AIMC Topic: Brain
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Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.
Communications biology
Jun 26, 2024
In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a ma...
Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.
F1000Research
Jun 26, 2024
BACKGROUND: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blot...
Shaping dynamical neural computations using spatiotemporal constraints.
Biochemical and biophysical research communications
Jun 25, 2024
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinar...
Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.
Scientific reports
Jun 25, 2024
3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods...
Machine learning and deep learning for classifying the justification of brain CT referrals.
European radiology
Jun 24, 2024
OBJECTIVES: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and ...
Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.
Autism research : official journal of the International Society for Autism Research
Jun 24, 2024
Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter bra...
An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.
Aging cell
Jun 24, 2024
Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to info...
Representations and generalization in artificial and brain neural networks.
Proceedings of the National Academy of Sciences of the United States of America
Jun 24, 2024
Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribu...
Robot-aided assessment and associated brain lesions of impaired ankle proprioception in chronic stroke.
Journal of neuroengineering and rehabilitation
Jun 24, 2024
BACKGROUND: Impaired ankle proprioception strongly predicts balance dysfunction in chronic stroke. However, only sparse data on ankle position sense and no systematic data on ankle motion sense dysfunction in stroke are available. Moreover, the lesio...
Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.
Journal of neurology
Jun 23, 2024
BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.
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