Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes

Ivleva, Elena I.; Clementz, Brett A.; Dutcher, Anthony M.; Arnold, Sara J.M.; Jeon–Slaughter, Haekyung; Aslan, Sina; Witte, Bradley; Poudyal, Gaurav; Lu, Hanzhang; Meda, Shashwath A.; Pearlson, Godfrey D.; Sweeney, John A.; Keshavan, Matcheri S.; Tamminga, Carol A.

doi:10.1016/j.biopsych.2016.08.030

Cited by 123 publications

(83 citation statements)

References 74 publications

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“…However, our GWAS on these structures' separate volumes did not have significant associations, which implies that the volumetric association may reflect joint regulation of part of the morphology of these structures. We noted that hippocampal volumes in our sample were significantly smaller in cases than in controls (ANOVA P=3E-06) [32][33][34], and there were also significant reductions in cases for the amygdala volumes (P=0.001), but not for the caudate. Gaser et al reported ventricular enlargement in schizophrenia associated with volumetric reduction of the thalamus and superior temporal cortex [30]; in our data, however, we did not observe significant correlation between the volumes of these structures and the volume of the temporal horn of lateral ventricle, or any detectable associations between these structures volumes and the CSF volume-associated genes.…”

Section: Significant Genetic Associations With Brain Csf Volumetric Vmentioning

confidence: 97%

Common variants ofNRXN1, LRP1BandRORAare associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Alliey‐Rodriguez

Grey

Shafee

et al. 2017

Preprint

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ABSTRACT:Schizophrenia, Schizoaffective, and Bipolar Disorders share common illness traits, intermediate phenotypes and a partially overlapping polygenic basis. We performed GWAS on deep phenotyping data, including structural MRI and DTI, clinical, and behavioral scales from 1,115 cases and controls. Significant associations were observed with two cerebrospinal fluid volumes: the temporal horn of left lateral ventricle was associated with NRXN1, and the volume of the cavum septum pellucidum was associated with LRP1B and RORA. Both volumes were associated with illness. Suggestive associations were observed with local gyrification indices, fractional anisotropy and age at onset. The deep phenotyping approach allowed unexpected genetic sharing to be found between phenotypes, including temporal horn of left lateral ventricle and age at onset.

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Section: Significant Genetic Associations With Brain Csf Volumetric Vmentioning

confidence: 97%

Common variants ofNRXN1, LRP1BandRORAare associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Alliey‐Rodriguez

Grey

Shafee

et al. 2017

Preprint

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“…2 Duration of illness is calculated based on the date of the appearance of first positive symptoms of schizophrenia until the date of scan. 3 Antipsychotic treatment data is not available for all the patients included (82 patients had medication information). Cumulative doses were calculated per time of scan and given in chlorpromazine equivalents using standard conversion factors and estimated by the daily doses of each of the antipsychotics used by the patient.…”

Section: Disclosuresmentioning

confidence: 99%

Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance

Janssen

Díaz‐Caneja

Alloza

et al. 2020

Preprint

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Schizophrenia is a biologically complex disorder with multiple regional deficits in cortical brain morphology. In addition, interindividual heterogeneity of cortical morphological metrics is larger in patients with schizophrenia when compared to healthy controls. Exploiting interindividual differences in severity of cortical morphological deficits in patients instead of focusing on group averages may aid in detecting biologically informed homogeneous subgroups. The Person-Based Similarity Index (PBSI) of brain morphology indexes an individual's morphometric similarity across numerous cortical regions amongst a sample of healthy subjects. We extended the PBSI such that it indexes morphometric similarity of an independent individual (e.g., a patient) with respect to healthy control subjects. By employing a normative modeling approach on longitudinal data, we determined an individual's degree of morphometric dissimilarity to the norm. We calculated the PBSI for sulcal width (PBSI-SW) in patients with schizophrenia and healthy control subjects (164 patients, 164 healthy controls; 656 MRI scans) and associated it with cognitive performance and cortical sulcation index. A subgroup of patients with markedly deviant PBSI-SW showed extreme deficits in cognitive performance and cortical sulcation. Progressive reduction of PBSI-SW in the schizophrenia group relative to healthy controls was driven by these deviating individuals. By explicitly leveraging interindividual differences in severity of PBSI-SW deficits, neuroimaging-driven subgrouping of patients is feasible. As such, our results pave the way for future applications of morphometric similarity indices for subtyping of clinical populations.

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“…The B-SNIP-1 dataset used in this study included 912 subjects after quality control assessment. Structural MRI three-dimensional acquisitions were carried out on 3T scanners (GE Signa, Philips Achieva, Siemens Allegra, and Siemens Trio) [21] [22]. High resolution isotropic T1-weighted MP-RAGE sequences were acquired following the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol [21,23].…”

Section: B-snip-1 Datasetmentioning

confidence: 99%

Addressing Inaccurate Nosology in Mental Health: A Multi Label Data Cleansing Approach for Detecting Label Noise from Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders

Rokham

Pearlson

Abrol

et al. 2020

Preprint

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Background: Mental health diagnostic approaches are seeking to identify biological markers to work alongside advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. Methods: We worked with T1 structural magnetic resonance imaging data collected from individuals with mood and psychosis disorders from over 1400 individuals comprising healthy controls, psychosis patients and their unaffected first-degree relatives including 176 bipolar probands, 134 schizoaffective probands, 240 schizophrenia proband, 581 patients relatives and 362 controls. We assumed there might be noise in the diagnostic labeling process. We detected label noise by classifying the data multiple times using a support vector machine classifier, and then we flagged those individuals in which all classifiers unanimously mislabeled those subjects. Next, we assigned a new diagnostic label to these individuals, based on the biological data (MRI), using iterative data cleansing approach. Results: Simulation results showed our method was highly accurate in identifying label noise. Both diagnostic and Biotype categories showed about 65% and 63% respectively of noisy labels with the largest amount of relabeling occurring between the healthy control and bipolar and schizophrenia disorder individuals as well as in the unaffected close relatives. The extraction of imaging features highlighted regional brain changes associated with each group. Conclusions: This approach represents an initial step towards developing strategies that need not assume existing mental health diagnostic categories are always valid, but rather allows us to leverage this information while also acknowledging that there are misassignments.

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Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes

Abstract: GMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.

Cited by 123 publications

References 74 publications

Common variants ofNRXN1, LRP1BandRORAare associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Common variants ofNRXN1, LRP1BandRORAare associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance

Addressing Inaccurate Nosology in Mental Health: A Multi Label Data Cleansing Approach for Detecting Label Noise from Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders

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