Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.
Hao-Ting Wang,Hao-Ting Wang,Jonathan Smallwood,Janaina Mourao-Miranda,Cedric Huchuan Xia,Theodore D. Satterthwaite,Danielle S. Bassett,Danilo Bzdok +7 more
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TLDR
Canonical correlation analysis is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities and so is well suited to the analysis of big neuroscience datasets.About:
This article is published in NeuroImage.The article was published on 2020-04-08 and is currently open access. It has received 133 citations till now.read more
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Hyperalignment: Modeling Shared Information Encoded in Idiosyncratic Cortical Topographies
TL;DR: This Perspective presents the conceptual framework that motivates hyperalignment, its computational underpinnings for joint modeling of a common information space and idiosyncratic cortical topographies, and discuss implications for understanding the structure of cortical functional architecture.
Posted ContentDOI
On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations
Markus Helmer,Shaun Warrington,Ali-Reza Mohammadi-Nejad,Ali-Reza Mohammadi-Nejad,Jie Lisa Ji,Amber Howell,Benjamin Rosand,Alan Anticevic,Stamatios N. Sotiropoulos,Stamatios N. Sotiropoulos,Stamatios N. Sotiropoulos,John D. Murray +11 more
TL;DR: A generative model is developed to simulate synthetic datasets with multivariate associations, and characterized how obtained feature profiles can be unstable, which hinders interpretability and generalizability, unless a sufficient number of samples are available to estimate them.
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How to Interpret Resting-State fMRI: Ask Your Participants
TL;DR: In this article, the authors argue that understanding the role of ongoing experience in rsfMRI requires access to standardized, temporally resolved, scientifically validated first-person descriptions of those experiences, and suggest best practices for obtaining those descriptions via introspective methods appropriately adapted for use in fMRI research.
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Linking interindividual variability in brain structure to behaviour
TL;DR: In this article , the authors examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations, as well as present and future research directions.
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Multi-dimensional connectivity: a conceptual and mathematical review.
TL;DR: The most common multi-dimensional connectivity methods are reviewed, from an intuitive perspective, from a formal (mathematical) point of view, and through a number of simulated and real data examples that illustrate the strengths and weaknesses of each method.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Multivariate Data Analysis
TL;DR: This book deals with probability distributions, discrete and continuous densities, distribution functions, bivariate distributions, means, variances, covariance, correlation, and some random process material.
Journal ArticleDOI
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain
Nathalie Tzourio-Mazoyer,B. Landeau,D. Papathanassiou,Fabrice Crivello,Octave Etard,Nicolas Delcroix,Bernard Mazoyer,Marc Joliot +7 more
TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.