Published on Thu Apr 26 2018

Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry

Alexander Bernstein, Evgeny Burnaev, Ekaterina Kondratyeva, Svetlana Sushchinskaya, Maxim Sharaev, Alexander Andreev, Alexey Artemov, Renat Akzhigitov

We consider a problem of diagnostic pattern recognition/classification from neuroimaging data. We propose a common data analysis pipeline for diagnostic classification problems. We illustrate the application by discovering new biomarkers for diagnostics of epilepsy and depression based on clinical and MRI/fMRI data.

0
0
0
Abstract

We consider a problem of diagnostic pattern recognition/classification from neuroimaging data. We propose a common data analysis pipeline for neuroimaging-based diagnostic classification problems using various ML algorithms and processing toolboxes for brain imaging. We illustrate the pipeline application by discovering new biomarkers for diagnostics of epilepsy and depression based on clinical and MRI/fMRI data for patients and healthy volunteers.

Thu Apr 26 2018
Computer Vision
fMRI: preprocessing, classification and pattern recognition
Machine learning continues to gain momentum in the neuroscience community. Systematic research into mental disorders increasingly involves drawing clinical conclusions on the basis of data-driven approaches. Identification of key neuroimaging markers requires establishing a comprehensive pre-processing pipeline.
0
0
0
Wed Jan 27 2021
Machine Learning
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
We propose a novel deep neural network architecture to integrate imaging and genetic data. We use a learnable dropout layer to extract interpretable biomarkers from the data. Our unique training strategy can easily accommodate missing data modalities across subjects.
0
0
0
Sun May 10 2020
Machine Learning
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages. It has achieved significant performance improvements over traditional machine learning. This paper reviews the applications of deep learning methods for brain disorder analysis.
0
0
0
Fri Dec 13 2019
Computer Vision
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
We observe a disconcerting phenomenon in machine learning studies in psychiatry. While we would expect larger samples to yield better results, larger studies consistently show much weaker performance than the numerous small-scale studies. We first trained and tested a classification model on the full dataset which yielded an accuracy of .
2
3
6
Tue Apr 16 2019
Machine Learning
ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data
Mental disorders such as Autism Spectrum Disorders (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behaviouralobservation of symptomology (DSM-5/ICD-10) We need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental disorders.
0
0
0
Sun Jun 17 2018
Machine Learning
Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging
High-dimensional data classification problems are routinely encountered in neuroimaging studies. We review several important feature learning and selection techniques. We compare these approaches using a numerical study involving Alzheimer's disease.
0
0
0