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Bipolar Disorder

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The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Neuroscience and biobehavioral reviews
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder....

BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.

Psychiatry research. Neuroimaging
BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can be used to analyse deviation from physiological developmenta...

An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.

BMC genomics
BACKGROUND: Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the...

Targeted use of growth mixture modeling: a learning perspective.

Statistics in medicine
From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we uti...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

NeuroImage
Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to diff...

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

NeuroImage
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 ...

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

PloS one
Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy r...

Advanced literature analysis in a Big Data world.

Annals of the New York Academy of Sciences
Comprehensive data mining of the scientific literature has become an increasing challenge. To address this challenge, Elsevier's Pathway Studio software uses the techniques of natural language processing to systematically extract specific biological ...

Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines.

Psychiatry research
Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from ...

Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study.

Journal of psychiatry & neuroscience : JPN
BACKGROUND: Brain imaging is of limited diagnostic use in psychiatry owing to clinical heterogeneity and low sensitivity/specificity of between-group neuroimaging differences. Machine learning (ML) may better translate neuroimaging to the level of in...