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Related Concept Videos

Working Memory01:24

Working Memory

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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...
464

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Related Experiment Video

Updated: Oct 2, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment
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Personalized Prediction of Postconcussive Working Memory Decline: A Feasibility Study.

Yung-Chieh Chen1,2, Yung-Li Chen3, Duen-Pang Kuo1,2

  • 1Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan.

Journal of Personalized Medicine
|February 25, 2022
PubMed
Summary

Machine learning models using functional MRI (fMRI) and demographic data can predict long-term working memory (WM) deficits after concussion. This enables early intervention for patients with potential poor cognitive outcomes.

Keywords:
concussionlong-term cognitive outcomemild traumatic brain injurypersonalized predictionsupport vector machine classifierworking memory

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Concussion, or mild traumatic brain injury (mTBI), can lead to persistent working memory (WM) deficits, impacting return to work.
  • Predicting long-term cognitive outcomes after mTBI is challenging due to often normal structural imaging.
  • Functional magnetic resonance imaging (fMRI) shows sensitivity to post-injury WM deficits and can guide treatment.

Purpose of the Study:

  • To assess if machine learning models, using baseline fMRI biomarkers and demographic/neuropsychological data, can predict 1-year cognitive outcomes in concussion patients.
  • To identify predictors of sustained cognitive impairment following mild traumatic brain injury.

Main Methods:

  • Prospective observational study comparing mTBI patients and healthy controls.
  • Baseline assessments within one week of injury, with follow-ups up to 1 year.
  • Support vector machine classifier trained on fMRI, demographic, and neuropsychological features correlated with WM changes.

Main Results:

  • Patients showed initial cognitive recovery followed by decline, with ~50% experiencing prolonged impairment at 1 year.
  • Accurate predictions (83.3-87.5%) were achieved for patients with non-recovered WM function at 3 months, 6 months, and 1 year.
  • Models successfully predicted outcomes for patients with worse 1-year performance compared to baseline.

Conclusions:

  • Machine learning models using baseline fMRI and demographic data can effectively predict long-term working memory outcomes post-concussion.
  • This approach facilitates personalized prediction and early rehabilitation interventions for individuals at risk of poor cognitive recovery.
  • Highlights the potential of fMRI in understanding and managing the long-term cognitive effects of mTBI.