Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

Beaton, Derek; Filbey, Francesca M.; Abdi, Hervé

doi:10.1007/978-1-4614-8283-3_4

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…For example, Meda et al (34) derived a five-factor model based on state and trait measures in healthy controls vs. “at-risk/addicted” participants. Huba, Newcomb, and Bentler (66) used Interbattery Factor Analysis (67) – a technique also known more recently as partial least squares correlation (68,69) – to examine the relationship between sensation-seeking and drug use in an adolescent population. Huba et al (66) identified factors common to types of drugs used and sensation-seeking traits.…”

Section: Discussionmentioning

confidence: 99%

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

Beaton

Abdi

Filbey

2014

The American Journal of Drug and Alcohol Abuse

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Background Impulsivity is a complex trait often studied in substance abuse and overeating disorders, but the exact nature of impulsivity traits and their contribution to these disorders are still debated. Thus, understanding how to measure impulsivity is essential for comprehending addictive behaviors. Objectives Identify unique impulsivity traits specific to substance use and overeating. Methods Impulsive Sensation Seeking (ImpSS) and Barratt’s Impulsivity scales (BIS) Scales were analyzed with a non-parametric factor analytic technique (discriminant correspondence analysis) to identify group-specific traits on 297 individuals from five groups: Marijuana (n = 88), Nicotine (n = 82), Overeaters (n = 27), Marijuauna + Nicotine (n = 63), and Controls (n = 37). Results A significant overall factor structure revealed three components of impulsivity that explained respectively 50.19% (pperm<0.0005), 24.18% (pperm<0.0005), and 15.98% (pperm<0.0005) of the variance. All groups were significantly different from one another. When analyzed together, the BIS and ImpSS produce a multi-factorial structure that identified the impulsivity traits specific to these groups. The group specific traits are (1) Control: low impulse, avoids thrill-seeking behaviors; (2) Marijuana: seeks mild sensation, is focused and attentive; (3) Marijuana + Nicotine: pursues thrill-seeking, lacks focus and attention; (4) Nicotine: lacks focus and planning; (5) Overeating: lacks focus, but plans (short and long term). Conclusions Our results reveal impulsivity traits specific to each group. This may provide better criteria to define spectrums and trajectories – instead of categories – of symptoms for substance use and eating disorders. Defining symptomatic spectrums could be an important step forward in diagnostic strategies.

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Section: Discussionmentioning

confidence: 99%

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

Beaton

Abdi

Filbey

2014

The American Journal of Drug and Alcohol Abuse

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“…Then, each SNP was coded in accordance with genotypic (codominant) models 37,75 : each SNP was coded into 3 genotypes (e.g., AA, AG, or GG) as categorical variables, and each variable was weighted according to the information it provided. The weight of a variable was defined as the inverse of its relative frequency because a rare variable provides more information than does a frequent variable 37,75 . This fully categorical coding scheme allows us to detects linear and nonlinear relationships within (e.g., genotypic) and between datasets 37 .…”

Section: Mri Data Processing Structural Mrimentioning

confidence: 99%

COMT-Polymorphisms Modulated Functional Profile of the Fusiform Face Area Contributes to Face-Specific Recognition Ability

