AIMC Topic: Cognitive Dysfunction

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Cortical surface registration using unsupervised learning.

NeuroImage
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface propert...

Home-Based Cognitively Assistive Robots: Maximizing Cognitive Functioning and Maintaining Independence in Older Adults Without Dementia.

Clinical interventions in aging
Promoting health and prolonging independence in the home is a priority for older adults, caregivers, clinicians, and society at large. Rapidly developing robotics technology provides a platform for interventions, with the fields of physically and soc...

An Efficient Combination among sMRI, CSF, Cognitive Score, and 4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine.

Computational intelligence and neuroscience
Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere...

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

NeuroImage. Clinical
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have...

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

Human brain mapping
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS).

BMC neurology
BACKGROUND: Cognitive impairment is common in patients with multiple sclerosis (MS). Accurate and repeatable measures of cognition have the potential to be used as markers of disease activity.

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.

Human brain mapping
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to ...

Deep learning based mild cognitive impairment diagnosis using structure MR images.

Neuroscience letters
Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth leading disease mostly found in the aged population. Early intervention of MCI will possibly delay the progress towards AD, and this makes it very import...

Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.

Medical image analysis
Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have been widely applied to analyzing and diagnosing brain diseases, such as Alzheimer's disease (AD) and its prodrome stage, i.e., mild cognitive impairmen...