Cortical Brain Imaging Research Articles

Genetic overlap between cortical brain morphometry and frontotemporal dementia risk

AbstractBackgroundFrontotemporal dementia (FTD) has a complex genetic aetiology, where the precise mechanisms underlying the selective vulnerability of brain regions remain unknown.MethodWe leveraged summary‐based data from genome‐wide association studies (GWASs) and performed LD‐score regression to estimate pairwise genetic correlations between FTD risk and cortical brain imaging. Then, we isolate specific genomic loci with a shared aetiology between FTD and brain structure. We also performed functional annotation, summary‐based‐data Mendelian randomisation for eQTL using human peripheral blood and brain tissue data and evaluated the gene expression in mice targeted brain regions to better understand the dynamics of the FTD candidate genes.ResultPairwise genetic correlation estimates between FTD and brain morphology measures were high but not statistically significant. We identified five brain regions with a strong genetic correlation (rg > 0.45) with FTD risk. Functional annotation identified eight protein‐coding genes. Building upon these findings, we show in a mouse model of FTD that cortical NSF expression decreases with age.ConclusionOur results highlight the molecular and genetic overlap between brain morphology and higher risk for FTD, specifically for the right inferior parietal surface area and right medial orbitofrontal cortical thickness. In addition, our findings implicate NSF gene expression in the aetiology of FTD.

Genetic overlap between cortical brain morphometry and frontotemporal dementia risk.

Frontotemporal dementia (FTD) has a complex genetic etiology, where the precise mechanisms underlying the selective vulnerability of brain regions remain unknown. We leveraged summary-based data from genome-wide association studies (GWAS) and performed LD score regression to estimate pairwise genetic correlations between FTD risk and cortical brain imaging. Then, we isolated specific genomic loci with a shared etiology between FTD and brain structure. We also performed functional annotation, summary-data-based Mendelian randomization for eQTL using human peripheral blood and brain tissue data, and evaluated the gene expression in mice targeted brain regions to better understand the dynamics of the FTD candidate genes. Pairwise genetic correlation estimates between FTD and brain morphology measures were high but not statistically significant. We identified 5 brain regions with a strong genetic correlation (rg > 0.45) with FTD risk. Functional annotation identified 8 protein-coding genes. Building upon these findings, we show in a mouse model of FTD that cortical N-ethylmaleimide sensitive factor (NSF) expression decreases with age. Our results highlight the molecular and genetic overlap between brain morphology and higher risk for FTD, specifically for the right inferior parietal surface area and right medial orbitofrontal cortical thickness. In addition, our findings implicate NSF gene expression in the etiology of FTD.

Cortical brain imaging by adaptive filtering of NIRS signals

This paper presents an online brain imaging framework for cognitive tasks conducted with functional near-infrared spectroscopy (fNIRS). The measured signal at each channel is regarded as the output from a linear system with unknown coefficients. The unknown coefficients are estimated by using the recursive least squares estimation (RLSE) method. The validity of the estimated parameters is tested using the t-statistics. Contrary to the classical approach that is offline and applies the same preprocessing scheme to all channels, the proposed RLSE method for a linear model formulation provides an independent robust adaptive process for individual channels. The experiments carried out with two fNIRS instruments (continuous-wave and frequency-domain) have verified the potential of the proposed methodology which can facilitate a prompt medical diagnostics by providing real-time brain activation maps.