How to capture developmental brain dynamics: Gaps and solutions
Capturing developmental and learning-induced brain dynamics is extremely challenging as
changes occur interactively across multiple levels and emerging functions. Different levels …
changes occur interactively across multiple levels and emerging functions. Different levels …
[HTML][HTML] A hypothesis-driven method based on machine learning for neuroimaging data analysis
There remains an open question about the usefulness and the interpretation of machine
learning (ML) approaches for discrimination of spatial patterns of brain images between …
learning (ML) approaches for discrimination of spatial patterns of brain images between …
ERP source analysis guided by fMRI during familiar face processing
MA Bobes, A Lage-Castellanos, EI Olivares… - Brain Topography, 2019 - Springer
Event related potentials (ERPs) provide precise temporal information about cognitive
processing, but with poor spatial resolution, while functional magnetic resonance imaging …
processing, but with poor spatial resolution, while functional magnetic resonance imaging …
Imaging of neural oscillations with embedded inferential and group prevalence statistics
Magnetoencephalography and electroencephalography (MEG, EEG) are essential
techniques for studying distributed signal dynamics in the human brain. In particular, the …
techniques for studying distributed signal dynamics in the human brain. In particular, the …
On mixture alternatives and Wilcoxon's signed-rank test
JD Rosenblatt, Y Benjamini - The American Statistician, 2018 - Taylor & Francis
The shift alternative model has been the canonical alternative hypothesis since the early
days of statistics. This holds true both in parametric and nonparametric statistical testing. In …
days of statistics. This holds true both in parametric and nonparametric statistical testing. In …
A connection between the pattern classification problem and the General Linear Model for statistical inference
JM Gorriz, J Suckling - arXiv preprint arXiv:2012.08903, 2020 - arxiv.org
A connection between the General Linear Model (GLM) in combination with classical
statistical inference and the machine learning (MLE)-based inference is described in this …
statistical inference and the machine learning (MLE)-based inference is described in this …
Enhancing diagnostic accuracy in neuroimaging through machine learning: advancements in statistical classification and mapping
C Jiménez Mesa - 2023 - digibug.ugr.es
In recent years, the application of arti cial intelligence (AI) techniques in health and
medicine, including neuroimaging, has grown exponentially. Neuroimaging plays a crucial …
medicine, including neuroimaging, has grown exponentially. Neuroimaging plays a crucial …
How to capture developmental brain dynamics: gaps and solutions
V Maaike, W Wouter, B Milene - NPJ Science of Learning, 2021 - search.proquest.com
Capturing developmental and learning-induced brain dynamics is extremely challenging as
changes occur interactively across multiple levels and emerging functions. Different levels …
changes occur interactively across multiple levels and emerging functions. Different levels …
[HTML][HTML] Valid and powerful second-level group statistics for decoding accuracy: information prevalence inference using the i-th order statistic (i-test)
S Hirose - Neuroimage, 2021 - Elsevier
In functional magnetic resonance imaging (fMRI) decoding studies using pattern
classification, a second-level group statistical test is typically performed after first-level …
classification, a second-level group statistical test is typically performed after first-level …
Prevalence estimation
JD Rosenblatt - Handbook of Multiple Comparisons, 2021 - taylorfrancis.com
Given a multivariate parameter,? and appropriate data, prevalence estimation deals with the
counting of the number of entries in? that depart from their hypothesized null values. The …
counting of the number of entries in? that depart from their hypothesized null values. The …