Individual Differences in Cognitive Abilities Correlate with Brain White Matter in Young Children

Submission No:

2066 

Submission Type:

Abstract Submission 

Authors:

Clara Ekerdt1, Clara Kühn1, Alfred Anwander1, Jens Brauer1, Angela Friederici1

Institutions:

1Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Introduction:

At the age of four years, children have reached many milestones, including speaking in sentences. However, there is still much to be learned, and the brain is actively changing during this time. While the ongoing changes in the development of the brain of a preschool child are well understood, to date the question of how this relates to cognitive functions, specifically to language, is understudied (1). A recent study reported a relationship between fractional anisotropy (FA), an index of directionality of water diffusion, and phonological processing in children (2). There is also evidence supporting a relationship between white matter structure and IQ (3). However, there are no studies that looked at the similarities and differences of a number of cognitive abilities and their relationship to white matter structure. In the current investigation we had two aims: first, to add to the growing literature outlining the relationship between cognitive functions, such as language ability, general intelligence, and working memory, and the brain white matter structural correlates. The second aim was to compare and contrast the relationship each cognitive measure shows with white matter.

Methods:

Standardized test scores and diffusion weighted MRI data from 59 typically developing four-year-old children were included in these analyses. We assessed vocabulary (AWST-R), syntactic ability (TSVK), verbal working memory (Mottier test, digit span), and full scale IQ (WPPSI-III). Diffusion weighted as well as T1 images were collected on a 3T Siemens TimTrio scanner using a 32-channel head coil. Diffusion data were acquired with 60 diffusion directions (b=1000m/s) and 7 volumes with no diffusion weighting, and the following parameters: 1.9 mm3 isotropic voxel size, TE=76 ms, TR=4000 ms, whole brain coverage. Diffusion images were preprocessed using ExploreDTI (v4.8.3) by applying motion, susceptibility and eddy current correction (4). FA maps were used to create a study-specific template. Data were processed using FSL's tract-based spatial statistics (TBSS) (5). We conducted five correlation analyses to test for correlations between FA and our cognitive measures using randomise (6). Age, sex, and mean FA were included as covariates of no interest in each analysis. Using AFNI's 3dFWHMx we estimated the global smoothness of the data to determine the significant cluster size using 3dClustSim (7). Clusters were formed at P <0.01 and are significant at a cluster-level P<0.05, corrected for multiple tests (8).

Results:

We found that three of our cognitive measures showed correlations with FA in the white matter of the left inferior frontal gyrus (IFG), namely the AWST-R, Mottier and IQ. Additionally, the AWST-R correlated significantly with FA in the white matter of the right middle temporal gyrus and the right superior frontal gyrus. The TSVK showed a relationship with FA in the left superior frontal gyrus white matter. Digit span correlated with FA in the left external capsule, and IQ additionally correlated with a cluster in the left hemisphere of the cerebellum. In addition to these positive correlations, we report significant negative correlations between FA and Mottier, AWST-R, and IQ in the cerebellar white matter.

Conclusions:

Our results demonstrate that there are correlations between several cognitive measures and FA. Using age as a covariate of no interest allows us to conclude that these relationships are not solely driven by age, but rather point to individual differences in typical cognitive development being related to white matter structure of the developing brain. In line with previous research correlating developing abilities with white matter structure, we found correlations in regions also found in the adult literature as well as additional regions (2). Our results are in accordance with the claim that children employ a more diffuse network to process tasks such as language in young age before the mature network emerges, which is in line with previous work (9).

Higher Cognitive Functions:

Higher Cognitive Functions Other

Imaging Methods:

Diffusion MRI 2

Language:

Language Acquisition 1

Lifespan Development:

Normal Brain Development: Fetus to Adolescence

Keywords:

Cognition
Development
Language
NORMAL HUMAN
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Cognitive Development; Children

1|2Indicates the priority used for review