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  3. Beyond Glm: Inter-subject Variability As A Complementary Approach To Detect Longitudinal Changes In Emotion Processing In Multiple Sclerosis.
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  3. Beyond Glm: Inter-subject Variability As A Complementary Approach To Detect Longitudinal Changes In Emotion Processing In Multiple Sclerosis.

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Beyond GLM: Inter-Subject Variability as a Complementary Approach to Detect Longitudinal Changes in Emotion

Alice Pirastru1, Valeria Blasi1, Diego Michael Cacciatore1,2

  • 1IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148 Milan, Italy.

Journal of Imaging
|May 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Capturing treatment effects in neurological patients is hard. Modeling inter-subject variability, not just group averages, better detects neural changes from emotion rehabilitation in multiple sclerosis patients.

Keywords:
EMDRdepressionemotion stimulation task-fMRIinter-subject variabilitymultiple sclerosisrehabilitation

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Clinical Psychology

Background:

  • Reliably capturing treatment-induced neural changes in neurological conditions is challenging.
  • Standard group-level analyses (GLM) in longitudinal neuroimaging studies may obscure individualized neuroplasticity by treating inter-subject variability (ISV) as noise.
  • Emotion processing deficits are common in multiple sclerosis (MS) and are a target for rehabilitation.

Purpose of the Study:

  • To investigate if modeling inter-subject variability (ISV) can better detect treatment-related neural changes compared to conventional methods.
  • To assess the utility of variability-based approaches in capturing neuroplasticity in individuals with neurological conditions undergoing rehabilitation.
  • To use emotion-focused rehabilitation as a model to test these neuroimaging analysis techniques.

Main Methods:

  • Comparison of General Linear Model (GLM) with threshold-weighted overlap maps (OMth-w) for analyzing neuroimaging data.
  • fMRI task involving emotion processing administered to healthy controls (HCs) and people with MS (pwMS) before and after treatment.
  • Analysis of spatial consistency across individuals using OMth-w to quantify ISV.

Main Results:

  • The standard GLM analysis revealed no significant longitudinal effects in people with MS (pwMS) after treatment.
  • The variability-based OMth-w approach detected reduced neural variability in pwMS post-treatment.
  • Reduced neural variability correlated with decreased depressive symptoms in pwMS (p < 0.001).

Conclusions:

  • Modeling inter-subject variability (ISV) offers a valuable complementary approach to traditional GLM analyses for detecting treatment-related neuroplasticity.
  • Variability-based methods can reveal subtle, individualized neural changes missed by group-level analyses in neurological populations.
  • This approach enhances the detection of treatment efficacy in conditions like multiple sclerosis, particularly in domains like emotion processing.