Research ArticleResearch Article: New Research, Integrative Systems

Functional Connectome Correlates of Laterality Preferences: Insights into Hand, Foot, and Eye Dominance across the Lifespan

Link Tejavibulya, Corey Horien, Carolyn Fredericks, Bronte Ficek, Margaret L. Westwater and Dustin Scheinost
eNeuro 5 June 2025, 12 (7) ENEURO.0580-24.2025; https://doi.org/10.1523/ENEURO.0580-24.2025

Abstract

Humans exhibit laterality preferences, with handedness being the most extensively studied. Accordingly, brain–handedness associations are well documented. However, laterality preferences extend beyond handedness to include other limbs, such as footedness and eyedness. Despite these distinctions, brain–footedness and brain–eyedness associations using resting-state functional connectomes remain largely unexplored. We utilize two large datasets, the Human Connectome Project-Development (HCP-D) and Human Connectome Project-Aging (HCP-A), to study the associations between sidedness (i.e., handedness, footedness, and eyedness) and whole-brain functional connectomes. While hand and foot preferences were correlated significantly, they explained <40% of the variance, suggesting some distinctions between measures. For both cohorts, significant associations between handedness connectivity were observed [p < 0.05, network-based statistics (NBS) corrected]. Notable patterns include increased connectivity for left-handedness in the posterior temporal areas and right-handedness in cerebellar regions. In contrast, significant associations between footedness and handedness connectivity were observed only in the HCP-A (p < 0.05, NBS corrected) and not the HCP-D. No significant associations between eyedness and connectivity were observed for either group. Finally, we compared the effect size between brain–handedness and brain–footedness associations. A greater difference was found in the HCP-D. The two cohorts primarily differed in edge distribution in the prefrontal lobe, temporal lobe, and cerebellum. Overall, in adults, brain–handedness and brain–footedness associations were similar. However, in children to adolescents, brain–handedness and brain–footedness associations diverge, suggesting a developmental shift. Characterizing sidedness associations with whole-brain connectomes may provide important insights into understanding the motor and visual systems, rehabilitation and occupational therapy, and benchmarking normative variations in the connectome.

Significance Statement

Lateral preferences stem from functional biases in brain hemispheres. However, studies associating brain connectivity to these predominant preferences have often been oversimplified to handedness. Although approximately 90% of the population is right-handed, other lateral preferences—like footedness and eyedness—show much lower and more varied distributions. This study aims to provide a foundational understanding of how broader lateral preferences might offer a more nuanced view of the brain's functional connectivity.

Introduction

Humans have laterality preferences, with handedness being the most studied. Not only does handedness play a role in everyday life with motor control, but it has been linked to various physical and mental health outcomes (Klar, 1999; Deep-Soboslay et al., 2010; Hirnstein and Hugdahl, 2014; Wiberg et al., 2019; Sainburg and Duff, 2006). Accordingly, brain–handedness associations are well-known. The brains of left-handed individuals are organized differently regarding language, motor, somatosensory, and the corpus callosum (Habib et al., 1991; Gut et al., 2007). Studies have shown that language (Knecht et al., 2000; Szaflarski et al., 2002; Wilberg et al., 2009; Ocklenburg et al., 2014; Esteves et al., 2021), motor (Solodkin et al., 2001; Hammond, 2002; Hatta, 2007), and somatosensory (Hoshiyama and Kakigi, 1999; Sörös et al., 1999; Jung et al., 2008) networks demonstrate robust activation and connectivity differences between left- and right-handed individuals. Recent work has shown that brain–handedness associations extend beyond localized regions of interest to widespread functional connectivity differences, affecting every canonical brain network (Faurion et al., 1999; Perlaki et al., 2013; Tejavibulya et al., 2022).

Laterality preferences extend far beyond handedness. Preferences exist for other limbs and organs, including feet (i.e., footedness) and eyes (i.e., eyedness). It is often assumed that other laterality preferences follow handedness, and, as such, they remain understudied compared with handedness (Elias et al., 1998). Nevertheless, these preferences are unique and potentially have different brain associations. For example, although 90% of the population is right-handed, this percentage drops considerably for foot and eye preference (Bourassa et al., 1996; Muraleedharan et al., 2020). Putatively, footedness predicts cognition and language better than handedness (Elias et al., 1998). Similarly, changes in ocular dominance (i.e., switch from right eye preference to left eye preference) cause neural circuit changes in the visual cortex (Sato and Stryker, 2008). Although distinct from handedness, brain–footedness and brain–eyedness associations using whole-brain functional connectomes have not been studied. Such associations may have important insights for understanding the motor and visual systems, for rehabilitation and occupational therapy, and for benchmarking normative variations in the connectome.

