Neurology

Region-wise stacking ensembles for estimating brain-age using MRI

Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms and feature extraction methods have been employed to improve predictions and explore healthy and accelerated aging e.g. neurodegenerative and psychiatric disorders. The high-dimensional MRI data pose challenges to building generalizable and interpre...

Deep Learning for Early Alzheimer Disease Detection with MRI Scans

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares ...

Data mining the functional architecture of the brain's circuitry

The brain is a highly complex organ consisting of a myriad of subsystems that flexibly interact and adapt over time and context to enable perception...

IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients

Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method b...

On the challenges of detecting MCI using EEG in the wild

Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) d...

TimeFlow: Longitudinal Brain Image Registration and Aging Progression Analysis

Predicting future brain states is crucial for understanding healthy aging and neurodegenerative diseases. Longitudinal brain MRI registration, a cor...

A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly...

Head Motion Degrades Machine Learning Classification of Alzheimer's Disease from Positron Emission Tomography

Brain positron emission tomography (PET) imaging is broadly used in research and clinical routines to study, diagnose, and stage Alzheimer's disease...

EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics

The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, al...

Tutorial: VAE as an inference paradigm for neuroimaging

In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensi...

Combining imaging and shape features for prediction tasks of Alzheimer's disease classification and brain age regression

We investigate combining imaging and shape features extracted from MRI for the clinically relevant tasks of brain age prediction and Alzheimer's dis...

Exploring the distribution of connectivity weights in resting-state EEG networks

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for dec...

Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory

Objective: This study explores a novel deep learning approach for EEG analysis and perceptual state guidance, inspired by Level of Detail (LOD) theo...

The tardigrade as an emerging model organism for systems neuroscience

We present the case for developing the tardigrade (Hypsibius exemplaris) into a model organism for systems neuroscience. These microscopic, transpar...

MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework

Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) an...

CognoSpeak: an automatic, remote assessment of early cognitive decline in real-world conversational speech

The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and...

Recovery of activation propagation and self-sustained oscillation abilities in stroke brain networks

Healthy brain networks usually show highly efficient information communication and self-sustained oscillation abilities. However, how the brain netw...

Emergence of Painting Ability via Recognition-Driven Evolution

From Paleolithic cave paintings to Impressionism, human painting has evolved to depict increasingly complex and detailed scenes, conveying more nuan...

Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures

This study examined the viability of enhancing the prediction accuracy of artificial neural networks (ANNs) in image classification tasks by develop...

Motif Discovery Framework for Psychiatric EEG Data Classification

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response ...