AIMC Topic: Neuroimaging

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Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Medical image analysis
Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonl...

Improving the detection of autism spectrum disorder by combining structural and functional MRI information.

NeuroImage. Clinical
Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase i...

Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study.

NeuroImage. Clinical
The locus coeruleus (LC) has a long-established role in the attentional and arousal response to threat, and in the emergence of pathological anxiety in pre-clinical models. However, human evidence of links between LC function and pathological anxiety...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Medical image analysis
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. Th...

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages...

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

NeuroImage. Clinical
INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the diseas...

Prediction of lower-grade glioma molecular subtypes using deep learning.

Journal of neuro-oncology
INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.

Submillimeter MR fingerprinting using deep learning-based tissue quantification.

Magnetic resonance in medicine
PURPOSE: To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach.

Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the multi-modality based methods usuall...