In this study, we utilize two large datasets, the Human Connectome Project-Development (HCP-D; Harms et al., 2018; Somerville et al., 2018) and Human Connectome Project-Aging (HCP-A; Harms et al., 2018; Bookheimer et al., 2019), to study the associations between sidedness (i.e., handedness, footedness, and eyedness) and whole-brain functional connectivity. Since brainwide associations with handedness are established (Tejavibulya et al., 2022), we utilize handedness as a comparison benchmark. We investigated behavioral correlations between sidedness measures, whole-brain associations of footedness and eyedness, differences in effect sizes between brain–handedness and brain–footedness associations, and differences between the two cohorts in these associations.

Materials and Methods

Datasets: Human Connectome Project-Development (HCP-D) and Human Connectome Project-Aging (HCP-A)

We analyzed sidedness and resting-state fMRI data from the Human Connectome Project-Development (HCP-D; Van Essen et al., 2012; Harms et al., 2018; Somerville et al., 2018) and the Human Connectome Project-Aging (HCP-A; Harms et al., 2018; Bookheimer et al., 2019). The HCP-D consisted of individuals aged 5–21 years, whereas the HCP-A included participants between ages 36 and 100 years. For the analyses comparing sidedness across measures, individuals were excluded if they were missing sidedness measures. These analyses include n = 486 individuals from the HCP-D and n = 724 individuals from the HCP-A. For analyses using the resting-state data, individuals were excluded if they had >0.1 mm of average frame-to-frame head motion. These analyses include n = 465 individuals from the HCP-D and n = 368 individuals from the HCP-A. Demographic information is presented in Table 1.

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Table 1.

Summary of demographics represented in the HCP-D and HCP-A

Behavioral measures of sidedness

Handedness was determined from individuals’ self-reported preferences for their dominant hand using nine items from the Edinburgh Handedness Inventory (Oldfield, 1971). Participants were asked which hand they preferred to write, throw a ball, use a spoon, brush their teeth, use an upper hand when using a broom, light a match, open a box, use scissors, and use a knife (without a fork). Footedness was determined from individuals’ self-reported preferences for their dominant foot to kick. Eyedness was determined from individuals’ self-reported preferences for their dominant eye to keep open when looking through a telescope. Individuals responded using a five-point Likert scale, ranging from 1 “always left” to 5 “always right,” to indicate their preference for each. All measures were kept as ordinal data rather than binarizing the data to provide dimensional measures of sidedness. The counts of each response are shown in Tables 2 and 3.

View this table:
Table 2.

Table of handedness, footedness, and eyedness concordance for HCP-D

View this table:
Table 3.

Table of handedness, footedness, and eyedness concordance for HCP-A

Imaging data

All HCP-D and HCP-A neuroimaging was conducted on a 3T Siemens Prisma scanners (Siemens, Erlangen, Germany) using the Siemens 32-channel Prisma head coil. The resting-state scans are acquired with a 2D multiband gradient-recalled echoplanar imaging sequence (multiband factor, 8; TR, 800 ms; TE, 37 ms; flip angle, 52°; 2.0 mm isotropic voxels; and 72 slices). T1w scans are acquired with a multiecho MPRAGE sequence (0.8 mm isotropic voxels; sagittal FOV, 256 × 240 × 166 mm; matrix size, 320 × 300 × 208 slices; TR/TI, 2,500/1,000; TE, 1.8/3.6/5.4/7.2 ms; flip angle, 8°).

