scholarly journals A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247592
Author(s):  
Heungsun Hwang ◽  
Gyeongcheol Cho ◽  
Min Jin Jin ◽  
Ji Hoon Ryoo ◽  
Younyoung Choi ◽  
...  
Keyword(s):  
Receptor Gene ◽  
Component Analysis ◽  
Brain Regions ◽  
Environmental Variable ◽  
Imaging Genetics ◽  
Cognitive Outcomes ◽  
Depression Severity ◽  
Multivariate Statistical ◽  
Generalized Structured Component Analysis ◽  
Brain Functions

With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions’ variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

scholarly journals STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA

2020 ◽  
Vol 13 (2) ◽  
pp. 136-148
Author(s):  
Epha Diana Supandi
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Work Motivation ◽  
Positive Impact ◽  
Employee Performance ◽  
Component Analysis ◽  
Equation Modeling ◽  
Multivariate Statistical ◽  
Generalized Structured Component Analysis ◽  
The Relationship

Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.

Serotonergic Genes and Suicidality

Crisis ◽  
2001 ◽  
Vol 22 (2) ◽  
pp. 54-60 ◽  
Author(s):  
Lisheng Du ◽  
Gabor Faludi ◽  
Miklos Palkovits ◽  
David Bakish ◽  
Pavel D. Hrdina
Keyword(s):  
Suicidal Ideation ◽  
Suicidal Behavior ◽  
Tryptophan Hydroxylase ◽  
Association Studies ◽  
Receptor Gene ◽  
Brain Regions ◽  
Depressive Illness ◽  
Control Group ◽  
Serotonergic Activity ◽  
Completed Suicide

Summary: Several lines of evidence indicate that abnormalities in the functioning of the central serotonergic system are involved in the pathogenesis of depressive illness and suicidal behavior. Studies have shown that the number of brain and platelet serotonin transporter binding sites are reduced in patients with depression and in suicide victims, and that the density of 5-HT2A receptors is increased in brain regions of depressed in suicide victims and in platelets of depressed suicidal patients. Genes that code for proteins, such as tryptophan hydroxylase, 5-HT transporter, and 5-HT2A receptor, involved in regulating serotonergic neurotransmission, have thus been major candidate genes for association studies of suicide and suicidal behavior. Recent studies by our group and by others have shown that genetic variations in the serotonin-system-related genes might be associated with suicidal ideation and completed suicide. We have shown that the 102 C allele in 5-HT2A receptor gene was significantly associated with suicidal ideation (χ2 = 8.5, p < .005) in depressed patients. Patients with a 102 C/C genotype had a significantly higher mean HAMD item #3 score (indication of suicidal ideation) than T/C or T/T genotype patients. Our results suggest that the 102T/C polymorphism in 5-HT2A receptor gene is primarily associated with suicidal ideation in patients with major depression and not with depression itself. We also found that the 5-HT transporter gene S/L polymorphism was significantly associated with completed suicide. The frequency of the L/L genotype in depressed suicide victims was almost double of that found in control group (48.6% vs. 26.2%). The odds ratio for the L allele was 2.1 (95% CI 1.2-3.7). The association between polymorphism in serotonergic genes and suicidality supports the hypothesis that genetic factors can modulate suicide risk by influencing serotonergic activity.

scholarly journals MENTAL ACCOUNTING DAN DAMPAKNYA TERHADAP KINERJA PERUSAHAAN MELALUI PENEMPATAN MODAL KERJA PADA USAHA KECIL DAN MENENGAH DI PULAU LOMBOK

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Siti Aisyah Hidayati
Keyword(s):  
Data Analysis ◽  
Inventory Management ◽  
Component Analysis ◽  
Mental Accounting ◽  
Company Performance ◽  
Working Capital ◽  
Probability Sampling ◽  
Generalized Structured Component Analysis ◽  
Pottery Industry ◽  
Lombok Island

This study aims at analyzing: 1)  the effect of mental accounting on placement of funds  for working capital in small- and middle-scale business, 2) the effect of  placement of funds  for working capital on company performance in small-and middle-scale business, and 3) the effect of mental accounting on company performance through placement of funds  for working capital in small- and middle-scale business.The population of this study was all the small- and middle-scale businesses in Lombok Island. Samples were collected by using judgment sampling of non probability sampling. The samples taken were the small- and middle-scale businesses of pottery industry which have exported their goods. The respondents were the managers and owners of small- and middle-scale businesses. The method of data analysis employed in this study was Generalized Structured Component Analysis.                The finding of this study showed that  mental accounting is rational by nature and have significant effects on company performance through placement of funds for working capital in small-and middle-scale business.This phenomenon is grounded from the fact that most managers and owners of those small- and middle-scale businesses are men who are on their productive age, and have more than 10 years of experience.  The placement of funds for working capital is shown by the efficiency of cash, receivable  and inventory management which in turn improve the company performance. Keywords: mental accounting, working capital, company performance

scholarly journals Multigroup Moderation Test in Generalized Structured Component Analysis

CAUCHY ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Angga Dwi Mulyanto ◽  
Solimun Solimun ◽  
Ni Wayan Surya Wardhani ◽  
Suharno Suharno
Keyword(s):  
Least Squares ◽  
Standard Error ◽  
Complex Analysis ◽  
Component Analysis ◽  
T Test ◽  
Free Software ◽  
Path Coefficient ◽  
Generalized Structured Component Analysis ◽  
Sample Size Estimate ◽  
Long Time

