Abstract
Vascular cognitive impairment (VCI) plays an important role in dementia in elderly people, and refers to the contribution of vascular pathology to the entire spectrum of cognitive disorders, ranging from mild cognitive impairment to severe dementia, as well as the pathological spectrum, from 'pure' Alzheimer disease through degrees of vascular comorbidity to 'pure' vascular dementia. In the present study, we investigated the relationship between cognitive dysfunction and systemic metabolic disorders, by employing deep learning (DL). We studied 202 patients (73.4±13.0years), 94.6% of whom were undergoing treatment for lifestyle diseases, and 68.8% of whom had a history of cerebrovascular disorder. We evaluated cognitive dysfunction by performing a Mini Mental State Examination (MMSE). We performed general blood examination, including Complete Blood Count and Basic Metabolic Panel, and measured cerebral blood oxygenation in the prefrontal cortex (PFC) using time-resolved near infrared spectroscopy (TNIRS). We then used deep neural networks to assess the MMSE scores of the subjects based on the TNIRS parameters and the blood examination data, independently. Next, we compared predicted MMSE scores based on the TNIRS and the blood examination. There was a significant positive correlation between the TNIRS parameters and the blood examination data (r=0.6, p<0.01). These observations suggest that cognitive dysfunction in patients with VCI may be caused by combinations of systemic metabolic disorders such as energy and oxygen metabolisms and cerebral circulatory disturbance due to arteriosclerosis resulting from lifestyle-related diseases.
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