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Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers.

ABSTRACT: Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort.In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects' clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to 'train' a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls.The RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p<0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p<0.005) with gray matter density. Multiple brain regions - largely covering the fronto - limbic system were identified as 'most relevant' in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) - belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage).These results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden - which points to neuroprogression.

SUBMITTER: Mwangi B

PROVIDER: S-EPMC4817111 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers.

Mwangi Benson B Wu Mon-Ju MJ Cao Bo B Passos Ives C IC Lavagnino Luca L Keser Zafer Z Zunta-Soares Giovana B GB Hasan Khader M KM Kapczinski Flavio F Soares Jair C JC

Biological psychiatry. Cognitive neuroscience and neuroimaging 20160301 2

<h4>Background</h4>Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort.<h4>Methods</h4>In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy control ...[more]

PMID: 27047994

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Project description:Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.

Dataset Information

Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers.

Publications

Individualized Prediction and Clinical Staging of Bipolar Disorders using Neuroanatomical Biomarkers.

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