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Related Concept Videos

Working Memory01:24

Working Memory

497
Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
497

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Related Experiment Video

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Employing Connectome-Based Models to Predict Working Memory in Multiple Sclerosis.

Heena R Manglani1,2, Stephanie Fountain-Zaragoza1,2, Anita Shankar1,2

  • 1Department of Psychology, The Ohio State University, Columbus, Ohio, USA.

Brain Connectivity
|July 26, 2021
PubMed
Summary
This summary is machine-generated.

Working memory (WM) deficits in multiple sclerosis (MS) are better predicted by whole-brain functional connectivity models derived from healthy individuals than by MS-specific models. This highlights the translational potential of healthy brain networks for understanding WM in MS.

Keywords:
cognitionfunctional connectivitymultiple sclerosispredictive modelingworking memory

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Medical Imaging

Background:

  • Individuals with multiple sclerosis (MS) frequently experience working memory (WM) deficits.
  • Previous research on neural correlates of WM in MS has yielded inconclusive results, possibly due to localized analyses.
  • Widespread neural alterations in MS suggest that whole-brain connectivity approaches may better predict individual WM performance.

Purpose of the Study:

  • To apply connectome-based predictive modeling to functional magnetic resonance imaging (fMRI) data from WM tasks in individuals with MS.
  • To test the hypothesis that an MS-specific predictive model can predict WM performance.
  • To assess the generalizability of WM networks derived from healthy adults to individuals with MS.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) data from WM tasks were analyzed using connectome-based predictive modeling.
  • An MS-specific model was trained on functional connectivity data from an internal sample (n=36) to predict Paced Visual Serial Addition Test accuracy.
  • The model's predictive performance was validated on an independent cohort (n=36) using the N-back task, and compared against WM networks from healthy adults.

Main Results:

  • An MS-specific predictive model showed significant prediction in the internal sample (r_s=0.47, p=0.011) but failed to generalize to the validation cohort (r_s=-0.047, p=0.78).
  • In contrast, WM networks derived from healthy adults successfully predicted WM performance in both MS samples (internal: r_s=0.33, p=0.049; validation: r_s=0.46, p=0.005).
  • These findings demonstrate the translational potential of healthy brain networks for predicting WM in MS, suggesting disease-related heterogeneity may limit MS-specific models.

Conclusions:

  • Functional brain networks identified in healthy individuals effectively predict working memory performance in multiple sclerosis.
  • Predictive models derived from small patient samples may lack generalizability due to disease heterogeneity.
  • Further research is warranted to explore the robustness of models derived from large clinical samples for understanding neurological conditions.