosis. ACTA ACUST UNITED AC 2011. [DOI: 10.2217/npy.11.51] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Waters-Metenier S, Toulopoulou T. Putative structural neuroimaging endophenotypes in schizophrenia: a comprehensive review of the current evidence. FUTURE NEUROLOGY 2011. [DOI: 10.2217/fnl.11.35] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The genetic contribution to schizophrenia etiopathogenesis is underscored by the fact that the best predictor of developing schizophrenia is having an affected first-degree relative, which increases lifetime risk by tenfold, as well as the observation that when both parents are affected, the risk of schizophrenia increases to approximately 50%, compared with 1% in the general population. The search to elucidate the complex genetic architecture of schizophrenia has employed various approaches, including twin and family studies to examine co-aggregation of brain abnormalities, studies on genetic linkage and studies using genome-wide association to identify genetic variations associated with schizophrenia. ‘Endophenotypes’, or ‘intermediate phenotypes’, are potentially narrower constructs of genetic risk. Hypothetically, they are intermediate in the pathway between genetic variation and clinical phenotypes and can supposedly be implemented to assist in the identification of genetic diathesis for schizophrenia and, possibly, in redefining clinical phenomenology.
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Affiliation(s)
- Sheena Waters-Metenier
- Department of Psychosis Studies, King’s College London, King’s Health Partners, Institute of Psychiatry, London, UK
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Koutsouleris N, Gaser C, Patschurek-Kliche K, Scheuerecker J, Bottlender R, Decker P, Schmitt G, Reiser M, Möller HJ, Meisenzahl EM. Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis. Hum Brain Mapp 2011; 33:2104-24. [PMID: 22887825 DOI: 10.1002/hbm.21342] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2010] [Revised: 03/20/2011] [Accepted: 04/12/2011] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Neuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques. METHODS Voxel-based morphometry and partial least squares were employed to study system-level covariance patterns between whole-brain morphological data and processing speed, working memory, verbal learning/IQ, and executive functions in 40 ARMS subjects and 30 healthy controls (HC). The detected structure-cognition covariance patterns were tested for significance and reliability using non-parametric permutation and bootstrap resampling. RESULTS We identified ARMS-specific covariance patterns that described a generalized association of neurocognitive measures with predominantly prefronto-temporo-limbic and subcortical structures as well as the interconnecting white matter. In the conversion group, this generalized profile particularly involved working memory and verbal IQ and was positively correlated with limbic, insular and subcortical volumes as well as negatively related to prefrontal, temporal, parietal, and occipital cortices. Conversely, the neurocognitive profiles in the HC group were confined to working memory, learning and IQ, which were diffusely associated with cortical and subcortical brain regions. CONCLUSIONS These findings suggest that the ARMS and prodromal phase of psychosis are characterized by a convergent mapping from multi-domain neurocognitive measures to a set of prefronto-temporo-limbic and subcortical structures. Furthermore, a neuroanatomical separation between positive and negative brain-cognition correlations may not only point to a biological process determining the clinical risk for disease transition, but also to possible compensatory or dysmaturational neural processes.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
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Cohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA. Decoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci 2011; 5:75. [PMID: 21720520 PMCID: PMC3118657 DOI: 10.3389/fnins.2011.00075] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Accepted: 05/16/2011] [Indexed: 11/13/2022] Open
Abstract
The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
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Affiliation(s)
- Jessica R Cohen
- Helen Wills Neuroscience Institute, University of California Berkeley Berkeley, CA, USA
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Barrós-Loscertales A, Garavan H, Bustamante JC, Ventura-Campos N, Llopis JJ, Belloch V, Parcet MA, Ávila C. Reduced striatal volume in cocaine-dependent patients. Neuroimage 2011; 56:1021-6. [DOI: 10.1016/j.neuroimage.2011.02.035] [Citation(s) in RCA: 86] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Revised: 02/07/2011] [Accepted: 02/11/2011] [Indexed: 10/18/2022] Open
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Sato JR, de Oliveira-Souza R, Thomaz CE, Basílio R, Bramati IE, Amaro E, Tovar-Moll F, Hare RD, Moll J. Identification of psychopathic individuals using pattern classification of MRI images. Soc Neurosci 2011; 6:627-39. [PMID: 21590586 DOI: 10.1080/17470919.2011.562687] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Psychopathy is a disorder of personality characterized by severe impairments of social conduct, emotional experience, and interpersonal behavior. Psychopaths consistently violate social norms and bring considerable financial, emotional, or physical harm to others and to society as a whole. Recent developments in analysis methods of magnetic resonance imaging (MRI), such as voxel-based-morphometry (VBM), have become major tools to understand the anatomical correlates of this disorder. Nevertheless, the identification of psychopathy by neuroimaging or other neurobiological tools (e.g., genetic testing) remains elusive. METHODS/PRINCIPAL FINDINGS The main aim of this study was to develop an approach to distinguish psychopaths from healthy controls, based on the integration between pattern recognition methods and gray matter quantification. We employed support vector machines (SVM) and maximum uncertainty linear discrimination analysis (MLDA), with a feature-selection algorithm. Imaging data from 15 healthy controls and 15 psychopathic individuals (7 women in each group) were analyzed with SPM2 and the optimized VBM preprocessing routines. Participants were scanned with a 1.5 Tesla MRI system. Both SVM and MLDA achieved an overall leave-one-out accuracy of 80%, but SVM mapping was sparser than using MLDA. The superior temporal sulcus/gyrus (bilaterally) was identified as a region containing the most relevant information to separate the two groups. CONCLUSION/SIGNIFICANCE These results indicate that gray matter quantitative measures contain robust information to predict high psychopathy scores in individual subjects. The methods employed herein might prove useful as an adjunct to the established clinical and neuropsychological measures in patient screening and diagnostic accuracy.
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Affiliation(s)
- João R Sato
- Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Santo André, Brazil.
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Bora E, Fornito A, Radua J, Walterfang M, Seal M, Wood SJ, Yücel M, Velakoulis D, Pantelis C. Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophr Res 2011; 127:46-57. [PMID: 21300524 DOI: 10.1016/j.schres.2010.12.020] [Citation(s) in RCA: 343] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2010] [Revised: 12/20/2010] [Accepted: 12/27/2010] [Indexed: 01/11/2023]
Abstract
Despite an increasing number of published voxel based morphometry studies of schizophrenia, there has been no adequate attempt to examine gray (GM) and white matter (WM) abnormalities and the heterogeneity of published findings. In the current article, we used a coordinate based meta-analysis technique to simultaneously examine GM and WM abnormalities in schizophrenia and to assess the effects of gender, chronicity, negative symptoms and other clinical variables. 79 studies meeting our inclusion criteria were included in the meta-analysis. Schizophrenia was associated with GM reductions in the bilateral insula/inferior frontal cortex, superior temporal gyrus, anterior cingulate gyrus/medial frontal cortex, thalamus and left amygdala. In WM analyses of volumetric and diffusion-weighted images, schizophrenia was associated with decreased FA and/or WM in interhemispheric fibers, anterior thalamic radiation, inferior longitudinal fasciculi, inferior frontal occipital fasciculi, cingulum and fornix. Male gender, chronic illness and negative symptoms were associated with more severe GM abnormalities and illness chronicity was associated with more severe WM deficits. The meta-analyses revealed overlapping GM and WM structural findings in schizophrenia, characterized by bilateral anterior cortical, limbic and subcortical GM abnormalities, and WM changes in regions including tracts that connect these structures within and between hemispheres. However, the available findings are biased towards characteristics of schizophrenia samples with poor prognosis.
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Affiliation(s)
- Emre Bora
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Level 3, National Neuroscience Facility, Alan Gilbert Building, 161, Barry St, Carlton South, VIC, 3053, Australia.
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