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Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

Zahra Shirzadi1,2, Bojana Stefanovic1,2, Henri J M M Mutsaerts2,3

  • 1Department of Medical Biophysics, University of Toronto, ON, Canada.

Journal of Magnetic Resonance Imaging : JMRI
|January 23, 2019
PubMed
Abstract

Insights

Arterial spin-labeled (ASL) MRI spatial heterogeneity, measured by sCoV, can differentiate cognitive groups. Lower temporal lobe sCoV indicates cognitive decline, aiding in early detection of Alzheimer's disease.

Area of Science:

  • Neuroimaging
  • Radiology
  • Biomedical Engineering

Background:

  • Arterial spin-labeled (ASL) MRI's spatial coefficient of variation (sCoV) quantifies cerebral blood flow heterogeneity.
  • This metric can indicate delayed blood delivery, visualized as signal variations in ASL imaging.

Purpose of the Study:

  • To evaluate the efficacy of ASL-sCoV in classifying cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts.
  • To determine if ASL-sCoV can serve as a biomarker for cognitive decline.

Main Methods:

  • A prospective cohort study analyzed baseline ASL images from 258 participants (CU, MCI, AD) from the AD neuroimaging initiative.
  • ASL-sCoV was computed for different brain regions, and its association with cognitive groups was assessed using ANCOVA.
  • Repeatability of sCoV was evaluated in a subgroup of CU participants.

Main Results:

  • Global and temporal lobe sCoV significantly differed across cognitive groups (P = 0.006).
  • Temporal lobe sCoV was lower in CU individuals compared to MCI and AD groups.
  • ASL-sCoV, combined with amyloid and glucose uptake, achieved high accuracy in classifying CU vs. MCI (78%) and CU vs. AD (97%).

Conclusions:

  • ASL-derived spatial heterogeneity is a promising biomarker for distinguishing cognitive states.
  • ASL-sCoV, particularly in the temporal lobe, can aid in differentiating cognitively unimpaired individuals from those with MCI or AD.
  • This metric holds potential for early detection and monitoring of neurodegenerative diseases.
Keywords:
Alzheimer's diseasearterial spin-labeled MRIcerebral blood flowreceiver operating characteristicreproducibilityspatial coefficient of variation

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