AIMC Topic: Brain

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Machine learning in the clinical and language characterisation of primary progressive aphasia variants.

Cortex; a journal devoted to the study of the nervous system and behavior
INTRODUCTION: Primary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was t...

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

NeuroImage
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue ...

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.

Magnetic resonance imaging
In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known compleme...

Brain tumor detection using statistical and machine learning method.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumo...

Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.

Neuroscience
Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) usi...

Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. During the process, the tuning to amplitude modulation (AM) is successively transformed by a cascade of brain regions. T...

A self-organized recurrent neural network for estimating the effective connectivity and its application to EEG data.

Computers in biology and medicine
OBJECTIVE: Effective connectivity is an important notion in neuroscience research, useful for detecting the interactions between regions of the brain.

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Nature communications
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept...

Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.

The British journal of radiology
OBJECTIVE: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-...

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

IEEE transactions on medical imaging
Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may o...