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Cognitive Dysfunction

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Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.

IEEE transactions on medical imaging
While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain fu...

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

Magnetic resonance imaging
In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD ...

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: Spatial neglect and attention.

NeuroImage
Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inc...

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

PloS one
Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demographic and clinical risk factors using traditional logistical regression methods. We hypothesized tha...

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.

NeuroImage. Clinical
Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/...

Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia.

Dementia and geriatric cognitive disorders
BACKGROUND: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support the care of persons with dementia (PwDs). However, establishing verbal communication with a PwD is cha...

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.

IEEE journal of biomedical and health informatics
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the a...

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Human brain mapping
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be...

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

Journal of neuroscience methods
BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). Hippocampal atrophy can be evaluated in terms of hippocampal volumes and shapes using ...

Deep learning only by normal brain PET identify unheralded brain anomalies.

EBioMedicine
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...