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Imaging-genetics in dyslexia: connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments.

ABSTRACT: Dyslexia is a common pediatric disorder that affects 5-17% of schoolchildren in the United States. It is marked by unexpected difficulties in fluent reading despite adequate intelligence, opportunity, and instruction. Classically, neuropsychologists have studied dyslexia using a variety of neurocognitive batteries to gain insight into the specific deficits and impairments in affected children. Since dyslexia is a complex genetic trait with high heritability, analyses conditioned on performance on these neurocognitive batteries have been used to try to identify associated genes. This has led to some successes in identifying contributing genes, although much of the heritability remains unexplained. Additionally, the lack of relevant human brain tissue for analysis and the challenges of modeling a uniquely human trait in animals are barriers to advancing our knowledge of the underlying pathophysiology. In vivo imaging technologies, however, present new opportunities to examine dyslexia and reading skills in a clearly relevant context in human subjects. Recent investigations have started to integrate these imaging data with genetic data in attempts to gain a more complete and complex understanding of reading processes. In addition to bridging the gap from genetic risk variant to a discernible neuroimaging phenotype and ultimately to the clinical impairments in reading performance, the use of neuroimaging phenotypes will reveal novel risk genes and variants. In this article, we briefly discuss the genetic and imaging investigations and take an in-depth look at the recent imaging-genetics investigations of dyslexia.

SUBMITTER: Eicher JD

PROVIDER: S-EPMC3800223 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Imaging-genetics in dyslexia: connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments.

Eicher John D JD Gruen Jeffrey R JR

Molecular genetics and metabolism 20130717 3

Dyslexia is a common pediatric disorder that affects 5-17% of schoolchildren in the United States. It is marked by unexpected difficulties in fluent reading despite adequate intelligence, opportunity, and instruction. Classically, neuropsychologists have studied dyslexia using a variety of neurocognitive batteries to gain insight into the specific deficits and impairments in affected children. Since dyslexia is a complex genetic trait with high heritability, analyses conditioned on performance o ...[more]

PMID: 23916419

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Project description:Functional magnetic resonance imaging studies have reported reduced activation in parietotemporal and occipitotemporal areas in adults and children with developmental dyslexia compared to controls during reading and reading related tasks. These patterns of regionally reduced activation have been linked to behavioral impairments of reading-related processes (e.g., phonological skills and rapid automatized naming). The observed functional and behavioral differences in individuals with developmental dyslexia have been complemented by reports of reduced gray matter in left parietotemporal, occipitotemporal areas, fusiform and lingual gyrus and the cerebellum. An important question for education is whether these neural differences are present before reading is taught. Developmental dyslexia can only be diagnosed after formal reading education starts. However, here we investigate whether the previously detected gray matter alterations in adults and children with developmental dyslexia can already be observed in a small group of pre-reading children with a family-history of developmental dyslexia compared to age and IQ-matched children without a family-history (N = 20/mean age: 5:9 years; age range 5:1-6:5 years). Voxel-based morphometry revealed significantly reduced gray matter volume indices for pre-reading children with, compared to children without, a family-history of developmental dyslexia in left occipitotemporal, bilateral parietotemporal regions, left fusiform gyrus and right lingual gyrus. Gray matter volume indices in left hemispheric occipitotemporal and parietotemporal regions of interest also correlated positively with rapid automatized naming. No differences between the two groups were observed in frontal and cerebellar regions. This discovery in a small group of children suggests that previously described functional and structural alterations in developmental dyslexia may not be due to experience-dependent brain changes but may be present at birth or develop in early childhood prior to reading onset. Further studies using larger sample sizes and longitudinal analyses are needed in order to determine whether the identified structural alterations may be utilized as structural markers for the early identification of children at risk, which may prevent the negative clinical, social and psychological outcome of developmental dyslexia.

Project description:BackgroundDealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes.ResultsWe find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features.ConclusionsThe pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

Dataset Information

Imaging-genetics in dyslexia: connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments.

Publications

Imaging-genetics in dyslexia: connecting risk genetic variants to brain neuroimaging and ultimately to reading impairments.

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