Zhen

Huang

et al. 2020

Sci Rep

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Previous studies have shown that face-specific recognition ability (FRA) is heritable; however, the neural basis of this heritability is unclear. Candidate gene studies have suggested that the catechol-Omethyltransferase (COMT) rs4680 polymorphism is related to face perception. Here, using a partial least squares (PLS) method, we examined the multivariate association between 12 genotypes of 4 COMT polymorphisms (rs6269-rs4633-rs4818-rs4680) and multimodal MRI phenotypes in the human fusiform face area (FFA), which selectively responds to face stimuli, in 338 Han Chinese adults (mean age 20.45 years; 135 males). The MRI phenotypes included gray matter volume (GMV), resting-state fractional amplitude of low-frequency fluctuations (fALFF), and face-selective blood-oxygen-level-dependent (BOLD) responses (FS). We found that the first COMT-variant component (PLS1) was positively associated with the FS but negatively associated with the fALFF in the FFA. Moreover, participants with the COMT heterozygous-HEA-haplotype showed higher PLS1 FFA-MRI scores, which were positively associated with the FRA in an old/new face recognition task, than those with the COMT homozygous HEA haplotype and HEA non-carriers, suggesting that individuals with an appropriate (intermediate) level of dopamine activity in the FFA might have better FRA. In summary, our study provides empirical evidence for the genetic and neural basis for the heritability of face recognition and informs the formation of neural module functional specificity.Face recognition is a highly developed skill in humans that shows considerable variability between individuals 1-5 . Previous behavioral genetic studies have shown that face recognition ability is heritable 4,6 ; however, the neural basis for this heritability is unclear. One candidate cortical region underlying inter-individual variability in face recognition is the human fusiform face area (FFA) especially in the right hemisphere, which shows more significant responses to visual face stimuli than to other non-face stimuli 7 and represents a critical hub in the face network 8-10 . More importantly, features of the FFA are associated with face-specific recognition ability (FRA) 2,3 . However, information about the effects of genetic variability on FFA structure and function and their relationship with face recognition ability remain limited.Previous twin studies have demonstrated the higher intra-pair similarity of cortical structure 11 and functional magnetic resonance imaging (fMRI) responses 11,12 in the occipitotemporal cortex encompassing the FFA in monozygotic twins than dizygotic twins. Additionally, neural responses to facial expressions are modulated by common genetic variance in a set of regions constituting the face network that include the FFA 13 . These studies suggest that the FFA may be modulated by genetic variations. However, previous studies have not specifically localized the FFA, and it is unclear whether the observed genetic effects are derived from changes in the FFA or other regions. Notab...

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“…However, SNPs and many types of behavioral data (e.g., surveys, clinical assessments, and diagnostic groups) are inherently categorical. We now present a new PLSC method—called partial least squares correspondence analysis (PLSCA)—designed specifically to analyze two tables of categorical data (Beaton, Filbey, et al, 2013). We have implemented PLSCA (and several of its derivatives) in the R package TExPosition (Beaton, Chin Fatt, & Abdi, 2014; Beaton, Rieck, Fatt, & Abdi, 2013).…”

Section: A Précis Of Plscmentioning

confidence: 99%

“…Here, we present partial least squares correspondence analysis (PLSCA; Beaton, Filbey, & Abdi, 2013)-a derivative of PLS-and several of its novel extensions tailored for the particular data and design issues often confronted in genetic association studies within the psychological, cognitive, and neurological sciences. PLSCA (like traditional PLS) is designed to simultaneously analyze two tables of data.…”

Section: Snp Coding Problem and Solutionmentioning

confidence: 99%

Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data.

Beaton

Dunlop

Abdi

2016

Psychological Methods

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For nearly a century, detecting the genetic contributions to cognitive and behavioral phenomena has been a core interest for psychological research. Recently, this interest has been reinvigorated by the availability of genotyping technologies (e.g., microarrays) that provide new genetic data, such as single nucleotide polymorphisms (SNPs). These SNPs-which represent pairs of nucleotide letters (e.g., AA, AG, or GG) found at specific positions on human chromosomes-are best considered as categorical variables, but this coding scheme can make difficult the multivariate analysis of their relationships with behavioral measurements, because most multivariate techniques developed for the analysis between sets of variables are designed for quantitative variables. To palliate this problem, we present a generalization of partial least squares-a technique used to extract the information common to 2 different data tables measured on the same observations-called partial least squares correspondence analysis-that is specifically tailored for the analysis of categorical and mixed ("heterogeneous") data types. Here, we formally define and illustrate-in a tutorial format-how partial least squares correspondence analysis extends to various types of data and design problems that are particularly relevant for psychological research that include genetic data. We illustrate partial least squares correspondence analysis with genetic, behavioral, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative. R code is available on the Comprehensive R Archive Network and via the authors' websites. (PsycINFO Database Record

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Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

Cited by 4 publications

References 25 publications

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

COMT-Polymorphisms Modulated Functional Profile of the Fusiform Face Area Contributes to Face-Specific Recognition Ability

Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data.

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