A cross-cohort study: sexual dimorphism in the relationship between brain complexity & intelligence

Poster No:

1836 

Submission Type:

Abstract Submission 

Authors:

Anca-Larisa Sandu1, Gordon Waiter1, Nafeesa Nazlee1, Tina Habota1, Chris McNeil1, Dorota Chapko2, Justin Williams3, Caroline Fall4, Giriraj Chandak5, Shailesh Pene6, Murali Krishna7, Andrew McIntosh8, Heather Whalley8, Kalyanaraman Kumaran4,9, Ghattu Krishnaveni9, Alison Murray1

Institutions:

1Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK, 2Creative Computing Institute, University of the Arts, London, UK, 3Gold Coast University Hospital, Southport QLD, Australia, 4MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK, 5Genomic Research on Complex diseases, CSIR - Centre for Cellular and Molecular Biology, Hyderabad, India, 6Department of Imaging and Interventional Radiology, Narayana Multispecialty Hospital, Mysore, India, 7Foundation for Research and Advocacy in Mental Health, Mysore, India, 8Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK, 9Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India

First Author:

Anca-Larisa Sandu, Dr  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK

Co-Author(s):

Gordon Waiter, Dr  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK
Nafeesa Nazlee, Mrs  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK
Tina Habota, Dr  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK
Chris McNeil, Dr  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK
Dorota Chapko, Dr  
Creative Computing Institute, University of the Arts
London, UK
Justin Williams, Dr  
Gold Coast University Hospital
Southport QLD, Australia
Caroline Fall, Prof  
MRC Lifecourse Epidemiology Unit, University of Southampton
Southampton, UK
Giriraj Chandak, Dr  
Genomic Research on Complex diseases, CSIR - Centre for Cellular and Molecular Biology
Hyderabad, India
Shailesh Pene, Dr  
Department of Imaging and Interventional Radiology, Narayana Multispecialty Hospital
Mysore, India
Murali Krishna, Dr  
Foundation for Research and Advocacy in Mental Health
Mysore, India
Andrew McIntosh  
Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh
Edinburgh, UK
Heather Whalley  
Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh
Edinburgh, UK
Kalyanaraman Kumaran, Dr  
MRC Lifecourse Epidemiology Unit, University of Southampton|Epidemiology Research Unit, CSI Holdsworth Memorial Hospital
Southampton, UK|Mysore, India
Ghattu Krishnaveni, Dr  
Epidemiology Research Unit, CSI Holdsworth Memorial Hospital
Mysore, India
Alison Murray, Prof  
Aberdeen Biomedical Imaging Centre, University of Aberdeen
Aberdeen, UK

Introduction:

The human brain has a complex structure with cortical folding defining gyri and sulci; the complexity of the shape can be captured using Fractal Dimension (FD)1. FD varies with age2, pathology3,4and sex5. In this study we analysed sex differences in the relationship between brain structural complexity determined from magnetic resonance images (MRI) and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian).

Methods:

We compute MRI derived structural brain complexity (FD) for an Indian cohort (age 20-22 y), and two in the UK: one from Generation Scotland (60-66 y) and UK Biobank (45-79 y). Included in this study are 80 participants (43 males) from the Mysore Parthenon Cohort (MPC)6, from Mysore, South India; 238 participants (122 males) from the Aberdeen Children of the 1950s (ACONF) cohort7, Scotland; and 6659 participants (3154 males) from the January 2017 data realise of UK Biobank8.
The participants also have contemporaneous cognitive data from a battery of culturally validated tests administered at the time of acquisition of MRI. The tests administrated in Mysore are WAIS-IV (India) and contain 10 subtests (Block Design, Similarities, Digit Span, Matrix Reasoning, Vocabulary, Arithmetic, Symbol Search, Visual Puzzle, Information, Coding), which measure crystallised and fluid intelligence, and short- and long-term memory. Six cognitive tests were collected in Aberdeen (Verbal fluency, Mill Hill Vocabulary, Logical memory – immediate recall, Logical memory – delayed recall, Digit symbol and Matrix reasoning). UK Biobank participants were administered five tests that measured fluid intelligence: numeric memory, verbal-numeric reasoning, reaction time, visual memory and prospective memory. The principal component analysis was used to calculate the first unrotated principal component g from each battery of cognitive tests on each occasion and standardised in an IQ -like score called general intelligence g.
A brain mask was extracted from MRI data collected using FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) for each individual. FD was computed using the box-counting method applied to the whole brain mask using an in-house written software in Matlab1,9.

Results:

Pearson (bivariate) correlations between whole brain complexity and general intelligence g were performed across all participants and for both sexes individually. Whole brain complexity shows significant correlations with the general intelligence g in women case in all cohorts (in MPC r(36)=.396, p=.015, in ACONF r(121)=.270, p=.003; in UKBiobank r(3504)=.153, p<.001). There are significant differences between the two correlations corresponding to men and women and also in their corresponding slopes, in ACONF and UK Biobank.
The two-way ANOVA was used to evaluate if there is any significant interaction effect of sex* brain complexity when the dependent variable is general intelligence g and we found: 1) in the ACONF cohort, interaction effect F(1,234)=3.935, p=0.048, partial η2 =.017 and estimated power=.506 and 2) in UKBiobank, interaction effect F(1,6654)=6.238, p=.013, partial η2=.001 and estimated power=.704. The age was added as a covariate.

Conclusions:

A more complex cortical shape is associated with higher intelligence. Here we demonstrate that the relationship between structural brain complexity and cognitive ability is strongest in women and is consistent across populations of different age ranges and geographical locations. This stresses once more the sex-specific differences in brain complexity and the importance of considering sex as a contributing variable in the analysis of brain-related data. Neurobiological sex-differences can provide a clue in understanding neurodevelopmental and neurodegenerative aspects, which can evolve differently as a function of sex.

Higher Cognitive Functions:

Higher Cognitive Functions Other 1

Learning and Memory:

Long-Term Memory (Episodic and Semantic)

Lifespan Development:

Early life, Adolescence, Aging

Modeling and Analysis Methods:

Methods Development 2

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Neuroanatomy Other

Keywords:

Cognition
Computing
Cortex
Sexual Dimorphism
STRUCTURAL MRI

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Other

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Structural MRI
Neuropsychological testing

For human MRI, what field strength scanner do you use?

1.5T
3.0T

Which processing packages did you use for your study?

Free Surfer

Provide references using author date format

1. Sandu, A.-L., Rasmussen Jr., I.-A., Lundervold, A., et al. 'Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia', Computerized Medical Imaging and Graphics 2008;32(2):150-158. doi: 10.1016/j.compmedimag.2007.10.005.
2. Madan, C. R., & Kensinger, E. A. 'Cortical complexity as a measure of age-related brain atrophy', NeuroImage 2016;34:617-629. doi:10.1016/j.neuroimage.2016.04.029
3. King, R. D., Brown, B., Hwang, M., et al. 'Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease', NeuroImage 2010;53(2):471-479. doi:10.1016/j.neuroimage.2010.06.050
4. Sandu, A.L., Paillère Martinot, M.L., Artiges, E., et al. '1910s' brains revisited. Cortical complexity in early 20th century patients with intellectual disability or with dementia praecox', Acta Psychiatrica Scandinavica 2014;130(3):227-37. doi:10.1111/acps.12243
5. Luders, E., Narr, K. L., Thompson, P. M., et al. 'Gender differences in cortical complexity', Nature Neuroscience 2004;7(8):799-800. doi:10.1038/nn1277
6. Krishnaveni, G.V., Veena, S.R., Hill, J.C et al. 'Cohort Profile: Mysore Parthenon Birth Cohort', International Journal of Epidemiology 2015;44(1):28-36. doi: 10.1093/ije/dyu050.
7. Habota, T., Sandu, A.L., Waiter G.D. et al. 'Cohort profile for the STratifying Resilience and Depression Longitudinally (STRADL) study: A depression-focused investigation of Generation Scotland, using detailed clinical, cognitive, and neuroimaging assessments', Wellcome Open Research 2019;4:185. doi.org/10.12688/wellcomeopenres.15538.1
8. Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., et al. 'Multimodal population brain imaging in the UK biobank prospective epidemiological study', Nature Neuroscience 2016;19(11):1523-1536. doi:10.1038/nn.4393
9. Sandu, A.L., Staff, R.T., McNeil, C.J., et al. 'Structural brain complexity and cognitive decline in late life - a longitudinal study in the Aberdeen 1936 Birth Cohort', NeuroImage 2014;100:558-63. doi:10.1016/j.neuroimage.2014.06.054.