Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI

Abstract

Introduction: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.

Methods: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.

Results: Increased brain conductivity was associated with Aβ and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.

Discussion: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.

Highlights: Brain tissue conductivity correlates with Aβ and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.

Keywords: MRI; PET; dementia; electrical conductivity; positron emission tomography; protein aggregation.

FIGURE 1

Representative slices from group average images of different modalities in MNI space for CN, MCI, and Dem groups. (A) CN group‐average image. (B) MCI group‐average image. (C) Dem group‐average image. Arrows highlight key regions of increased brain conductivity associated with brain atrophy and cerebrospinal fluid (CSF) expansion (red, bilateral), elevated Aβ signal (yellow), and higher tau signal (green). Aβ, amyloid beta; CN, cognitively normal controls; Dem, dementia; MCI, mild cognitive impairment; S/m, Siemens per meter; SUVR, standardized uptake value ratio.

FIGURE 2

Voxel‐based analysis of relationship between brain tissue conductivity and protein aggregation. (A) Brain regions showing significant correlations between conductivity and Aβ‐PET (left) and tau‐PET (right) after adjusting for age (p < .05, TFCE corrected). (B) Brain regions with significant difference between dementia and CN in conductivity (left), Aβ‐PET (middle), and tau‐PET (right) after adjusting for age (P < .05, TFCE corrected). Aβ, amyloid beta; CN, cognitively normal controls; Dem, dementia; MCI, mild cognitive impairment; PET, positron emission tomography; TFCE, threshold‐free cluster enhancement.

FIGURE 3

ROI analysis of the relationship between brain tissue conductivity and protein aggregation with regional brain volume as the covariate. (A) Regions included in the correlation analysis. (B) Regional correlations between conductivity and SUVR values. (C) Conductivity levels of different regions in the three participant groups. *p < .05. CN, cognitively normal controls; Dem, dementia; MCI, mild cognitive impairment; ROI, region of interest; SUVR, standardized uptake value ratio.

FIGURE 4

Relationship between electrical conductivity and cognition and plasma protein levels, with regional brain volume as covariate. (A) Relationship between brain tissue conductivity and MMSE scores. (B) Relationship between CSF conductivity and plasma protein levels. CSF, cerebral spinal fluid; GFAP, glial fibrillary acidic protein; MMSE, Mini‐Mental State Examination; NFL, neurofilament light; pTau‐181, phosphorylated‐tau‐181.

FIGURE 5

Area under Receiver Operating Characteristic curve for discrimination of dementia using electrical conductivity and gray matter volumes. (A) Discrimination between CN and MCI. (B) Discrimination between MCI and dementia. (C) Discrimination between CN and dementia. AUC, area under curve; CN, cognitively normal controls; CSF, cerebral spinal fluid; Dem, dementia; GMV, gray matter volume; MCI, mild cognitive impairment; MTC, middle temporal cortex; PCC, posterior cingulate cortex.

FIGURE 6

Cortical profiles of PLS regression results. (A) Conductivity, (C) Aβ, and (E) tau difference maps between dementia and CN show spatial patterns similar to the regionally weighted sum of gene expression scores defined by PLS2. Positive correlations between regional PLS2 scores and (B) conductivity, (D) Aβ, and (F) tau difference. In the scatterplot, each point represents one of 148 cortical regions. The results imply that genes with higher weights on PLS2 also exhibited greater expression levels in cortical regions with increased signal differences. Aβ, amyloid beta; CN, cognitively normal controls; PLS, partial least squares.

FIGURE 7

Enrichment analyses of genes associated with brain tissue conductivity in dementia. (A) Conductivity‐related GO terms. (B) Aβ aggregation‐related GO terms. (C) Tau aggregation‐related GO terms. These GO terms for biological processes were significantly enriched in genes with higher weights defined by PLS2. The terms are plotted in semantic space, with more similar terms clustered together. Non‐redundant GO terms significant at g:SCS‐corrected p < .001 are shown, with larger, darker circles indicating greater significance. (D) Overlap of GO terms for biological processes across the three categories, with specific terms highlighted. Aβ, amyloid beta; GO, Gene Ontology; PLS, partial least squares.