Neuroanatomical correlates of intellectual ability across the lifespan

By Suzanne Goh, Ravi Bansal, Dongrong Xu, Xuejun Hao, Jun Liu, Bradley S. Peterson

Anchor Study

Aim/Goal

To determine the correlation of cortical thickness in the cerebrum of the human brain with FSIQ (Full Scale Intelligence quotient) and determine the moderating effects of sex and age.

To use the results of the cortical findings to determine whether they were supported by differences in white matter volume.

http://tinyurl.com/62tqytz

http://tinyurl.com/64hzge4

Participants and Sample Size

• The participants of this cross-sectional study included 105 healthy people (no current or past psychiatric/neurological disorder) selected randomly from a telemarketing database. • Mean age - 17.7 years •Age range 7-57 years (children < 18years = 62%)• FSIQ of 70 and higher• Gender Females - 45.7%, Males - 54.3%

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Methodology/Research Design

Neuropsychological Assessment –Intelligence Testing

Different instruments were used based on research study protocol. All instruments tested similar domain of cognitive function. Intelligence testing was done on the same day of Magnetic Resonance

Imaging (MRI) or within 1 month.

Wechsler Intelligence scales, age determined which version was used. -For 51 children - Wechsler Intelligence Scale for Children (WISC-III) -For 34 adults - Wechsler Adult Intelligence Scale revised (WAIS-R) -For 3 children & 6 adults - Wechsler Abbreviated Scale of

Intelligence(WASI) Kaufmann Brief Test of Intelligence (K-BIT) -For 11 children

Image Acquisition and ProcessingMR Image Acquisition

- 1.5 Tesla Scanner was used to acquire anatomical image. - Cantho-meatal landmarks used to standardized head positioning. - 3-dimensional spoiled gradient recall sequence used to acquire image. http://tinyurl.com/3gtl9o9

Image Acquisition and Processing MR Image Processing

Brain and non-brain tissues were separated using an automated tool and manual editing.

Cortical gray matter was isolated using representative values of gray and white matter together with 3 orthogonal views on Sun Ultra 10 workstation (ANALYZE 7.5 software).

The gray scale values of pure representations of cortical gray and white matter was sampled bilaterally in 4 locations using 8x8 pixel array.

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Image Acquisition and Processing MR Image Processing continued

Average calculated for each tissue type to reduce bias. Mean threshhold value used to provide rough classification of gray and

white matter throughout the cerebrum. This classification was hand-edited in 3 orthogonal views to remove

subcortical gray matter and rims of the ventricles. White matter was isolated. Images were flipped to remove bias, then corrected.

Image Acquisition and Processing Template Selection – To select the most appropriate template

Selected template brain of participant that most demographically represented all healthy controls. Average brain is not used as template in order to ensure accuracy.

Brains for all other participants were coregistered to template brain using a rigid body similarity transformation.

Coregistered brains were then nonlinearly transformed to the template brain using a high-dimensional, non-rigid warping algorithm based on fluid dynamics.

Each brain now the same shape and size as template brain, permitted identification of points on all participants’ cerebral surfaces to compare with corresponding points on template.

Distance was computed between points on brain surfaces using least square analysis.

The cerebrum for which all points across its surface was closest to the average computed distance was reselected as the template brain.

Processes were repeated.

Image Acquisition and Processing Calculation of cortical thickness

Coregistered brain - cortical mantel 3-dimensional morphological operator to distance transform the brain

without the cortex from coregistered brain. Cortical thickness was calculated as the smallest distance of each point on

the external cortical surface from the outermost surface of the white matter in the coregistered brain.

Calculation of cortical thickness was controlled for whole brain volume.

http://tinyurl.com/64hzge4

Image Acquisition and Processing Volume-preserved warping (VPW)

VPW warped a binarized image of the brain to form a spatially normalized image that has varying pixel intensities.

Voxel-wise statistical analyses performed to detect regions of significant correlation between local brain volume and FSIQ.

Results

Cortical thickness and FSIQ

In localized regions of the prefrontal cortices ( left ventromedial and right dorsolateral prefrontal cortices), an inverse correlation between cortical thickness and FSIQ was found. Specific areas include left anterior cingulate gyrus, left orbitofrontal cortex, left subgenal gyrus and right middle frontal gyrus.

http://tinyurl.com/3vvobnk

http://www.psych-it.com.au/Psychlopedia/article.asp?id=191

Results continuedCortical thickness and FSIQGender and FSIQ Gender-modified correlations in many regions but primarily in the frontal regions. These

include the Broca’s area, left cingulate cortex and right medial prefrontal gyrus. Correlation for cortical thickness differed for males and females in prefrontal regions. - Females showed a positive correlation in nearly all regions - Males showed an inverse correlation. Exception – left anterior cingulate cortex showed a positive correlation for males and

negative for females.

http://tinyurl.com/3woz5dghttp://tinyurl.com/67a5c25

Results continuedWhite matter and FSIQ A significant FSIQ-by-age interaction was detected in the right frontal

white matter volume and in the white matter underlying the dorsolateral prefrontal cortex in adults but not in children. An inverse correlation was found, that increased with age.

In the left periventricular matter of adults a positive correlation was detected between FSIQ and white matter volume.

Observation due to fibers of the superior corona radiata, superior longitudinal fasciculus and anterior portion of the corpus callosum.

No FSIQ-by-sex correlation observed.

Total Brain Volume and FSIQ FSIQ correlated positively with total brain volume

Implication of Results The inverse correlation between cortical thickness and FSIQ in the prefrontal cortex

could be as a consequence of -

The inverse correlation between white matter volume and FSIQ, means that smaller volumes of white matter influenced higher FSIQ in adults.

The findings from gray and white matter volume therefore further support the idea that synaptic pruning accounts for the inter-intellectual differences in adults.

Correlation of cortical thickness with FSIQ in the Broca’s area in females possibly reflects why females have an advantage over males for verbal fluency.

Inter-individual differences in intellectual ability observed in adults

Synaptic pruning/ increase myelin coating of fibers in lower cortical areas, leading to cortical thinning +

increase in regional volume

Late maturation of prefrontal cortex

Parieto-Frontal Integration Theory of Intelligence

The findings of this study overlap with findings from other studies to indicate that the frontal and parietal regions compose the primary neurological substrate for “g” , general intelligence as well as the necessary skills to perform many cognitive functions.

This theory states that the frontal and parietal lobes in the brain are the primary areas involve in intelligence. (http://www.science20.com/news_account/parieto_frontal_integration_theory_p_fit_a_neural_basis_of_intelligence)

Anchor study findings therefore support the Parieto-Frontal Integration Theory of Intelligence:

- inverse correlation between cortical thickness and intelligence - gender specific patterns correlated with intelligence - cortical and white matter are involved in intelligence

Discussion Questions1) How reflective is the anchor study “ Neuroanatomical correlates of

intellectual ability across the lifespan, of Gardner’s, “Myths and realities of multiple intelligence”?.

2) Gardner wrote, “I would recommend that any intelligence be assessed by a number of complementary approaches that consider the core components of an intelligence.” Analyzing this statement, how would you suggest the implementation of assessment based on Gardner’s Multiple Intelligence?

3) In the anchor text, the effects of age on intelligence was studied. How then does the theory nature vs. nurture impact intelligence across lifespan?