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Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction
IntroductionAutism Spectrum Disorder (ASD) diagnosis remains complex due to limited access to large-scale multimodal datas...
CNN-based framework for Alzheimer's disease detection from EEG via dynamic mode decomposition
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are major neurodegenerative disorders with characteristic EEG a...
Enhancing dementia and cognitive decline detection with large language models and speech representation learning
Dementia poses a major challenge to individuals and public health systems. Detecting cognitive decline through spontaneous...
Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging
Assessing the eligibility of Brainomix e-ASPECTS for acute stroke imaging
BackgroundTimely and accurate assessment of acute ischemic stroke is crucial for determining eligibility for mechanical th...
SynSpine: an automated workflow for the generation of longitudinal spinal cord synthetic MRI data
BackgroundSpinal cord atrophy is a key biomarker for tracking disease progression in neurological disorders, including mul...
Computational reconstruction of evolutionary selection in human brain networks
IntroductionThe accumulation of genomic and brain data opens new opportunities for resource friendly, data driven brain ex...
On the need for abstract, deep reinforcement learning models in neuroscience
In science we understand complex phenomena through various models, which exist on a spectrum from high to low abstraction....
Macular: a multi-scale simulation platform for the retina and the primary visual system
We developed Macular, a simulation platform with a graphical interface, designed to produce in silico experiment scenarios...
Editorial: Machine learning algorithms for brain imaging: new frontiers in neurodiagnostics and treatment
The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid 3 advances in m...
CycleGAN models show consistent brain MRI synthesis across datasets supporting downstream tissue characterization in multiple sclerosis
BackgroundSecondary quantitative analysis of brain magnetic resonance imaging (MRI) can provide valuable information for m...
Correction: A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease
A correction refers to a change to their article that the author wishes to publish after publication. The publication of t...
Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals
PurposeDyslexia is a prevalent neurodevelopmental disorder that impairs a children’s ability to reading, writing, and lang...
BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning
Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses ...
A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI
BackgroundBrain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variabili...
Editorial: Multimodal brain data integration and computational modeling
Further, multimodal brain science increasingly extends beyond imaging to genetic, behavioral, and clinical variables, push...
Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer’s disease
Reliable imaging biomarkers are essential for improving early detection of Alzheimer’s disease (AD). We evaluated whether ...
A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction
Vascular network reconstruction is a crucial step in extracting vessel morphology and establishing its topological relatio...
Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis
Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounti...
IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research
In the era of data-intensive science, managing and verifying research processes that include raw data, analysis scripts, w...
Systems Neuroscience Computing in Python (SyNCoPy): a python package for large-scale analysis of electrophysiological data
Introduction In neuroscience, methods such as electroencephalography (EEG), magnetoencephalography (MEG), electrocortico...
Editorial: Reproducible analysis in neuroscience
One of the key ingredients of scientific progress is the ability to repeat, replicate and reproduce independently importan...
Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique
1 Introduction Brain tumor is the most prevalent condition in children and also the most challenging sickness to identify...
Unsupervised method for representation transfer from one brain to another
1 Introduction Acquiring information from the brain not only contributes to understanding the neurological mechanisms und...
Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communicati...
Editorial: Addressing large scale computing challenges in neuroscience: current advances and future directions
1. IntroductionNeuroscience research generates vast amounts of data, requiring advanced computing resources for storage, m...
Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing
In the rapidly evolving field of neuroinformatics, the intersection of artificial intelligence (AI) and neuroscience prese...
Editorial: Emerging trends in large-scale data analysis for neuroscience research
The primary aim of this research topic is to showcase recent progress in data-driven approaches for studying the brain. It...
Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks
Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks
1 Introduction Working memory (WM) is crucial for preparing and organizing goal-directed behaviors, with its functions of...
hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction
1 Introduction High-fidelity reconstruction of electroencephalography (EEG) data is of key relevance to many deep learnin...
Leveraging deep learning for robust EEG analysis in mental health monitoring
1 Introduction Monitoring mental health through electroencephalography (EEG) has become an increasingly important area of...
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