Deep learning in mental health outcome research: a scoping review
Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
an individual's physical health. Recently, artificial intelligence (AI) methods have been …
Clinical characteristics, pathophysiology, and management of noncentral nervous system cancer‐related cognitive impairment in adults
JS Wefel, SR Kesler, KR Noll… - CA: a cancer journal for …, 2015 - Wiley Online Library
Answer questions and earn CME/CNE Over the past few decades, a body of research has
emerged confirming what many adult patients with noncentral nervous system cancer have …
emerged confirming what many adult patients with noncentral nervous system cancer have …
[HTML][HTML] Machine learning for neuroimaging with scikit-learn
A Abraham, F Pedregosa, M Eickenberg… - Frontiers in …, 2014 - frontiersin.org
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
Their main virtue is their ability to model high-dimensional datasets, eg multivariate analysis …
Their main virtue is their ability to model high-dimensional datasets, eg multivariate analysis …
[图书][B] After phrenology: Neural reuse and the interactive brain
ML Anderson - 2021 - books.google.com
A proposal for a fully post-phrenological neuroscience that details the evolutionary roots of
functional diversity in brain regions and networks. The computer analogy of the mind has …
functional diversity in brain regions and networks. The computer analogy of the mind has …
Decoding neural representational spaces using multivariate pattern analysis
JV Haxby, AC Connolly… - Annual review of …, 2014 - annualreviews.org
A major challenge for systems neuroscience is to break the neural code. Computational
algorithms for encoding information into neural activity and extracting information from …
algorithms for encoding information into neural activity and extracting information from …
Mitigating site effects in covariance for machine learning in neuroimaging data
To acquire larger samples for answering complex questions in neuroscience, researchers
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …
have increasingly turned to multi‐site neuroimaging studies. However, these studies are …
Encoding and decoding in fMRI
Over the past decade fMRI researchers have developed increasingly sensitive techniques
for analyzing the information represented in BOLD activity. The most popular of these …
for analyzing the information represented in BOLD activity. The most popular of these …
[HTML][HTML] A common, high-dimensional model of the representational space in human ventral temporal cortex
We present a high-dimensional model of the representational space in human ventral
temporal (VT) cortex in which dimensions are response-tuning functions that are common …
temporal (VT) cortex in which dimensions are response-tuning functions that are common …
Machine learning classifiers and fMRI: a tutorial overview
Interpreting brain image experiments requires analysis of complex, multivariate data. In
recent years, one analysis approach that has grown in popularity is the use of machine …
recent years, one analysis approach that has grown in popularity is the use of machine …
Multivariate pattern analysis of fMRI: the early beginnings
JV Haxby - Neuroimage, 2012 - Elsevier
In 2001, we published a paper on the representation of faces and objects in ventral temporal
cortex that introduced a new method for fMRI analysis, which subsequently came to be …
cortex that introduced a new method for fMRI analysis, which subsequently came to be …