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Disturbance Rejection in the Cerebellum
This paper further explores the hypothesis, put forward in our earlier work, that the cerebellum performs disturbance rejection to meet the...
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A Cerebellum-Inspired Model-Free Kinematic Control Method with RCM Constraint
Minimally invasive surgery requires to reduce trauma to patients during the movement of the inserted robot end-effector. The cerebellum is able to...
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Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learning
Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development...
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Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation
To characterize early cerebellum development, accurate segmentation of the cerebellum into white matter (WM), gray matter (GM), and cerebrospinal...
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The Antagonistic Alterations of Cerebellar Functional Segregation and Integration in Athletes with Fast Demands of Visual-Motor Coordination
The theoretical foundation of brain-computer interface partly lies in the neural mechanism of motor function plasticity. There has been extensive...
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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson’s disease based on magnetic resonance imaging
Identification of Parkinson’s disease (PD) patients at risk for development of dementia is crucial for early intervention. However, diagnosing...
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Weakly Supervised Cerebellar Cortical Surface Parcellation with Self-Visual Representation Learning
The cerebellum (i.e., little brain) plays an important role in motion and balances control abilities, despite its much smaller size and deeper sulci...
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Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand
Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to...
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NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images
Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of...
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Hierarchical Parcellation of the Cerebellum
Parcellation of the cerebellum in an MR image has been used to study regional associations with both motion and cognitive functions. Despite the...
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3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores
Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between...
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Survey of Computational Modeling of the Functional Parts of the Brain
AbstractA review of connectionist models of the nervous system is presented. First, the information about the main elements of the models and...
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Convolutional Neural Networks as Support Tools for Spinocerebellar Ataxia Detection from Magnetic Resonances
Spinocerebellar ataxias (SCAs) are a group of neurodegenerative diseases, characterized by loss of balance and motor coordination due to dysfunction...
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Autism spectrum disorder diagnosis using fractal and non-fractal-based functional connectivity analysis and machine learning methods
Autism spectrum disorder (ASD) is a neurological condition characterized by impaired functional connectivity (FC) networks in the brain. There are...
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Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks
Community detection in complex signed networks is a significant challenge, traditionally addressed using the Louvain method directly applied to the...
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Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis
In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages...
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A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation
Accurate segmentation of the brain is crucial for many clinical processes. Traditionally, manual segmentation was used, but this had several...
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Combining Registration Errors and Supervoxel Classification for Unsupervised Brain Anomaly Detection
Automatic detection of brain anomalies in MR images is challenging and complex due to intensity similarity between lesions and healthy tissues as...
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Hierarchical Approach for Neonate Cerebellum Segmentation from MRI: An Experimental Study
Morphometric analysis of brain structures is of high interest for premature neonates, in particular for defining predictive neurodevelopment...
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An Adaboost Support Vector Machine Based Harris Hawks Optimization Algorithm for Intelligent Quotient Estimation from MRI Images
Human intelligence is measured using the Intelligent Quotient (IQ) score which is derived from a range of tests. Due to a lack of a large dataset, IQ...
