Intelligence Quotient Data Research Articles

Mathematics Achievement - Intelligence Quotient (IQ): A Study of Simple Relations in Class 10 High School Students

This study aims to test whether students' mathematics achievement affects their Intelligence Quotient levels based on the correlation and regression. The sample of the study consisted of 81 new students from one Madrasah Aliyah in Surakarta City. The learning achievement data and IQ data were obtained from school documents. This study used a quantitative approach with a survey design. Data were analyzed using descriptive statistical analysis and inferential statistics covering prerequisite tests in the form of normality tests and linearity tests, classical assumption tests in the form of multicollinearity tests and heteroscedasticity tests, and hypothesis tests in the form of simple regression analysis. The results showed that the mean score of students' mathematics achievement was 45.78 with the smallest score of 8 and the highest score of 92. While the mean score of student's IQ was 112.43 with the lowest score of 100 and the highest score of 126. The test results showed that students' mathematics achievement affected their IQ level with the regression equation of Y=107.739+0.103X indicating that for every 1% addition of math achievement scores, the IQ score will increase by 0.103. Therefore, it can be said that when students' mathematics achievement increases, their IQ will also increase. The influence of students' mathematics achievement on IQ is 16% with a correlation value of 0.4.

Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN.

AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.

Relationship between growth and intelligence quotient in children with Down syndrome.

The relationship between intelligence quotient (IQ) and somatic development, especially growth, has been demonstrated in various groups of children. Down syndrome (DS) is characterized by short stature, overweight, and cognitive impairment. The objective of our work was to assess whether anthropometric measurements [weight, height, body mass index (BMI)] of children with DS correlate with their IQ. The results of the study may be valuable for this population in the light of increasing access to growth hormone therapy (GHT) in various genetic syndromes with short stature. Based on previous studies on children, we hypothesized that a link exists between IQ and somatic development, particularly growth. This cross-sectional study included 40 children with DS, who were aged 9-18 years. The studied population was selected from the registry of the Genetic Clinic at the University Clinical Center in Gdańsk (Poland). Anthropometric measurements (weight and height) were taken for all the children, and their BMI was determined using these data. The obtained results were plotted on charts for children with DS. The IQ of the children was assessed using the Stanford Binet Intelligence Scale, Fifth Edition. The correlations between IQ and anthropometric data were analyzed using univariate correlation and multiple regression analyses. The results showed that full-scale, verbal, and nonverbal IQ correlated with height percentile (P=0.03, P=0.02, and P=0.04, respectively), but not with weight (P=0.26, P=0.19, and P=0.61, respectively) or BMI (P=0.6, P=0.5, and P=0.72, respectively). In multiple linear regression analysis, height percentile remained as an independent determinant of the IQ results after adjusting for birth weight, hypothyroidism with L-thyroxine replacement therapy, and congenital cardiac defect (β=0.48, P=0.018). The results of our study suggest an association between growth and IQ in children with DS. The presented findings may be valuable for improving access to GHT for populations with genetic syndromes characterized by short stature. However, these should be confirmed by further research with a longitudinal sample of children with DS.

The temporal stability of premorbid intelligence: A non-clinical 10-year follow-up study using the Irregular Word Reading Test (TeLPI)

Introduction The Cognitive Reserve (CR) describes the brain's ability to actively cope with neurological damage, enabling the maintenance of premorbid cognitive functioning through compensatory processes. The most common way to estimate CR is through formal education, the intelligence quotient (IQ) and participation in cognitive stimulating activities. In the absence of IQ data, the Irregular Word Reading Test (TeLPI) allows you to estimate the premorbid intelligence. Objective The comparison of the TeLPI results between two times of assessment (baseline and re-assessment) with an interval time (IT) of 9 years. To analyze of the stability of their results as a valid dimension for the CR estimation. Results The TeLPI presented temporal stability of its results between the two evaluation times (IT = 9.07 ± 1.02). The sample, composed by 63 cognitively healthy participants, showed no differences for the estimated Full Scale IQ (t(62) = 0.49, p = .63), for the Estimated Verbal IQ (t(62) = 0.71, p = .48) and for the estimated Performance IQ (t(62) = 0.64, p = .52). Likewise, no differences were found in the number of TeLPI errors at the two assessment times (t(62) = –0.61, p = .54). Conclusions Considering that CR is characterized as being relatively stable, the TeLPI should be included in its assessment, as an indicator with proved stability over a long period of time, on the physiological aging spectrum.

