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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
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Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

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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.

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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.

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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.