AIMC Topic: Brain

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Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

Neurobiology of disease
BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in exploring the mechanism of seizures and localizing the epileptogenic zone (EZ). This study aims to i...

Effects of concurrent HER2-directed therapy on development of cerebral radionecrosis after stereotactic radiotherapy: a systematic review.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
PURPOSE: With increasing use of human epithelial growth factor receptor two (HER2)-targeted therapies, outcomes for numerous breast cancer patients have improved. Nevertheless, patients with HER2-positive tumours face a comparatively heightened risk ...

Uncovering Image-Driven Subtypes with Distinct Pathology and Clinical Course in Autopsy-Confirmed Four Repeat Tauopathies.

Annals of neurology
OBJECTIVES: The four-repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and globular glial tauopathy (GGT). This study aimed to characterize spatiotempor...

A deep learning model for accurate segmentation of the Drosophila melanogaster brain from Micro-CT imaging.

Developmental biology
The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, and the advance of software platforms that enable accurate microstructur...

Symbolic and hybrid AI for brain tissue segmentation using spatial model checking.

Artificial intelligence in medicine
Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, exp...

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) da...

Dynamic Multi-scale Feature Integration Network for unsupervised MR-CT synthesis.

Neural networks : the official journal of the International Neural Network Society
Unsupervised MR-CT synthesis presents a significant opportunity to reduce radiation exposure from CT scans and lower costs by eliminating the need for both MR and CT imaging. However, many existing unsupervised methods face limitations in capturing d...

A systematic review of artificial intelligence techniques based on electroencephalography analysis in the diagnosis of epilepsy disorders: A clinical perspective.

Epilepsy research
In recent years, Artificial Intelligence (AI), with a specific emphasis on attention mechanisms instead of conventional Deep Learning (DL) or Machine Learning (ML), has demonstrated significant applicability across diverse medical domains. This paper...

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder.

Neural networks : the official journal of the International Neural Network Society
Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network m...