Associations of genetic liability for Alzheimer's disease with cognition and eye movements in a large, population-based cohort study

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

Deep Learning-Based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Differential eye movement features between Alzheimer's disease patients with and without depressive symptoms

BACKGROUND

Accurately diagnosing depressive symptoms in Alzheimer's disease (AD) patients is often challenging. Eye movement parameters have been demonstrated as biomarkers for assessing cognition and psychological conditions.

AIM

To investigate the differences in eye movement between AD patients with and without depressive symptoms.

METHODS

Eye movement data of 65 AD patients were compared between the depressed AD (D-AD) and non-depressed AD (nD-AD) groups. Logistic regression analysis was employed to identify diagnostic biomarkers and the ROC curve was plotted. The correlation between eye movement and HAMD-17 scores was assessed by partial correlation analysis.

RESULTS

The D-AD patients showed longer saccade latency and faster average/peak saccade velocities in the overlap prosaccade test, longer average reaction time and faster average saccade velocity in the gap prosaccade test, longer start-up durations, slower pursuit velocity, more offsets, and larger total offset degrees in the smooth pursuit test, and poorer fixation stability in both the central and lateral fixation tests compared to nD-AD patients. The start-up duration in the smooth pursuit test and the number of offsets in the central fixation test were identified as the diagnostic eye movement parameters for D-AD patients with the area under the ROC curves of 0.8011. Partial correlation analysis revealed that the start-up duration and pursuit velocity in the smooth pursuit test and the total offset degrees in the lateral fixation test were correlated with HAMD-17 scores in D-AD patients.

DISCUSSION AND CONCLUSIONS

Eye movement differences may help to differentiate D-AD patients from nD-AD patients in a non-invasive and cost-effective manner.