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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Unsupervised machine learning identifies clinically relevant patterns of CSF dynamic dysfunction in normal pressure

Emanuele Camerucci¹, Petrice M Cogswell², Jeffrey L Gunter²

¹Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Kansas University Medical Center (KUMC), Kansas City, KS, USA.

Clinical Neurology and Neurosurgery

|September 19, 2025

Summary

This summary is machine-generated.

Non-negative Matrix Factorization (NMF) accurately predicts idiopathic normal pressure hydrocephalus (iNPH) by analyzing cerebrospinal fluid (CSF) distribution patterns. This data-driven approach aids in diagnosing iNPH but does not predict treatment response.

Keywords:

DESH INPH Non-negative matrix factorization

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Area of Science:

Neurology
Radiology
Medical Imaging Analysis

Background:

Idiopathic normal pressure hydrocephalus (iNPH) presents with non-specific symptoms, complicating diagnosis.
Accurate diagnosis of iNPH is crucial for effective treatment and patient management.

Purpose of the Study:

To investigate if data-driven patterns of cerebrospinal fluid (CSF) distribution can predict iNPH diagnosis.
To assess the utility of these patterns in predicting treatment response in iNPH patients.

Main Methods:

Non-negative Matrix Factorization (NMF) applied to CSF probability maps from MRI scans.
Analysis of CSF distribution patterns and component loads to predict diagnosis and clinical outcomes.
Comparison with manual measurements like Evan's index and callosal angle.

Main Results:

NMF identified 7 CSF distribution patterns in 98 iNPH patients and 98 controls.
The automated NMF model achieved high accuracy (96%/97%) in predicting iNPH diagnosis.
Patterns associated with disproportionately enlarged subarachnoid space hydrocephalus (DESH) showed the highest diagnostic potential.

Conclusions:

NMF-based CSF distribution patterns accurately differentiate iNPH patients from controls.
The NMF algorithm, particularly when focusing on DESH features, shows promise as a diagnostic aid.
The proposed tool complements, rather than replaces, clinical expertise for iNPH diagnosis.