Splenium tract projections of the corpus callosum to the parietal cortex classifies Alzheimer's disease and mild cognitive impairment.

The corpus callosum (CC) is the largest bundle of white matter tracts in the brain connecting the left and right cerebral hemispheres. The posterior region of the CC, known as the splenium, seems to be relatively preserved throughout the lifespan and is regularly examined for indications of various pathologies, including Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). However, the splenium has rarely been investigated in terms of its distinct inter-hemispheric tract bundles that project to bilateral occipital, parietal and temporal areas of the cortex. The aim of the present study was to determine if any of these sub-splenium tract bundles are specifically affected by individuals with AD and MCI compared to normal controls. Diffusion Tensor Imaging was used to directly examine the integrity of these distinct tract bundles and their diffusion metrics were compared between groups of MCI, AD, and control individuals. Results revealed that differences between MCI, AD, and controls were particularly evident at parietal tracts of the CC splenium and were consistent with an interpretation of compromised white matter integrity. Combined parietal tract diffusivity and density information strongly discriminated between AD patients and controls with an accuracy (AUC) of 97.19%. Combined parietal tract diffusivity parameters correctly classified MCI subjects against controls with an accuracy of 74.97%. These findings demonstrated the potential of examining the CC splenium in terms of its distinct inter-hemispheric tract bundles for the diagnosis of AD and MCI.

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance- The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100.

Splenium tracts of the corpus callosum degrade in old age.

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.

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

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.

Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging.

The individual alpha peak frequency (IAPF) of the human electroencephalography (EEG) typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for intergroup differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting-state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10-12 Hz) compared with young adults. By contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins.

Visual and visuomotor interhemispheric transfer time in older adults.

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.