A technical review of canonical correlation analysis for neuroscience applications

Zhuang, Xiaowei; Yang, Zhiguo; Cordes, Dietmar

doi:10.1002/hbm.25090

Cited by 135 publications

(99 citation statements)

References 191 publications

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“…These methods include complex machine learning pipelines, e.g. HPC Netmats Metatrawls, and dimensionality reduction approaches, such as partial least-squares analysis (PLS) 39 or canonical correlation analysis (CCA) 40 . In the current analysis, we employed the connectome-based predictive modelling (CPM) method 9 for its relative simplicity and clear interpretability 41 .…”

Section: Discussionmentioning

confidence: 99%

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Bathelt

Geurts

Borsboom

2020

Preprint

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Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

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Section: Discussionmentioning

confidence: 99%

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Bathelt

Geurts

Borsboom

2020

Preprint

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“…Models E or F ) to the brain response. This, in essence, is what is accomplished by a hybrid model such as CCA (Dmochowski et al, 2017; de Cheveigné et al, 2018; Zhuang et al, 2020). CCA is effective because it allows response variance unrelated to stimulation to be stripped away, leaving a remainder that can be more meanifully related to the stimulus.…”

Section: Discussionmentioning

confidence: 99%

Auditory Stimulus-response Modeling with a Match-Mismatch Task

Cheveigné

Slaney

Fuglsang

et al. 2020

Preprint

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The relation between a continuous ongoing stimulus and the brain response that it evokes can be characterized by a stimulus-response model fit to the data. This systems-identification approach offers insight into perceptual processes within the brain, and it is also of potential practical use for devices such as Brain Computer Interfaces (BCI). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance. Here we focus on a {\em match-mismatch}\ task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it. The match-mismatch task can be used to compare performance of different stimulus-response models. We show that performance in a match-mismatch task and metrics summarizing regression accuracies can provide complementary insights in the relation between stimulus and response. Importantly, the match-mismatch task provides information about discriminatory power, making it directly applicable to BCI applications. Evaluation is performed on a freely available database, and code is available for scripts and functions to allow scrutiny of our results and facilitate comparative evaluation of future developments.

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“…PCA has been found used in a wide range of fields ranging from spike-triggered covariance analysis in neuroscience [49], [50], to quantitative finance [51]- [54] with the most common application being facial recognition [54]- [56] and other applications like medical data correlation [57]- [60].…”

Section: Principal Component Analysismentioning

confidence: 99%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

124

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Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

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A technical review of canonical correlation analysis for neuroscience applications

Cited by 135 publications

References 191 publications

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Auditory Stimulus-response Modeling with a Match-Mismatch Task

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

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