mlni 0.1.5.1

Machine Learning in NeuroImaging for various tasks, e.g., regression, classification and clustering.

MLNI

Machine Learning in NeuroImaging

Documentation

MLNI

MLNI is a python package that performs various tasks using neuroimaging data: i) binary classification for disease diagnosis, following good practice proposed in AD-ML; ii) regression prediction, such as age prediction; and iii) semi-supervised clustering with HYDRA.

Citing this work

:warning: Please let me know if you use this package for your publication; I will update your papers in the section of Publication using MLNI...

:warning: Please cite the software using the Cite this repository button on the right sidebar menu, as well as the original papers below for different tasks...

If you use this software for classification:

Wen, J., 2020. Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease. Neuroinformatics, pp.1-22. doi:10.1007/s12021-020-09469-5 - Paper in PDF

If you use this software for regression:

Wen, J., 2024. The Genetic Architecture of Multimodal Human Brain Age. Nature Communications, pp.1-22. 10.1038/s41467-024-46796-6 - Paper in PDF

If you use this software for clustering:

Varol, E., 2017. HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage, 145, pp.346-364. doi:10.1016/j.neuroimage.2016.02.041 - Paper in PDF

Publication using MLNI

Wen, J., 2021. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. MedIA. - Link

Wen, J., 2022. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry - Link

Lalousis, P.A., 2022. Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biological Psychiatry. - Link