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iq_brain_size

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Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren

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Relationship between IQ and Brain Size
Summary:
Monozygotic twins share numerous physical, psychological, and pathological traits. Recent advances in in vivo brain image acquisition and analysis have made it possible to determine quantitatively whether: 1) twins share neuroanatomical traits; and 2) neuroanatomical measures correlate with brain size.
Using magnetic resonance imaging and computer-based image analysis techniques, measurements of the volume of the forebrain, the surface area of the cerebral cortex and the mid-sagittal area of the corpus callosum were obtained in 10 pairs of monozygotic twins. Head circumference, body weight, and Full-Scale IQ were also measured. Analyses of variance were carried out using genotype, birth order, and sex, as between-subject factors. Pearson correlation coefficients were computed to assess the interrelationships between brain measures, head circumference, and IQ.
Effects of genotype (but not of birth order) were found for total forebrain volume, total cortical surface area, and callosal area. Consistent with previous twin studies, highly significant effects of genotype but not birth order were also found for head circumference, body weight, and Full-Scale IQ. The significant effect of genotype on all measures was not attributable to sex differences across unrelated twin pairs. Significant correlations were observed between forebrain volume, cortical surface area, and callosal area as well as between each brain measure and head circumference. No correlation between IQ and any other measure was found.
Monozygotic twins share similarities in forebrain volume, cortical surface area, and callosal area. Brain measures are highly correlated with one another and with head circumference, but none is correlated with IQ.
Authorization: Contact Authors
Reference:
Tramo MJ, Loftus WC, Green RL, Stukel TA, Weaver JB, Gazzaniga MS. Brain Size, Head Size, and IQ in Monozygotic Twins. Neurology 1998; 50:1246-1252.
Description: This datafile contains 20 observations (10 pairs of twins) on 9 variables. This data set can be used to demonstrate simple linear regression and correlation.
Variable Names in order from left to right:
CCMIDSA: Corpus Collasum Surface Area (cm2)
FIQ: Full-Scale IQ
HC: Head Circumference (cm)
ORDER: Birth Order
PAIR: Pair ID (Genotype)
SEX: Sex (1=Male 2=Female)
TOTSA: Total Surface Area (cm2)
TOTVOL: Total Brain Volume (cm3)
WEIGHT: Body Weight (kg)
Therese Stukel
Dartmouth Hitchcock Medical Center
One Medical Center Dr.
Lebanon, NH 03756
e-mail: stukel@dartmouth.edu
Information about the dataset
CLASSTYPE: numeric
CLASSINDEX: 2

FIQ (target) | numeric | 15 unique values 0 missing | |

CCMIDSA | numeric | 18 unique values 0 missing | |

HC | numeric | 15 unique values 0 missing | |

ORDER | nominal | 2 unique values 0 missing | |

PAIR | nominal | 10 unique values 0 missing | |

SEX | nominal | 2 unique values 0 missing | |

TOTSA | numeric | 20 unique values 0 missing | |

TOTVOL | numeric | 19 unique values 0 missing | |

WEIGHT | numeric | 18 unique values 0 missing |

Third quartile of mutual information between the nominal attributes and the target attribute.

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

1.18

Third quartile of skewness among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

137.41

Third quartile of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

-0.76

First quartile of kurtosis among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

Average mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

First quartile of mutual information between the nominal attributes and the target attribute.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

4.62

Standard deviation of the number of distinct values among attributes of the nominal type.

4.67

Average number of distinct values among the attributes of the nominal type.

0.15

First quartile of skewness among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

1.6

First quartile of standard deviation of attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

-0.35

Second quartile (Median) of kurtosis among attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

89.4

Second quartile (Median) of means among attributes of the numeric type.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

Minimal mutual information between the nominal attributes and the target attribute.

0.79

Second quartile (Median) of skewness among attributes of the numeric type.

16.63

Second quartile (Median) of standard deviation of attributes of the numeric type.

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

10

The maximum number of distinct values among attributes of the nominal type.

0.94

Third quartile of kurtosis among attributes of the numeric type.

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2