Search

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0–600. The graphs were combined into tiled topographical maps governed by the 10–20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

It is well established that the posterior region of the corpus callosum, known as the splenium, is relatively preserved during the course of normal ageing. However, the effect of age on its distinct interhemispheric tract bundles that project to bilateral occipital, parietal and temporal areas of the cortex, is largely unknown. In the present study, diffusion tensor imaging was used to directly examine the integrity of these distinct segregations and their diffusion metrics were compared between groups of young adults (n = 20, mean age = 30.75) and older adults (n = 19, mean age = 80.21). Results revealed that while occipital tracts were preserved in older adults, parietal and temporal segments were particularly impaired. These findings are the first to indicate the existence of selective alterations in the posterior region of the corpus callosum in older age.

Older adults typically experience reductions in the structural integrity of the anterior channels of the corpus callosum. Despite preserved structural integrity in central and posterior channels, many studies have reported that interhemispheric transfer, a function attributed to these regions, is detrimentally affected by aging. In this study, we use a constrained event-related potential analysis in the theta and alpha frequency bands to determine whether interhemispheric transfer is affected in older adults. The crossed-uncrossed difference and lateralized visual evoked potentials were used to assess interhemispheric transfer in young (18-27) and older adults (63-80). We observed no differences in the crossed-uncrossed difference measure between young and older groups. Older adults appeared to have elongated transfer in the theta band potentials, but this effect was driven by shortened contralateral peak latencies, rather than delayed ipsilateral latencies. In the alpha band, there was a trend toward quicker transfer in older adults. We conclude that older adults do not experience elongated interhemispheric transfer in the visuomotor or visual domains and that these functions are likely attributed to posterior sections of the corpus callosum, which are unaffected by aging.