Preprocessing and generating connectomes

Both the HCP-D and HCP-A datasets were analyzed with identical processing pipelines. T1w scans were first skull-stripped using an optimized version of the FMRIB's Software Library (Smith et al., 2004) pipeline (Lutkenhoff et al., 2014). Functional images were motion-corrected using SPM12. All further analyses were performed using BioImage Suite (Joshi et al., 2011). Several covariates of no interest were regressed from the data, including linear and quadratic drifts, mean cerebral spinal fluid signal, mean white matter signal, and mean gray matter signal. For additional control of possible motion-related confounds, a 24-parameter motion model (including six rigid-body motion parameters, six temporal derivatives, and these terms squared) was regressed from the data. The data were temporally smoothed with a Gaussian filter (approximate cutoff frequency, 0.12 Hz).

Nodes were defined using the Shen 268-node brain atlas (Shen et al., 2013), which includes the cortex, subcortex, and cerebellum. The atlas was warped from MNI space into single-subject space via a series of linear and nonlinear transformations calculated using a previously validated algorithm (Scheinost et al., 2017), implemented in BioImage Suite. Resting-state connectivity was calculated as the mean time courses for each of the 268 nodes (i.e., averaging the time courses of all constituent voxels). Node-by-node pairwise correlations were computed, and Pearson’s correlation coefficients were Fisher z-transformed to yield symmetric 268 × 268 connectivity matrices, in which each matrix element represents the connectivity strength between two individual nodes (i.e., “edge”).

Statistical analyses

To investigate the associations between handedness, footedness, and eyedness, Spearman's correlations (ρ) were calculated between pairs of sidedness measures and subsequently squared to obtain the variance explained. We used Z-tests and Steiger's z-test for dependent correlations to compare effect sizes (Steiger, 1980; Lee and Preacher, 2013) between associations and groups.

We separately assessed edge-wise Spearman's correlations with the handedness, footedness, and eyedness scores for the HCP-D and HCP-A datasets to investigate brain–handedness, brain–footedness, and brain–eyedness associations. We used network-based statistics (NBS; Zalesky et al., 2010) to identify components where functional connectivity was significantly associated with handedness while controlling for the familywise error rate. We used a component-determining threshold z = 1.96, two tails, and K = 5,000 permutations. NBS is analogous to cluster-based correction and solves the statistical problem of massively multiple comparisons in a whole-brain connectivity analysis. In NBS, using the correlation between sidedness measures and connectivity, the largest fully connected network of suprathreshold edges, or “component,” is identified, and its extent is defined as the number of edges it comprises. Finally, these calculations are repeated for 5,000 iterations, in which subjects’ group assignments are randomly permuted to create a null distribution for the expected component size due to chance. NBS results are shown at p < 0.05, corrected for multiple comparisons.

We examined the overlap of significant edges from the NBS analyses to investigate the convergence of connectivity profiles across sidedness measures. Significance was determined with the hypergeometric cumulative density function (Rosenberg et al., 2016), which returns the probability of drawing up to x of K possible items in n drawings without replacement from an M-item population. In other words, this approach calculates the probability of finding the number of overlapping from two independent analyses by chance. Given many possible edges (>35,000), finding overlapping edges when randomly sampling two sets of ∼500 edges is rare. This was implemented in Matlab as: p = 1-hygecdf (x, M, K, n), where x is the number of overlapping edges, K is the number of connections in the HCP-D, n is the number of connections in the HCP-A, and M is the total number of edges in the matrix (35,778).

As thresholding results based on significance can modulate observed group differences (Taylor et al., 2023), we also examined the effect sizes for brain–handedness, brain–footedness, and brain–eyedness associations for the HCP-D and the HCP-A datasets without thresholding for significance. We quantified the effect size using Cohen's d. To investigate if edges strongly associated with handedness were strongly associated with footedness or eyedness, we correlated effect sizes for all 35,778 edges. We used Z-tests to compare these correlations across groups.

Finally, we compared brain–handedness and brain–footedness associations using edge-wise Steiger's z-test (Steiger, 1980; Lee and Preacher, 2013) to account for the correlation between handedness and footedness. Chi-squared tests were used to quantify significant differences in edge distributions between the HCP-A and HCP-D.

