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. 2023 Sep 15;13(1):15348.
doi: 10.1038/s41598-023-42563-7.

A deep phenotyping approach to assess the association of handedness, early life factors and mental health

Lena Sophie Pfeifer #  1 , Judith Schmitz #  2 , Maike Schwalvenberg  3 , Onur Güntürkün  3 , Sebastian Ocklenburg  3   4   5
Affiliations

A deep phenotyping approach to assess the association of handedness, early life factors and mental health

Lena Sophie Pfeifer et al. Sci Rep. .

Abstract

The development of handedness and other form of functional asymmetries is not yet understood in its critical determinants. Early life factors (e.g., birth weight, birth order) have been discussed to contribute to individual manifestations of functional asymmetries. However, large-scale data such as the UK Biobank suggest that the variance in handedness that is explained by early life factors is minimal. Additionally, atypical handedness has been linked to clinical outcomes such as neurodevelopmental and psychiatric disorders. Against the background of this triad, the current study investigated associations between different forms of functional asymmetries and (a) early life factors as well as (b) clinical outcomes. Functional asymmetries were determined by means of a deep phenotyping approach which notably extends previous work. In our final sample of N = 598 healthy participants, the different variables were tested for associations by means of linear regression models and group comparisons (i.e., ANOVAs and Chi-squared tests). Confirming previous findings from larger cohorts with shallow phenotyping, we found that birth factors do not explain a substantial amount of variance in functional asymmetries. Likewise, functional asymmetries did not seem to have comprehensive predictive power concerning clinical outcomes in our healthy participants. Future studies may further investigate postulated relations in healthy and clinical samples while acknowledging deep phenotyping of laterality.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Quantitative asymmetry measures as a function of quantitative birth factors (linear regression).
Figure 2
Figure 2
Quantitative asymmetry measures as a function of binary birth factors (ANOVA).
Figure 3
Figure 3
Categorical asymmetry measures (L–M–R) as a function of quantitative birth factors.
Figure 4
Figure 4
Clinical questionnaires (overall scores) as a function of quantitative asymmetry measures (linear regression).
Figure 5
Figure 5
Clinical questionnaires (overall scores) as a function of categorical asymmetry measures (L–M–R) (linear regression).
See this image and copyright information in PMC

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