Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia

Abstract

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer’s disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer’s disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer’s disease.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

“…Importantly, no single parcellation consistently outperforms the others across all evaluation criteria ( Arslan et al, 2018 ). Thus, by considering the various studies that used the AAL atlas in dementia research, even with ML methods ( Asim et al, 2018 ; Bachli et al, 2020 ; Castellazzi et al, 2020 ; Hao et al, 2020 ; McMillan et al, 2014 ; Park et al, 2022 ; Sedeño et al, 2017 ), our choice of parcellation was based on the reproducibility criteria. A systematic comparison of multiple atlases across the different modalities is beyond the scope of this work.…”