AIMC Topic: Cognitive Dysfunction

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Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

NeuroImage
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adult...

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.

PloS one
BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysi...

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Cell reports. Medicine
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date...

The role of a socially assistive robot in enabling older adults with mild cognitive impairment to cope with the measures of the COVID-19 lockdown: A qualitative study.

Scandinavian journal of occupational therapy
BACKGROUND: During the COVID-19 pandemic, mandatory containment measures led to lockdowns and severely diminished social interaction, with older adults being one vulnerable group. Socially assistive robots (SARs) could prove to be an effective interv...

A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Nature communications
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical...

Use of robots to encourage social engagement between older adults.

Geriatric nursing (New York, N.Y.)
We designed a robotic architecture system within a commercially available socially assistive robot to engage pairs of older adults in multimodal activities over 3 weeks for 6 sessions. The study took place in two assisted living facilities. Seven pai...

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association
We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 41...

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records.

JAMA network open
IMPORTANCE: Detecting cognitive decline earlier among older adults can facilitate enrollment in clinical trials and early interventions. Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline...