A-157 How Important Is Sustained Attention in Reversal Learning and Visual Task Shifting Abilities: A Canonical Correlation Analysis in Adults

Abstract Objective We used canonical correlation analysis (CCA) to examine the relationship between performance on cognitive neuroscience measures of sustained attention, deterministic reversal learning (DRLT), and visual task-shifting (VTS). We evaluated whether DRLT and VTS predicted performance on the Continuous Performance Test-II (CPT-II). Method Participants were 1011 adults from the Consortium for Neuropsychiatric Phenomics. The first CCA was conducted between four VST variables (set 1) and three CPT-II variables (set 2). The second CCA was conducted using eight Reversal Learning variables (set 1) and three CPT-II variables (set 2). Results Our first CCA suggests that accuracy of performance in VTS predicts CPT-II measures, Rc = 0.33, Wilks’s λ = 0.86, F(12, 2646) = 1.92, p < .001. The analysis revealed a positive relationship with Hits (=0.87) and a negative relationship with FA (= − 0.76), consistent with sustained attention. The second CCA revealed that acquisition trials and RT on reversal trials significantly predicted less FA and more hits on the CPT-II, Rc = 0.23, Wilks’s λ = 0.90, F(24, 1273) = 1.92, p = .005. Conclusion Our multivariate findings confirm that attention is significantly involved in executive and mnemonic processes. To our knowledge, we are the first neuroscientific group to report multivariate evidence from a large data set that confirms sustained attention plays a significant role in reversal learning and task-shifting. Our results show that the CPT-II FA and mean RT variables specifically are important predictors of reversal learning and task-shifting, strengthening the concurrent validity of our experimental measures.

Download Full-text

A Canonical Correlation Analysis of Relationships Between Growth, Compositional Traits and Longevity, Lifetime Productivity and Efficiency in Polish Landrace Sows

Annals of Animal Science ◽

10.2478/aoas-2014-0006 ◽

2014 ◽

Vol 14 (2) ◽

pp. 257-270 ◽

Cited By ~ 1

Author(s):

Magdalena Sobczyńska ◽

Tadeusz Blicharski ◽

Mirosław Tyra

Keyword(s):

Growth Rate ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Performance Test ◽

Selection Index ◽

Phenotypic Selection ◽

Backfat Thickness ◽

Canonical Variate ◽

Weaned Piglets

Abstract Relationships between performance test traits (growth rate, backfat thickness, loin depth, lean meat percentage, exterior, phenotypic selection index) and longevity traits (length of productive life, number of litters, total number of weaned pigs, number of weaned piglets per year, number of litters per year) in Landrace sows were evaluated using canonical correlation analysis. The data set consisted of 23,012 purebred sows that farrowed from 1994 to 2011 in 161 herds. The first three canonical correlations (0.37, 0.25, 0.07) were highly significant (P<0.0001). Correlations of the first canonical variate with the original measured variables indicated that sows with high values for this variate had lower growth rate (r=-0.31) and loin depth (r=-0.43), greater backfat thickness (r=0.23), as well as being older at birth of their last litter (r=0.98). These sows also had a greater number of litters (r=0.94) and better lifetime efficiency (r=0.61 and r=0.70 for number of weaned piglets per year and number of litters per year, respectively). Canonical loadings for the second canonical function indicate that sows with high values for the second set of variates had high growth rate (r=0.79) and phenotypic selection index (r=0.83), excellent conformation (r=0.62), as well as better efficiency in pig production (r=0.67). The squared multiple correlations show that the first canonical variate of the performance traits is a poor predictor of longevity (0.13) and nearly useless for predicting efficiency traits (0.07). Performance test traits explain 11% of the variance in the variables of longevity and lifetime productivity, whereas dependent variables explain only 3% of the variance in performance test traits. The relationships between performance test data and subsequent lifetime productivity or longevity were significant and unfavourable but low for Polish Landrace population

Download Full-text

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.652623 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dabin Jeong ◽

Sangsoo Lim ◽

Sangseon Lee ◽

Minsik Oh ◽

Changyun Cho ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Computational Method ◽

Transcriptional Networks ◽

Specific Gene ◽

Transcriptome Data ◽

Data Set ◽

Kernel Canonical Correlation Analysis ◽

Kernel Cca

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

Download Full-text

Compositional Canonical Correlation Analysis

10.1101/144584 ◽

2017 ◽

Author(s):

Jan Graffelman ◽

Vera Pawlowsky-Glahn ◽

Juan José Egozcue ◽

Antonella Buccianti

Keyword(s):

Trace Elements ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Covariance Matrices ◽

Generalized Inverses ◽

Geological Data ◽

Data Set ◽

X Ray ◽

Log Ratio

AbstractThe study of the relationships between two compositions by means of canonical correlation analysis is addressed A coimnositional version of canonical correlation analysis is developed. and called CODA-CCO. We consider two approaches, using the centred log-ratio transformation and the calculation of all possible pairwise log-ratios within sets. The relationships between both approaches are pointed out, and their merits are discussed. The related covariance matrices are structurally singular, and this is efficiently dealt with by using generalized inverses. We develop compositional canonical biplots and detail their properties. The canonical biplots are shown to be powerful tools for discovering the most salient relationships between two compositions. Some guidelines for compositional canonical biplots construction are discussed. A geological data set with X-ray fluorescence spectrometry measurements on major oxides and trace elements is used to illustrate the proposed method. The relationships between an analysis based on centred log-ratios and on isometric log-ratios are also shown.

Download Full-text

ONLINE REMOVAL OF EYE BLINK ARTIFACT FROM SCALP EEG USING CANONICAL CORRELATION ANALYSIS BASED METHOD

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519412500911 ◽

2012 ◽

Vol 12 (05) ◽

pp. 1250091 ◽

Cited By ~ 6

Author(s):

LI ZHANG ◽

YUDING WANG ◽

CHUANHONG HE

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Synthetic Data ◽

Data Set ◽

Eye Blink ◽

Scalp Eeg ◽

Online Test ◽

Comparable Performance ◽

Blink Artifact

Eye blink artifact, the main contamination in electroencephalography (EEG), brings serious problems for the analysis of EEG data. In this paper, an online method for eye blink artifact removal is presented. Canonical correlation analysis (CCA) is used to decompose the recorded signals containing several-channel EEG and one-channel vertical electrooculography (EOG). The identification of the artifactual component is fully automatically implemented based on evaluating the similarity between the reference EOG and decomposed CCA components. This method was compared with an independent component analysis based technique on a synthetic data set and achieved comparable performance for removing eye blink artifact. Moreover, the CCA based method is less time-consuming. The proposed method was finally implemented with Labview for removing eye blink artifact in online test. The online experiment results show that the proposed method could fulfill the identification and suppression of eye blink artifact from contaminated EEG in real-time.

Download Full-text

Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis

Journal of Applied Mathematics ◽

10.1155/2013/401976 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Xun Chen ◽

Aiping Liu ◽

Z. Jane Wang ◽

Hu Peng

Keyword(s):

Correlation Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Medical Knowledge ◽

Data Sets ◽

Data Set ◽

Activity Modeling ◽

Multiple Data Sets

Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposed method takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG and EMG data collected in a Parkinson’s disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis.

Download Full-text

Accounting for Variance Shared by Measures of Personality and Stress-Related Variables: A Canonical Correlation Analysis

Psychological Reports ◽

10.2466/pr0.1995.76.3.959 ◽

1995 ◽

Vol 76 (3) ◽

pp. 959-962 ◽

Cited By ~ 8

Author(s):

Janette Jelinek ◽

Martin E. Morf

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Psychology Students ◽

Sampling Errors ◽

Data Set ◽

Neo Personality Inventory ◽

Ways Of Coping Questionnaire ◽

Multivariate Procedures ◽

Two Measures

Correlations were computed among the five personality scales of the NEO Personality Inventory, two measures derived from the Hassles Scale, and eight ways of dealing with stress measured by the Ways of Coping Questionnaire. Subjects were 66 undergraduate psychology students. Canonical correlation analysis suggests that multivariate procedures treating the data set as a whole can detect underlying patterns obscured by large sampling errors at lower levels of analysis.

Download Full-text

A PCA–CCA network for RGB-D object recognition

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417752820 ◽

2018 ◽

Vol 15 (1) ◽

pp. 172988141775282 ◽

Cited By ~ 6

Author(s):

Shiying Sun ◽

Ning An ◽

Xiaoguang Zhao ◽

Min Tan

Keyword(s):

Principal Component Analysis ◽

Object Recognition ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Principal Component ◽

Component Analysis ◽

Depth Information ◽

Processing Unit ◽

Data Set

Object recognition is one of the essential issues in computer vision and robotics. Recently, deep learning methods have achieved excellent performance in red-green-blue (RGB) object recognition. However, the introduction of depth information presents a new challenge: How can we exploit this RGB-D data to characterize an object more adequately? In this article, we propose a principal component analysis–canonical correlation analysis network for RGB-D object recognition. In this new method, two stages of cascaded filter layers are constructed and followed by binary hashing and block histograms. In the first layer, the network separately learns principal component analysis filters for RGB and depth. Then, in the second layer, canonical correlation analysis filters are learned jointly using the two modalities. In this way, the different characteristics of the RGB and depth modalities are considered by our network as well as the characteristics of the correlation between the two modalities. Experimental results on the most widely used RGB-D object data set show that the proposed method achieves an accuracy which is comparable to state-of-the-art methods. Moreover, our method has a simpler structure and is efficient even without graphics processing unit acceleration.

Download Full-text

Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis

10.1101/2020.04.16.20031492 ◽

2020 ◽

Author(s):

Lluís Revilla ◽

Aida Mayorgas ◽

Ana Maria Corraliza ◽

Maria C. Masamunt ◽

Amira Metwaly ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Complex Disease ◽

Data Set ◽

Relevant Variables ◽

Generalized Canonical Correlation Analysis ◽

Average Variance

AbstractBackgroundPersonalized medicine requires finding relationships between variables that influence a patient’s phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis.AimTo develop a method to find the relationships between microbiome and transcriptome data and the relevant clinical variables in a complex disease, such as Crohn’s disease.ResultsWe present here a method to identify interactions based on canonical correlation analysis. Our main contribution is to show that the model is the most important factor to identify relationships between blocks. Analysis were conducted on three independent datasets: a glioma, Crohn’s disease and a pouchitis data set. We describe how to select the optimum hyperparameters on the glioma dataset. Using such hyperparameters on the Crohn’s disease data set, our analysis revealed the best model for identifying relationships between transcriptome, gut microbiome and clinically relevant variables. With the pouchitis data set our analysis revealed that adding the clinically relevant variables improves the average variance explained by the model.ConclusionsThe methodology described herein provides a framework for identifying interactions between sets of (omic) data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.

Download Full-text

Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022028 ◽

2021 ◽

Vol 19 (1) ◽

pp. 624-642

Author(s):

Hongming Liu ◽

◽

Yunyuan Gao ◽

Jianhai Zhang ◽

Juanjuan Zhang ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Binary Classification ◽

Response Speed ◽

Classification Method ◽

High Dimensional ◽

Data Set ◽

Locality Preserving ◽

Fusion Features

<abstract><p>Existing epileptic seizure automatic detection systems are often troubled by high-dimensional electroencephalogram (EEG) features. High-dimensional features will not only bring redundant information and noise, but also reduce the response speed of the system. In order to solve this problem, supervised locality preserving canonical correlation analysis (SLPCCA), which can effectively use both sample category information and nonlinear relationships between features, is introduced. And an epileptic signal classification method based on SLPCCA is proposed. Firstly, the power spectral density and the fluctuation index of the frequency slice wavelet transform are extracted as features from the EEG fragments. Next, SLPCCA obtains the optimal projection direction by maximizing the weight correlation between the paired samples in the class and their neighbors. And the projection combination of original features in the optimal direction is the fusion feature. The fusion features are then input into LS-SVM for training and testing. This method is verified on the Bonn dataset and the CHB-MIT dataset and gets good results. On various classification tasks of Bonn data set, the proposed method achieves an average classification accuracy of 99.16%. On the binary classification task of the inter-seizure and seizure epileptic EEG of the CHB-MIT dataset, the proposed method achieves an average accuracy of 97.18%. The experimental results show that the algorithm achieves excellent results compared with several state-of-the-art methods. In addition, the parameter sensitivity of SLPCCA and the relationship between the dimension of the fusion features and the classification results are discussed. Therefore, the stability and effectiveness of the method are further verified.</p></abstract>

Download Full-text

A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs

International Journal of Neural Systems ◽

10.1142/s0129065720500203 ◽

2020 ◽

Vol 30 (05) ◽

pp. 2050020 ◽

Cited By ~ 1

Author(s):

Qingguo Wei ◽

Shan Zhu ◽

Yijun Wang ◽

Xiaorong Gao ◽

Hai Guo ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Signal To Noise Ratio ◽

Classification Performance ◽

Training Data ◽

Data Driven ◽

Spatial Filters ◽

Data Set ◽

Practical Applications

Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.

Download Full-text