AIMC Topic: Brain
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Investigating the intrinsic top-down dynamics of deep generative models.
Scientific reports
Jan 22, 2025
Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity ...
Development of compositionality through interactive learning of language and action of robots.
Science robotics
Jan 22, 2025
Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental que...
Neuromorphic computing at scale.
Nature
Jan 22, 2025
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficien...
[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Jan 21, 2025
Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain...
Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...
Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.
Alzheimer's research & therapy
Jan 21, 2025
BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatme...
Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods (San Diego, Calif.)
Jan 19, 2025
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...
Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.
Neuroradiology
Jan 18, 2025
INTRODUCTION: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI a...
PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.
Journal of neuroscience methods
Jan 18, 2025
BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more ef...
AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice.
European journal of radiology
Jan 18, 2025
OBJECTIVES: In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of ...
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