Learning visual-phonological associations is a key skill underlying successful reading acquisition. However, we are yet to understand the cognitive mechanisms that enable efficient learning in good readers, and those which are aberrant in individuals with developmental dyslexia. Here, we use a repeated cued-recall task to examine how typical and reading-impaired adults acquire novel associations between visual and phonological stimuli, incorporating a looking-at-nothing paradigm to probe implicit memory for target locations. Cued recall accuracy revealed that typical readers' recall of novel phonological associates was better than dyslexic readers' recall, and it also improved more with repetition. Eye fixation-contingent error analyses suggest that typical readers' greater improvement from repetition reflects their more robust encoding and/or retrieval of each instance in which a given pair was presented: whereas dyslexic readers tended to recall a phonological target better when fixating its most recent location, typical readers showed this pattern more strongly when the target location was consistent across multiple trials. Thus, typical readers' greater success in reading acquisition may derive from their better use of statistical contingencies to identify consistent stimulus features across multiple exposures. We discuss these findings in relation to the role of implicit memory in forming new visual-phonological associations as a foundational skill in reading, and areas of weakness in developmental dyslexia.

This study aimed to characterize age-related white matter changes by evaluating patterns of overlap between the linear association of age with fractional anisotropy (FA) with mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Specifically, we assessed patterns of overlap between diffusion measures of normal appearing white matter by covarying for white matter hyperintensity (WMH) load, as WMHs are thought to increase with age and impact diffusion measures. Seventy-nine healthy adults aged between 18 and 75 years took part in the study. Diffusion tensor imaging (DTI) data were based on 61 directions acquired with a b-value of 2,000. We found five main patterns of overlap: FA alone (15.95%); FA and RD (31.90%); FA and AD (12.99%); FA, RD, and AD (27.93%); and FA, RD, and MD (8.79%). We showed that cognitively healthy aging adults had low WMH load, which subsequently had minimal effect on diffusion measures. We discuss how patterns of overlap may reflect underlying biological changes observed with aging such as loss of myelination, axonal damage, as well as mild microstructural and chronic white matter impairments. This study contributes to understanding the underlying causes of degeneration in specific regions of the brain and highlights the importance of considering the impact of WMHs in aging studies of white matter.

1819 This study aimed to evaluate the linear association of age with diffusion tensor imaging (DTI) 20 measures of white matter such as fractional anisotropy (FA), mean diffusivity (MD), axial 21 diffusivity (AD) and radial diffusivity (RD). We assessed patterns of overlap between linear 22 correlations of age with FA with RD, MD and AD to characterize the process of white matter 23 degeneration observed with ageing. 79 healthy adults aged between 18 and 75 took part in the 24 study. The DTI data were based on 61 directions acquired with a b-value of 2000. There was a 25 statistically significant negative linear correlation of age with FA and AD and a positive linear 26 correlation with RD and MD, and AD. The forceps minor tract showed largest percentage of voxels 27 with an association of age with FA, RD and AD, and the anterior thalamic radiation with MD. We 28 found 5 main patterns of overlap: FA alone (15.95%); FA and RD (31.90%); FA and AD (12.99%); 29 FA, RD and AD (27.37%); FA RD, and MD (6.94%). Patterns of overlap between diffusion measures 30 may reflect underlying biological changes with healthy ageing such as loss of myelination, axonal 31 damage, as well as mild microstructural and chronic white matter impairments. This study may 32 provide information about causes of degeneration in specific regions of the brain, and how this 33 may affect healthy brain functioning in older adults. 3 34 Introduction 35 36Diffusion tensor imaging (DTI) is a neuroimaging technique, which allows for non-invasive, 37 in vivo, investigation of white matter [1][2][3]. DTI measures are based on random motion of water 38 molecules, where within the brain diffusion of water is less restricted, or more isotropic, in areas 39 of grey matter and CSF, and more restricted, or more anisotropic, in areas of white matter. When 40 white matter structural architecture deteriorates, water molecules within white matter tissue 41 become more isotropic, making DTI a useful tool for assessing atrophy [4][5][6][7][8][9]. 43The diffusion tensor is a 3x3 covariance matrix used to model diffusion within a voxel, in 44 which there are 3 positive eigenvalues (λ1, λ2, λ3) and 3 orthogonal eigenvectors (ε1, ε2, ε3). The 45 eigenvalues of the tensor give the diffusivity in the direction of each eigenvector. Together they 46 describe diffusion probability using an ellipsoid, where the axes of the ellipsoid are aligned with 47 the eigenvectors, and the major eigenvector (λ1) represents the principal diffusion direction. 49Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial 50 diffusivity (AD) are the four main diffusion-based measurements of white matter structural 51 architecture. FA measures the amount of diffusion asymmetry within a voxel, where a value of 0 52 is isotropic and is represented by a spherical ellipsoid with equal eigenvalues, and a value of 1 is 53 anisotropic and is represented by an elongated ellipsoid with unequal eigenvalues. FA has been 54 associated with the microstructural integrity of white m...

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