Meta-Analysis of Intelligence Quotient (IQ) in Obsessive-Compulsive Disorder

Obsessive compulsive disorder (OCD) is associated with a moderate degree of underperformance on cognitive tests, including deficient processing speed. However, despite little research focusing on Intelligence Quotient (IQ) in OCD, it has long been speculated that the disorder is associated with elevated intellectual capacity. The present meta-analytic study was, therefore, conducted to quantitatively summarize the literature on IQ in OCD systematically. We identified 98 studies containing IQ data among individuals with OCD and non-psychiatric comparison groups, and computed 108 effect sizes for Verbal IQ (VIQ, n=55), Performance IQ (PIQ, n=13), and Full Scale IQ (FSIQ, n=40). Across studies, small effect sizes were found for FSIQ and VIQ, and a moderate effect size for PIQ, exemplifying reduced IQ in OCD. However, mean IQ scores across OCD samples were in the normative range. Moderator analyses revealed no significant moderating effect across clinical and demographic indices. We conclude that, although lower than controls, OCD is associated with normative FSIQ and VIQ, and relatively lowered PIQ. These results are discussed in light of neuropsychological research in OCD, and particularly the putative impact of reduced processing speed in this population. Recommendations for utilization of IQ tests in OCD, and directions for future studies are offered.

OP9 – 2583: Phosphomanomutase deficiency (PMM2-CDG): Assessment of cerebellar dysfunction through ICARS

Objective To assess the validity of the International Cooperative Ataxia Rating Scale (ICARS) in children and adolescents with genetically confirmed PMM2-CDG deficiency. Secondary objectives: To compare ICARS results with Nijmegen paediatric CDG rating scale (NPCRS), neuroimaging findings, intelligence quotient (IQ) and molecular data. Methods Observational study including 13 PMM2-CDG patients and 21 control subjects. Ethical permission and informed consents were obtained. Three independent child neurologists rated PMM2-CDG patients with the ICARS, whereas the NPCRS was administered by a single observer. All patients underwent brain MRI and midsaggital vermis relative diameter was obtained. Psychometric evaluations were available in 6 patients. Mann-Whitney U test was used to compare ICARS between patients and controls. To evaluate inter-observer agreement in patients' ICARS, intraclass correlation coefficients (ICC) were calculated. ICARS Internal consistency was evaluated using Cronbach's alpha. Spearman's rank correlation coefficient test was used to correlate ICARS with NPCRS, midsaggital vermis relative diameter, and intelligence quotients (IQ). Results ICARS and ICARS subscores differed between patients and controls (p Conclusion ICARS is a reliable instrument for assessment of PMM2-CDG patients, without significant inter-rater variability. Despite limited sample size, results hint a good correlation between functional cerebellar assessment, IQ and neuroimaging data. ICARS may represent a valuable tool to evaluate therapeutic interventions on cerebellum dysfunction in PMM2-CDG patients.

The possible societal impact of the decrease in U.S. blood lead levels on adult IQ

BackgroundThe dramatic decrease in U.S. blood lead levels (BLLs) since the 1970s has been documented—however, the anticipated societal impact on intelligence quotient (IQ) has not. The objectives of this study were to determine whether mean IQs of American adults, adjusted for demographics, have increased in concert with society׳s decreasing BLL. MethodsMean IQs of eight normal adult cohorts (N=800), tested individually in 2007 by trained clinicians, were compared using ANCOVA and correlation analysis. Cohorts ranged in mean societal BLLs from 4 1/2μg/dL (born 1985–1987) to 19μg/dL (born 1963–1972). IQs were adjusted for confounders—education, gender, ethnicity, region, urban status. To control for age, we analyzed IQ data for a second adult sample (N=800), tested in 1995—all born when BLLs were high (1951–1975, BLL ≥15μg/dL). ResultsWhen controlling for education, gender, ethnicity, and region, the regression of IQs on BLLs was significant (r=−0.84, p<0.01); the modeled change in BLLs from 20 to 4μg/dL suggests predicted increases of 3.8 IQ points (95% CI, 1.4–6.2). Also controlling for urban status produced significance (r=−0.88, p<0.01) with predicted increases of 5.2 points (95% CI, 2.4–8.0). Control analyses ruled out aging as a confounder. ConclusionsThe dramatic societal decreases in BLLs in the U.S. since the 1970s were associated with a 4–5-point increase in the mean IQs of Americans. This effect is consistent with researchers’ predictions; however, other variables (e.g., medical advances) may have contributed to the IQ gains.

