Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics

Wiberg, Akira; Ng, Michael; Omran, Yasser Al; Alfaro-Almagro, Fidel; McCarthy, Paul; Marchini, Jonathan; Bennett, David L.H.; Smith, Stephen M.; Douaud, Gwenaëlle; Furniss, Dominic

doi:10.1093/brain/awz257

Cited by 143 publications

(158 citation statements)

References 49 publications

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“…5c ). There are several resting-state connectivity-related IDPs correlated with handedness ( Tables S3 ), consistent with a recent study 30 that also used UKB IDPs, while no IDPs related to other modalities are found significant; in both cases the maximum IDP correlation only reached r=0.12, whereas the strongest association with BigFLICA modes was almost double this.…”

Section: Resultssupporting

confidence: 89%

Phenotype Discovery from Population Brain Imaging

Gong

Beckmann

Smith

2020

Preprint

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Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed. Introduction1 Large-scale multimodal brain imaging has enormous potential for boosting epidemiological and neu-2 roscientific studies, generating markers for early disease diagnosis and prediction of disease progres-3 sion, and the understanding of human cognition, by means of linking to clinical or behavioural vari-4 ables. Recent major studies have been acquiring brain magnetic resonance imaging (MRI), genetics and 5 demographic/behavioural data from large cohorts. Examples are the UK Biobank (UKB) 1 , the Human 6 Connectome Project (HCP) 2 and the Adolescent Brain Cognitive Development (ABCD) study 3 . These 7 studies involve multimodal data, meaning that several distinct types of MRI data are acquired, mapping 8 activity, functional networks, structural connectivity, white matter microstructure, and organisation and 9 volumes of different brain tissues and sub-structures 1 . However, the multimodal, high-dimensional and 10 noisy nature of such big datasets makes many existing analytical approaches for extracting interpretable 11 information impractical 4 . 12Traditionally, large-scale neuroimaging studies first summarize the imaging data into interpretable 13 image-derived phenotypes (IDPs) 1, 5 , which are scalar quantities derived from raw imaging data (e.g., 14 regional volumes from structural MRI, mean task activations from task MRI, resting-state functional 15 connectivities between brain parcels). This knowledge-based approach is simple and efficient, and ef-16 fectively reduces the high-dimensional data into interpretable, compact, convenient features. However, 17...

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Section: Resultssupporting

confidence: 89%

Phenotype Discovery from Population Brain Imaging

Gong

Beckmann

Smith

2020

Preprint

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“…These observations have led to a number of studies investigating the role of handedness and brain asymmetries both in the context of human evolution and in cognitive abilities. Handedness has been investigated for association with cognitive skills, personality traits and psychiatric disorders (3)(4)(5). However, the cause/effect relationship between handedness, brain asymmetries and disorders remains unexplained and debated (6).…”

Section: Introductionmentioning

confidence: 99%

Different laterality indexes are poorly correlated with one another but consistently show the tendency of males and females to be more left- and right- lateralised, respectively

Castillo¹,

Lynch²,

Paracchini³

2019

Preprint

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Handedness is assessed primarily as a binary trait on the basis of the preferred hand for writing. At population level, about 90% people prefer using the right. Handedness can also be assessed as a continuous trait with laterality indexes, but these are not time and cost effective, and are not routinely collected. Here, we assessed the relationship of writing hand preference with four laterality indexes derived from measures of dexterity (pegboard task, marking squares and sorting matches) and strength (grip strength) available in a range of N = 6664-8069 children from the ALSPAC cohort.Although all indexes identified a higher proportion of individuals performing better with their right hand, they showed low correlation with each other (0.08-0.3). Analysis of sex effects on the laterality indexes showed that males and females tend to be, on all measures, more right-and the leftlateralised, respectively. Males also had a higher tendency to be poorly lateralised. This study shows that different handedness measures tap into different dimensions of laterality and cannot be used interchangeably. The similar trends for males and females observed across indexes suggest that sex effects should be taken into account in handedness and laterality studies.

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“…While very low levels of heritability pose methodological problems, the search for genetic variants continues, not so much with the goal of explaining large amounts of variance in handedness, but rather with the goal of illuminating the biological pathways involved in determining asymmetry. This can be feasible, provided there are suitable phenotypic measures available in very large samples Wiberg et al, 2019) It is possible that the same will prove to be the case for language lateralisation, especially given prior findings of significant heritability in structural measures of subcortical brain regions (Eyler et al, 2014;Guadalupe et al, 2017), cortical area and thickness (Kong et al, 2018) and language-related fibre tracts (Jahanshad et al, 2010), plus the large family study of Somers et al (2015) that used a binary phenotype, and the mixed findings on dichotic listening by Ocklenburg et al (2016). A further point to note is that different language tasks show different degrees of lateralisation (Woodhead et al, 2019), and we simply do not know which may be the most heritable.…”

Section: Discussionmentioning

confidence: 99%

Heritability of language laterality assessed by functional transcranial Doppler ultrasound: a twin study

Bishop¹,

Bates²

2020

Wellcome Open Res

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Prior studies have estimated heritability of around 0.25 for Background: the trait of handedness, with studies of structural brain asymmetry giving estimates in a similar or lower range. Little is known about heritability of functional language lateralization. This report describes heritability estimates using functional language laterality and handedness phenotypes in a twin sample previously reported by Wilson and Bishop (2018).The total sample consisted of 194 twin pairs (49% monozygotic) Methods: aged from 6 to 11 years. A language laterality index was obtained for 141 twin pairs, who completed a protocol where relative blood flow through left and right middle cerebral arteries was measured using functional transcranial Doppler ultrasound (fTCD) while the child described animation sequences. Handedness data was available from the Edinburgh Handedness Inventory (EHI) and Quantification of Hand Preference (QHP) for all 194 pairs. Heritability was assessed using conventional structural equation modeling, assuming no effect of shared environment (AE model).For the two handedness measures, heritability estimates were Results: consistent with prior research: 0.23 and 0.22 respectively for the EHI and QHP. For the language laterality index, however, the twin-cotwin correlations were close to zero for both MZ and DZ twins, and the heritability estimate was zero.A single study cannot rule out a genetic effect on language Conclusions: lateralisation. It is possible that the low twin-cotwin correlations were affected by noisy data: although the split-half reliability of the fTCD-based laterality index was high (0.85), we did not have information on test-retest reliability in children, which is likely to be lower. We cannot reject the hypothesis that there is low but nonzero heritability for this trait, but our data suggest that individual variation in language lateralisation is predominantly due to stochastic variation in neurodevelopment.

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Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics

Cited by 143 publications

References 49 publications

Phenotype Discovery from Population Brain Imaging

Phenotype Discovery from Population Brain Imaging

Different laterality indexes are poorly correlated with one another but consistently show the tendency of males and females to be more left- and right- lateralised, respectively

Heritability of language laterality assessed by functional transcranial Doppler ultrasound: a twin study

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