SCI Abstract

search
+
Super-resolution microscopy and deep learning methods: what can they bring to neuroscience: from neuron to 3D spine segmentation
In recent years, advances in microscopy and the development of novel fluorescent probes have significantly improved neuron...
Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on phonocardiogram signals
IntroductionHeart disease is one of the leading causes of mortality worldwide, and early detection is crucial for effectiv...
Speech pattern disorders in verbally fluent individuals with autism spectrum disorder: a machine learning analysis
IntroductionDiagnosing Autism Spectrum Disorder (ASD) in verbally fluent individuals based on speech patterns in examiner-...
Information-theoretic gradient flows in mouse visual cortex
IntroductionNeural activity can be described in terms of probability distributions that are continuously evolving in time....
Editorial: Women pioneering neuroinformatics and neuroscience-related machine learning, 2024
In recent years, neurosciences have undergone a translational turn [Kapur et al., 2012], as computational tools from AI, n...
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
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...
Artificial intelligence role in advancement of human brain connectome studies
Artificial intelligence role in advancement of human brain connectome studies
Neurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functio...
Reproducible brain PET data analysis: easier said than done
Reproducible brain PET data analysis: easier said than done
While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural m...
Can micro-expressions be used as a biomarker for autism spectrum disorder?
Can micro-expressions be used as a biomarker for autism spectrum disorder?
IntroductionEarly and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it r...
Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm
Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm
In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected e...
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: 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...
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...
Load More
format_indent_increase