Dataset Information

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis.

ABSTRACT:

Importance

The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.

Objective

To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).

Design, setting, and participants

In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020.

Main outcomes and measures

Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group).

Results

Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (ρ = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (ρ = 0.43; 95% CI, 0.20 to 0.61; P = .001).

Conclusions and relevance

This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.

SUBMITTER: ENIGMA Clinical High Risk for Psychosis Working Group

PROVIDER: S-EPMC8100913 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis.

Jalbrzikowski Maria M Hayes Rebecca A RA Wood Stephen J SJ Nordholm Dorte D Zhou Juan H JH Fusar-Poli Paolo P Uhlhaas Peter J PJ Takahashi Tsutomu T Sugranyes Gisela G Kwak Yoo Bin YB Mathalon Daniel H DH Katagiri Naoyuki N Hooker Christine I CI Smigielski Lukasz L Colibazzi Tiziano T Via Esther E Tang Jinsong J Koike Shinsuke S Rasser Paul E PE Michel Chantal C Lebedeva Irina I Hegelstad Wenche Ten Velden WTV de la Fuente-Sandoval Camilo C Waltz James A JA Mizrahi Romina R Corcoran Cheryl M CM Resch Franz F Tamnes Christian K CK Haas Shalaila S SS Lemmers-Jansen Imke L J ILJ Agartz Ingrid I Allen Paul P Amminger G Paul GP Andreassen Ole A OA Atkinson Kimberley K Bachman Peter P Baeza Inmaculada I Baldwin Helen H Bartholomeusz Cali F CF Borgwardt Stefan S Catalano Sabrina S Chee Michael W L MWL Chen Xiaogang X Cho Kang Ik K KIK Cooper Rebecca E RE Cropley Vanessa L VL Dolz Montserrat M Ebdrup Bjørn H BH Fortea Adriana A Glenthøj Louise Birkedal LB Glenthøj Birte Y BY de Haan Lieuwe L Hamilton Holly K HK Harris Mathew A MA Haut Kristen M KM He Ying Y Heekeren Karsten K Heinz Andreas A Hubl Daniela D Hwang Wu Jeong WJ Kaess Michael M Kasai Kiyoto K Kim Minah M Kindler Jochen J Klaunig Mallory J MJ Koppel Alex A Kristensen Tina D TD Kwon Jun Soo JS Lawrie Stephen M SM Lee Jimmy J León-Ortiz Pablo P Lin Ashleigh A Loewy Rachel L RL Ma Xiaoqian X McGorry Patrick P McGuire Philip P Mizuno Masafumi M Møller Paul P Moncada-Habib Tomas T Muñoz-Samons Daniel D Nelson Barnaby B Nemoto Takahiro T Nordentoft Merete M Omelchenko Maria A MA Oppedal Ketil K Ouyang Lijun L Pantelis Christos C Pariente Jose C JC Raghava Jayachandra M JM Reyes-Madrigal Francisco F Roach Brian J BJ Røssberg Jan I JI Rössler Wulf W Salisbury Dean F DF Sasabayashi Daiki D Schall Ulrich U Schiffman Jason J Schlagenhauf Florian F Schmidt Andre A Sørensen Mikkel E ME Suzuki Michio M Theodoridou Anastasia A Tomyshev Alexander S AS Tor Jordina J Værnes Tor G TG Velakoulis Dennis D Venegoni Gloria D GD Vinogradov Sophia S Wenneberg Christina C Westlye Lars T LT Yamasue Hidenori H Yuan Liu L Yung Alison R AR van Amelsvoort Thérèse A M J TAMJ Turner Jessica A JA van Erp Theo G M TGM Thompson Paul M PM Hernaus Dennis D

JAMA psychiatry 20210701 7

<h4>Importance</h4>The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk.<h4>Objective</h4>To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-).<h4>Design, setting, and participan ...[more]

PMID: 33950164

Similar Datasets

Project description:ObjectiveFriedreich ataxia (FRDA) is an inherited neurological disease defined by progressive movement incoordination. We undertook a comprehensive characterization of the spatial profile and progressive evolution of structural brain abnormalities in people with FRDA.MethodsA coordinated international analysis of regional brain volume using magnetic resonance imaging data charted the whole-brain profile, interindividual variability, and temporal staging of structural brain differences in 248 individuals with FRDA and 262 healthy controls.ResultsThe brainstem, dentate nucleus region, and superior and inferior cerebellar peduncles showed the greatest reductions in volume relative to controls (Cohen d = 1.5-2.6). Cerebellar gray matter alterations were most pronounced in lobules I-VI (d = 0.8), whereas cerebral differences occurred most prominently in precentral gyri (d = 0.6) and corticospinal tracts (d = 1.4). Earlier onset age predicted less volume in the motor cerebellum (rmax = 0.35) and peduncles (rmax = 0.36). Disease duration and severity correlated with volume deficits in the dentate nucleus region, brainstem, and superior/inferior cerebellar peduncles (rmax = -0.49); subgrouping showed these to be robust and early features of FRDA, and strong candidates for further biomarker validation. Cerebral white matter abnormalities, particularly in corticospinal pathways, emerge as intermediate disease features. Cerebellar and cerebral gray matter loss, principally targeting motor and sensory systems, preferentially manifests later in the disease course.InterpretationFRDA is defined by an evolving spatial profile of neuroanatomical changes beyond primary pathology in the cerebellum and spinal cord, in line with its progressive clinical course. The design, interpretation, and generalization of research studies and clinical trials must consider neuroanatomical staging and associated interindividual variability in brain measures. ANN NEUROL 2021;90:570-583.

Project description:Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Project description:There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Dataset Information

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance

Publications

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets