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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
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Language biomarker screening using AI: a transdiagnostic approach to the brain.

Charalambos Themistocleous1, Brielle C Stark2

  • 1Department of Special Needs Education, Faculty of Education, University of Oslo, 0371, Oslo, Norway. charalth@uio.no.

Scientific Reports
|January 4, 2026
PubMed
Summary

This study developed NeuroScreen, a machine learning model using speech analysis to accurately differentiate neurological conditions like dementia and brain injury. It identifies distinct language biomarkers for better diagnosis and care.

Keywords:
Artificial IntelligenceDementiaLeft-hemisphere damageMild cognitive impairmentRight-hemisphere damageTraumatic brain injury

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Area of Science:

  • Neurolinguistics
  • Computational Linguistics
  • Artificial Intelligence in Medicine

Background:

  • Neurological conditions like dementia, mild cognitive impairment (MCI), left-hemisphere damage (LHD), right-hemisphere damage (RHD), and traumatic brain injury (TBI) present overlapping communication and social interaction deficits.
  • Objective and scalable methods for automatically differentiating these conditions using language are lacking.
  • Speech analysis offers a non-invasive approach to assess neurological health.

Purpose of the Study:

  • To develop comprehensive neurolinguistic measures for differentiating neurological conditions.
  • To create a machine learning multiclass screening and language assessment model (NeuroScreen).
  • To establish a large comparative database of linguistic biomarkers for future research.

Main Methods:

  • Utilized a large database of 291 linguistic biomarkers from speech samples of 1,394 participants (including patients with LHD, RHD, dementia, MCI, TBI, and healthy controls).
  • Employed natural language processing (NLP) via the Open Brain AI platform to extract linguistic features (readability, lexical richness, phonology, morphology, syntax, semantics).
  • Developed a Deep Neural Network (DNN) for classification and used linear mixed-effects models to identify biomarkers.

Main Results:

  • The DNN model achieved high accuracy (up to 91%) in classifying neurological conditions based on linguistic features.
  • Distinct neurolinguistic properties were identified: LHD and TBI showed syntax/phonology deficits; MCI exhibited simplification; dementia presented lexico-semantic impairments; RHD had the most preserved profile.
  • Quantitative neurolinguistic markers were revealed for each condition.

Conclusions:

  • The study provides an automatic detection and classification model (NeuroScreen) for key neurological conditions affecting language.
  • Novel, validated neurological markers facilitate differential diagnosis, remote monitoring, and personalized neurological care.
  • This approach offers a scalable and objective method for assessing neurological health through language.