Small sample sizes reduce the replicability of task-based fMRI studies
Despite a growing body of research suggesting that task-based functional magnetic
resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too …
resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too …
[HTML][HTML] Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects
The integration of positron emission tomography (PET) and single-photon emission
computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms …
computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms …
Review of functional MRI in HIV: effects of aging and medication
CS Hakkers, JE Arends, RE Barth, S Du Plessis… - Journal of …, 2017 - Springer
HIV-associated neurocognitive disorder (HAND) is a frequently occurring comorbidity of HIV
infection. Evidence suggests this condition starts subclinical before a progression to a …
infection. Evidence suggests this condition starts subclinical before a progression to a …
Valid population inference for information-based imaging: From the second-level t-test to prevalence inference
In multivariate pattern analysis of neuroimaging data,'second-level'inference is often
performed by entering classification accuracies into a t-test vs chance level across subjects …
performed by entering classification accuracies into a t-test vs chance level across subjects …
Bayesian inference of population prevalence
Within neuroscience, psychology, and neuroimaging, the most frequently used statistical
approach is null hypothesis significance testing (NHST) of the population mean. An …
approach is null hypothesis significance testing (NHST) of the population mean. An …
A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data Page 1 The Annals
of Applied Statistics 2016, Vol. 10, No. 2, 638–666 DOI: 10.1214/16-AOAS926 © Institute of …
of Applied Statistics 2016, Vol. 10, No. 2, 638–666 DOI: 10.1214/16-AOAS926 © Institute of …
[HTML][HTML] Statistical agnostic mapping: A framework in neuroimaging based on concentration inequalities
In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a
function for the pattern recognition problem that would fulfill a relationship between the …
function for the pattern recognition problem that would fulfill a relationship between the …
Quantifying differences between affine and nonlinear spatial normalization of FP-CIT SPECT images
D Castillo-Barnes, C Jimenez-Mesa… - … Journal of Neural …, 2022 - World Scientific
Spatial normalization helps us to compare quantitatively two or more input brain scans.
Although using an affine normalization approach preserves the anatomical structures, the …
Although using an affine normalization approach preserves the anatomical structures, the …
A connection between pattern classification by machine learning and statistical inference with the General Linear Model
A connection between the general linear model (GLM) with frequentist statistical testing and
machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM …
machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM …
Is K-fold cross validation the best model selection method for Machine Learning?
As a technique that can compactly represent complex patterns, machine learning has
significant potential for predictive inference. K-fold cross-validation (CV) is the most common …
significant potential for predictive inference. K-fold cross-validation (CV) is the most common …