Abstract
Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox (neuropm-lab.com/neuropm-box.html) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.
Highlights
Background populationThe contrastive Trajectories Inference (cTI) algorithm detects enriched patterns in the population of interest while adjusting by confounding components in the background population
NeuroPM-box (Fig. 1) enables both the separate and combined analysis of large-scale molecular and macroscopic data, including molecular screening, histopathology, molecular imaging (amyloid, tau-positron emission tomography (PET)), magnetic resonance imaging (MRI), and cognitive/clinical evaluations. It focuses on clarifying crucial mechanistic questions on how the brain functions; such as (i) Which series of sequential molecular or macroscopic states underlie decades of neuropathological evolution16,17? (ii) Which genes drive dysfunction in other genes and pathways9,16,18? (iii) How do disease agents spread through communicating cells in the brain12,13,19? (iv) Which multifactorial, synergistic interactions occur in diseased brain regions14,20? (iv) How would each patient potentially respond to different therapeutic interventions15?
The contrasted Trajectories Inference algorithm[16] (Fig. 2; cTI definition in “Online Methods”) uses recent advances in artificial intelligence (AI) to explore and visualize highdimensional data[21] to elucidate the distinctive/contrasted underlying paths, using large-scale biological observations
Summary
Background populationThe cTI algorithm detects enriched patterns in the population of interest while adjusting by confounding components in the background population (i.e. subjects free of the main effect of interest). To define the background population, the user should provide the list of corresponding IDs, which can be entered by just copying the IDs in the interface, or by loading a “.txt” file in which each row have an ID. To define the optional target population, the user should provide the list of corresponding IDs (following same format that for background population), which can be entered by just copying the IDs in the interface, or by loading a “.txt” file in which each row have an ID. Of note, this option is not valid when using the “cKernel PCA” method
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