1
Characterization of focal EEG signals: A review
Epilepsy is a common neurological condition that can occur in anyone at any age. Electroencephalogram (EEG) signals of non-focal (NF) and focal (F) types contain brain activity information that ... [Read More]
1
The use of super-resolution techniques to reduce slice thickness in functional MRI
The problem of increasing the slice resolution of functional MRI (fMRI) images without a loss in signal-to-noise ratio is considered. In standard fMRI experiments,... [Read More]
1
Introduction to Working with MRI Data in Python
Why Python? Python is rapidly becoming the standard language for data analysis, visualization and automated workflow building. It is a free and open-source software that is relatively easy to pi... [Read More]
1
Resting State Network Connectivity is attenuated by fMRI acoustic noise
IntroductionDuring the past decades, there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance im... [Read More]
1
BOLD5000, a public fMRI dataset while viewing 5000 visual images
Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual percep... [Read More]
1
Frontiers | The Benefit of Slice Timing Correction in Common fMRI Preprocessing Pipelines | Neuroscience
Due to the nature of fMRI acquisition protocols, slices cannot be acquired simultaneously, and as a result, are temporally misaligned from each other. To correct from this misalignment, preprocessing ... [Read More]
1
1
Data – Cognitive & Affective Neuroscience Laboratory
In the interest of full-disclosure and to be as open-data/source as possible, we are including links to our data reported in published articles. For our meta-analysis projects, we have incl... [Read More]
1
(Group) Bayesian Representational Similarity Analysis
Bayesian RSA builds a generative model for fMRI data which include a covariance structure as estimation target. The covariance structure specifies the distribution by which the unknown spatial neu... [Read More]
1
An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets
Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. T... [Read More]
1
1
Frontiers | A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia | Human Neuroscience
We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) dat... [Read More]
1
OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis
OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedbacktraining based on activity, connectivity and multivariate pattern analysis [Read More]
1
Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses | GigaScience | Oxford Academic
Background. The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings [Read More]
1
On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data
Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fM... [Read More]