Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality - Patrick Friedrich; Kaustubh R. Patil; Lisa N. Mochalski; Xuan Li; Julia A. Camilleri; Jean-Philippe Kröll; Lisa Wiersch; Simon B. Eickhoff; Susanne Weis (Brain Structure and Function, 2022)
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples ( n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

Relations between hemispheric asymmetries of grey matter and auditory processing of spoken syllables in 281 healthy adults - Tulio Guadalupe; Xiang-Zhen Kong; Sophie E. A. Akkermans; Simon E. Fisher; Clyde Francks (Brain Structure and Function, 2022)
Most people have a right-ear advantage for the perception of spoken syllables, consistent with left hemisphere dominance for speech processing. However, there is considerable variation, with some people showing left-ear advantage. The extent to which this variation is reflected in brain structure remains unclear. We tested for relations between hemispheric asymmetries of auditory processing and of grey matter in 281 adults, using dichotic listening and voxel-based morphometry. This was the largest study of this issue to date. Per-voxel asymmetry indexes were derived for each participant following registration of brain magnetic resonance images to a template that was symmetrized. The asymmetry index derived from dichotic listening was related to grey matter asymmetry in clusters of voxels corresponding to the amygdala and cerebellum lobule VI. There was also a smaller, non-significant cluster in the posterior superior temporal gyrus, a region of auditory cortex. These findings contribute to the mapping of asymmetrical structure–function links in the human brain and suggest that subcortical structures should be investigated in relation to hemispheric dominance for speech processing, in addition to auditory cortex.

To learn or to gain: neural signatures of exploration in human decision-making - Shanshan Zhen; Zachary A. Yaple; Simon B. Eickhoff; Rongjun Yu (Brain Structure and Function, 2022)

Brain cortical characteristics of lifetime cognitive ageing - Simon R. Cox; Mark E. Bastin; Stuart J. Ritchie; David Alexander Dickie; Dave C. Liewald; Susana Muñoz Maniega; Paul Redmond; Natalie A. Royle; Alison Pattie; Maria Valdés Hernández; Janie Corley; Benjamin S. Aribisala; et al. (Brain Structure and Function, 2018)

Risk and protective factors for structural brain ageing in the eighth decade of life - Stuart J. Ritchie; Elliot M. Tucker-Drob; Simon R. Cox; David Alexander Dickie; Maria del C. Valdés Hernández; Janie Corley; Natalie A. Royle; Paul Redmond; Susana Muñoz Maniega; Alison Pattie; Benjamin S. Aribisala; Adele M. Taylor; et al. (Brain Structure and Function, 2017)

Normal radial migration and lamination are maintained in dyslexia-susceptibility candidate gene homolog Kiaa0319 knockout mice - Isabel Martinez-Garay; Luiz G. Guidi; Zoe G. Holloway; Melissa A. G. Bailey; Daniel Lyngholm; Tomasz Schneider; Timothy Donnison; Simon J. B. Butt; Anthony P. Monaco; Zoltán Molnár; Antonio Velayos-Baeza (Brain Structure and Function, 2017)

Brain white matter structure and information processing speed in healthy older age - Ksenia A. Kuznetsova; Susana Muñoz Maniega; Stuart J. Ritchie; Simon R. Cox; Amos J. Storkey; John M. Starr; Joanna M. Wardlaw; Ian J. Deary; Mark E. Bastin (Brain Structure and Function, 2016)

A systematic review of brain frontal lobe parcellation techniques in magnetic resonance imaging - Simon R. Cox; Karen J. Ferguson; Natalie A. Royle; Susan D. Shenkin; Sarah E. MacPherson; Alasdair M. J. MacLullich; Ian J. Deary; Joanna M. Wardlaw (Brain Structure and Function, 2014)

ALE meta-analysis on facial judgments of trustworthiness and attractiveness - D. Bzdok; R. Langner; S. Caspers; F. Kurth; U. Habel; K. Zilles; A. Laird; Simon B. Eickhoff (Brain Structure and Function, 2011)