Violent offending and IQ level as predictors of suicide in schizophrenia: National cohort study

Currently there is a lack of strong epidemiological evidence for violent offending and intelligence quotient (IQ) level as predictors of suicide risk among people with schizophrenia. We interlinked several Swedish routinely collected national registers, including the Hospital Discharge, Crime, Conscript, Migration and Cause of Death Registers, to identify 13,804 patients admitted at least twice with a schizophrenia diagnosis during 1973–2004. All deaths by specific cause were ascertained and suicides identified, and rate ratios were estimated using Poisson regression models with adjustment for age and period effects. There were 871 suicides: 6.3% of the cohort. Almost 80% of these cases were aged 20–39years, and 80% occurred within 10years of second discharge, with almost a quarter taking place within a year. The following risk factors were identified: younger age, 15–19 versus 40years and older (rate ratio 3.18, 95% confidence interval 1.82–5.56), male sex (rate ratio 1.37, 1.18–1.59) and history of violent offending (rate ratio 1.45, 1.21–1.73). Intelligence quotient data were unavailable for women, but in men, lower IQ appeared protective compared to those of average or higher levels of intelligence (rate ratio 0.71, 0.58–0.86). Further stratified analyses to identify effect modification indicated that the elevated risk associated with past violent offending was restricted to males, and specifically to those with a lower IQ. Further research is needed to identify additional risk and protective factors for suicide that may be unique to people diagnosed with schizophrenia, and also other modifiers of the increased risk linked with violent offending.

Resuscitation at Birth and Cognition at 8 Years of Age: A Cohort Study

Severe hypoxic injury causing encephalopathy during the neonatal period is associated with impaired cognitive function in later life. It is unclear whether mild degrees of hypoxia that do not cause encephalopathy during the neonatal period can result in reduced intelligence quotient (IQ) scores in childhood. The aim of this cohort study was to determine whether infants who received resuscitation after birth, but had no symptoms of encephalopathy have reduced IQ scores in childhood. IQ scores in 3 groups of children at mean age of 8.6 years (SD 0.33) were measured. The groups included neonates asymptomatic for encephalopathy who needed resuscitation but had no further care (n = 815), neonates symptomatic for encephalopathy who were resuscitated and required neonatal care (n = 58), and healthy infants asymptomatic for encephalopathy who were not resuscitated, and had no further neonatal care (n = 10,609). An IQ score below 80 was defined as a low IQ. A shortened version of the Weschler intelligence scale for children (WISC-III) provided full-scale IQ data for 5887 children but only 4857 of these had complete data on resuscitation status, IQ data, and covariables. There was no data for 5529 children in the cohort. To reduce selection bias, the chained equations missing data method was used to impute missing confounder values in the 5529 nonresponders. Data were adjusted for clinical and socioeconomic potential confounders. In the final logistic regression model, infants without symptoms of encephalopathy in the neonatal period who required resuscitation were at increased risk of a low full-scale IQ score (adjusted odds ratio, 1.65; 95% confidence interval [CI], 1.13–2.43, P < 0.010). The risk of a low IQ among resuscitated infants who developed encephalopathy was almost 4 times greater (adjusted odds ratio, 6.22; 95% CI, 1.57–24.65, P < 0.009). Because the total number of resuscitated infants asymptomatic for encephalopathy was 14 times greater than that of infants with encephalopathy, the proportion of infants with low IQ scores that might be attributable to the need for resuscitation at birth was higher for asymptomatic infants (3.4%, with a 95% CI of 0.5–6.3) compared with symptomatic infants (1.2%, with a 95% of 0.2–2.2). These findings suggest that mild hypoxia or other perinatal events at birth may cause subtle brain damage that increases the risk for low IQ in childhood. Because of differences in population size, resuscitated infants without encephalopathy may account for a larger proportion of children and potentially adults with low IQ scores than that attributable to infants who develop encephalopathy.

Neural network analysis of preoperative variables and outcome in epilepsy surgery.

Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data has been successful in predicting medical outcome. The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom data on demographic, seizure, operative, and clinical variables to predict postoperative seizures were collected. Neural networks could be used to predict postoperative seizures in up to 98% of cases. Student's t-tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures: using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathological category (98%); all data excluding pathological category, intelligence quotient (IQ) data, and Wada results (84%); only demographics and tissue pathological category (65%); and only IQ data (63%). Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.

Neural network analysis of preoperative variables and outcome in epilepsy surgery

Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data, has been successful in predicting medical outcome. The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom demographic, seizure, operative, and clinical variables to predict postoperative seizures data were obtained. Neural networks were able to predict postoperative seizures in up to 98% of cases. Student's t tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathology (98%); all data excluding pathology, intelligence quotient (IQ) data, and Wada results (84%); only using demographics and tissue pathology (65%); and only using IQ data (63%). Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.