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iq_brain_size

iq_brain_size

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Author: Source: Unknown - Date unknown Please cite: 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

9 features

FIQ (target)numeric15 unique values
0 missing
CCMIDSAnumeric18 unique values
0 missing
HCnumeric15 unique values
0 missing
ORDERnominal2 unique values
0 missing
PAIRnominal10 unique values
0 missing
SEXnominal2 unique values
0 missing
TOTSAnumeric20 unique values
0 missing
TOTVOLnumeric19 unique values
0 missing
WEIGHTnumeric18 unique values
0 missing
Show all 9 features

107 properties

NumberOfInstances
20
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
6
Number of numeric attributes.
NumberOfSymbolicFeatures
3
Number of nominal attributes.
AutoCorrelation
-7.32
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.45
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.6
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1906.28
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.26
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
174.83
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.02
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
545.69
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
4.67
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.65
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
55.96
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.1
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
6.99
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.47
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.91
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
22.22
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
66.67
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
33.33
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.76
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
43.84
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.15
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.6
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
89.4
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.79
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
16.63
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.94
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
1321.03
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.18
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
137.41
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
4.62
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on iq_brain_size
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: FIQ
Supervised Regression on iq_brain_size
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: FIQ
Clustering on iq_brain_size
0 runs
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
Clustering on iq_brain_size
0 runs - estimation_procedure: 50 times Clustering
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