Generalized Structured Component Analysis (GSCA) is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.

scholarly journals Sex in the brain: hormones and sex differences

2016 ◽  
Vol 18 (4) ◽  
pp. 373-383 ◽  
Keyword(s):  
Sex Differences ◽  
Sex Hormones ◽  
Genetic Factors ◽  
Motor Coordination ◽  
Brain Regions ◽  
New Era ◽  
Brain Functions ◽  
Opioid Sensitivity ◽  
Males And Females ◽  
The Brain

Contrary to popular belief, sex hormones act throughout the entire brain of both males and females via both genomic and nongenomic receptors. Many neural and behavioral functions are affected by estrogens, including mood, cognitive function, blood pressure regulation, motor coordination, pain, and opioid sensitivity. Subtle sex differences exist for many of these functions that are developmentally programmed by hormones and by not yet precisely defined genetic factors, including the mitochondrial genome. These sex differences, and responses to sex hormones in brain regions and upon functions not previously regarded as subject to such differences, indicate that we are entering a new era in our ability to understand and appreciate the diversity of gender-related behaviors and brain functions.

scholarly journals Establishment of a Highly Sensitive Analytical Method For Measurement of Monoamine Neurotransmitters Using Mass Spectrometry

2021 ◽  
Author(s):  
Anri Hirai ◽  
Ryo Yamazaki ◽  
Atsushi Kobayashi ◽  
Takashi Kimura ◽  
Kei Nomiyama ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Body Weight ◽  
Analytical Method ◽  
Brain Regions ◽  
Liquid Chromatography Mass Spectrometry ◽  
Monoamine Neurotransmitters ◽  
Chemical Substances ◽  
Brain Functions ◽  
Spontaneous Movements ◽  
Highly Sensitive

Abstract Monoamine neurotransmitters (MAs), including dopamine (DA) and serotonin (5-HT), regulate brain functions such as behavior, memory, and learning. Neonicotinoids are pesticides that are being used more frequently. Neonicotinoid exposure has been observed to produce neurological symptoms, such as altered spontaneous movements and anxiety-like behaviors, which are suspected to be caused by altered MA levels. However, current neurotoxicity tests are not sufficiently sensitive to make these determinations. In this study, we performed some behavior tests and developed a sensitive and accurate analytical method using liquid chromatography-mass spectrometry (LC-MS/MS) to clarify the effects of neonicotinoid administration on MAs in the brain.We orally administered the neonicotinoid imidacloprid (0, 10, and 50 mg/kg body weight) to C57BL/6NCrSlc mice. In behavior tests, the decrease of activity was observed. The LC-MS/MS quantification of MAs in various brain regions by tetrafluoroborate salt of 2,4-diphenyl-pyranylium (DPP) derivatization, which was newly developed in this study, showed a decrease in some MA levels in the olfactory bulb and the striatum.Thus, in this study, we developed a new method for the sensitive detection of MAs by LC/MS using DPP derivatization. In addition, we showed that this sensitive MA quantification is effective in clarifying the neurotoxicity caused by chemical substances.

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

scholarly journals Potential Role of Neuroactive Tryptophan Metabolites in Central Fatigue: Establishment of the Fatigue Circuit

2020 ◽  
Vol 13 ◽  
pp. 117864692093627
Author(s):  
Masatoshi Yamashita
Keyword(s):  
Social Life ◽  
Potential Role ◽  
Memory Function ◽  
Dynamic Change ◽  
Central Fatigue ◽  
Brain Regions ◽  
Kynurenine Pathway ◽  
Treadmill Running ◽  
Brain Functions ◽  
Tryptophan Metabolites

Central fatigue leads to reduced ability to perform mental tasks, disrupted social life, and impaired brain functions from childhood to old age. Regarding the neurochemical mechanism, neuroactive tryptophan metabolites are thought to play key roles in central fatigue. Previous studies have supported the “tryptophan-serotonin enhancement hypothesis” in which tryptophan uptake into extensive brain regions enhances serotonin production in the rat model of exercise-induced fatigue. However, serotonin was transiently released after 30 minutes of treadmill running to exhaustion, but this did not reflect the duration of fatigue. In addition, as the vast majority of tryptophan is metabolized along the kynurenine pathway, possible involvement of the tryptophan-kynurenine pathway in the mechanism of central fatigue induction has been pointed out. More recently, our study demonstrated that uptake of tryptophan and kynurenine derived from the peripheral circulation into the brain enhances kynurenic acid production in rat brain in sleep deprivation–induced central fatigue, but without change in serotonin activity. In particular, dynamic change in glial-neuronal interactive processes within the hypothalamus-hippocampal circuit causes central fatigue. Furthermore, increased tryptophan-kynurenine pathway activity in this circuit causes reduced memory function. This indicates a major potential role for the endogenous tryptophan-kynurenine pathway in central fatigue, which supports the “tryptophan-kynurenine enhancement hypothesis.” Here, we review research on the basic neuronal mechanism underlying central fatigue induced by neuroactive tryptophan metabolites. Notably, these basic findings could contribute to our understanding of latent mental problems associated with central fatigue.

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