Thursday, June 29, 2017: 12:45 PM
Wednesday, June 28 & Thursday, June 29
Jessica Bulthé1, Jellina Prinsen1, Jolijn Vanderauwera1, Stefanie Duyck1, Nicky Daniels1, Céline GIllebert1, Dante Mantini1, Bert De Smedt1, Hans Op de Beeck1
1KU Leuven, Leuven, Belgium
Recently, a multi-method brain imaging approach was used in adults with developmental dyslexia to directly compare the representation versus access hypotheses by combining measures of the quality of neural representations with measures of connectivity (Boets et al., 2013). This study revealed that dyslexia in adults is associated with disrupted connectivity without dysfunctional representations (Boets et al., 2013).
Here, we test whether this can be extrapolated to other neurodevelopmental disorders, specifically to developmental dyscalculia (DD). DD is thought to originate from impaired numerical magnitude processing (De Smedt et al., 2013b), but to date no study has investigated the neural quality of these magnitude representations and their access in adults with DD. On the other hand, it has also been suggested that the number representations themselves are not impaired, but that these representations are difficult to access (Noël and Rousselle, 2011) (Fig 1a).
The current study is the first to directly test both the quality of neural magnitude representations in DD as well as the access to these neural representations. We therefore coupled multivoxel pattern analysis (Fig 1b) with measures of functional and structural connectivity (Fig 1c). Starting from the study that tested similar hypotheses on the causes of dyslexia (Boets et al., 2013), we extended the spectrum of analyses even further by additionally including subject classification methods as well as voxel-based morphometry analyses.
1. Participants: We acquired data for 24 participants with dyscalculia and 24 control participants with normal achievement in mathematics (all females, aged between 18 and 27). The two groups were individually matched pairwise for their education, gender, and age.
2. fMRI Analyses: We tested (a) if there were activation differences between groups (univariate analyses and subject classification) and (b) if the neural representations of number were less precise in participants with dyscalculia vs controls (whole brain searchlight analysis and region of interest (ROI) based decoding).
3. fcMRI Analyses: functional connectivity between ROIs in occipital, parietal, temporal, and frontal cortex were compared between groups.
4. DTI Analyses: corpus callosum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and arcuate fasciculus were delineated. For each of the tracts franctional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity values were compared between groups.
5. Structural Analyses: Voxel-based morphometry analysis was applied to test for grey matter differences between the two groups.
1. fMRI Analyses: (A) Activation: Univariate fMRI analyses showed no significant activation differences between the two groups for symbolic or non-symbolic numbers. The results of the subject classification analysis did not show a significant subject classification accuracy using the functional data of the whole brain. (B) Representations: The ROI-based decoding analyses and searchlight analysis (Fig 2) revealed more precise non-symbolic number representations in frontal, parietal, and temporal regions for controls.
2. fcMRI Analysis: A hyperconnectivity was observed between occipital and temporal regions in participants with dyscalculia.
3. DTI Analysis: In none of the tracts for non of the diffusivity measures a significant group difference was observed, even at uncorrected multiple comparison level.
4. VBM Analysis: An increase in grey matter in the bilateral posterior cingulate cortex was observed in dyscalculia compared to controls.
Our results indicated impaired non-symbolic magnitude representations across the entire cortex in adults with DD. We also found anatomical and functional connectivity differences in DD. Hence, the deficits in DD cannot be localized to one brain region or to one particular type of brain deficit (functional, anatomical or connectivity).
Higher Cognitive Functions:
Space, Time and Number Coding 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Multivariate modeling 2
Poster Session - Thursday
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Dyscalculia
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Boets, B. et al., 2013. Intact But Less Accessible Phonetic Representations in Adults with Dyslexia. Science, 342(6163), pp.1251–1254.
De Smedt, B. & Gilmore, C.K., 2011. Defective number module or impaired access? Numerical magnitude processing in first graders with mathematical difficulties. Journal of experimental child psychology, 108(2), pp.278–292.
Noël, M.-P. & Rousselle, L., 2011. Developmental Changes in the Profiles of Dyscalculia: An Explanation Based on a Double Exact-and-Approximate Number Representation Model. Frontiers in human neuroscience, 5(165), pp.1–4.