Results

Behavioral association between handedness, footedness, and eyedness

Spearman's correlations (ρ) between pairs of sidedness (e.g., handedness, footedness, and eyedness) are shown in Figure 1. In both cohorts, all associations were significant (r's > 0.14, p's < 0.05, corrected). However, the correlation between handedness and footedness and between handedness and eyedness were significantly greater than the correlation between footedness and eyedness (z's > 4.58, p's < 0.001) in both cohorts. Significantly stronger associations were observed in the HCP-A compared with the HCP-D (z's > 1.65, p's < 0.05). Overall, while handedness and footedness were correlated significantly, they explained <40% of the variance of the other one, suggesting some distinctions between the measures.

Figure 1.
Figure 1.

Behavioral correlations between sidedness measures. While handedness and footedness were correlated significantly, they explained <40% of the variance of the other one, suggesting some distinctions between the measures. Correlation heatmaps are shown as variance explained (r2) for each pair of sidedness measures in both cohorts.

Functional connectivity associations with handedness, footedness, and eyedness

For both cohorts, significant associations between handedness and connectivity were observed (p < 0.05, NBS correction). These associations were widespread, with edges in every functional network and lobe, mirroring our previous results in an independent cohort (Tejavibulya et al., 2022). In the HCP-A, 1,507 edges were positively associated with left-handedness, and 1,396 were positively associated with greater right-handedness. In the HCP-D, 1,437 edges were positively associated with left-handedness, and 1,306 were positively associated with greater right-handedness. Notable patterns include prominent representations in prefrontal and cerebellar regions for both left- and right-handedness in both datasets. In the HCP-A, right-handedness was highly represented in the occipital network attributing to 29.58% of edges, whereas left-handedness was shown to be represented in the limbic network at 29.13% of edges (Fig. 2, top row, orange circular plot). In contrast, edges attributing to differences in handedness in the HCP-D were mostly confined within the cerebellar and prefrontal networks (Fig. 2, top row, blue circular plot). Overall, detected edges significantly overlapped between the two cohorts (p < 0.001), suggesting connectivity profiles of handedness were consistent across the HCP-A and HCP-D.

Figure 2.
Figure 2.

Edges significantly differ between left- and right-sided individuals as calculated by network-based statistics. Circular bar plots showing the percent of significant edges belonging to each canonical network for each sidedness measure (rows) as separated by datasets (columns). Each circular bar plot depicts percentages for left- (cyan for HCP-D and salmon for HCP-A) and right-sided edges (blue for HCP-D and red for HCP-A). Handedness yielded a significance of p < 0.001 for both the HCP-A and the HCP-D, whereas footedness only yielded a significance of p < 0.001 for the HCP-A. The remainder of the results show no significant differences.

In contrast, significant associations between footedness and connectivity were observed only in the HCP-A (p < 0.05, NBS correction). In the HCP-A, 1,344 edges were positively associated with greater left-footedness, and 1,178 were positively associated with greater right-footedness. Like handedness, these associations were widespread, with edges in every functional network and lobe significantly overlapping with the connectivity profiles for handedness (p < 0.001). More specifically in the HCP-A, significant edges associated with right-footedness were distributed across multiple networks at 29.58, 20.49, and 21.31% for the occipital, motor, and limbic networks in right-handedness, respectively, while with left-handedness significant edges were more confined within the parietal and prefrontal networks at 24.62 and 29.66%, respectively. Although NBS results did not yield significance in the HCP-D, the limbic networks were associated with footedness differences with 30.92 and 29.31% of edges in left- and right-footedness, respectively, connected either within or between the limbic network. The edges associated with greater left-footedness and left-handedness in the HCP-A significantly overlapped (overlapping edges, 605; p < 0.001). Similarly, the edges associated with greater right-footedness and right-handedness in the HCP-A significantly overlapped (overlapping edges, 505; p < 0.001). Overall, handedness and footedness exhibited similar significant associations with functional connectivity only in HCP-A.

No significant associations between eyedness and connectivity were observed for either group; however, results are shown here for consistency.

Comparisons of effect sizes

The distributions of effect sizes across all edges (regardless of significance) for connectivity associations with handedness, footedness, and eyedness in the HCP-D and HCP-A, independently, are shown in Figure 3. Aligned with Figure 2, brain–eyedness associations exhibited the narrowest effect size distributions (i.e., smaller effect sizes between connectivity and eyedness), followed by footedness and handedness. Across all three measures of sidedness, the distributions of effect sizes in the HCP-D were narrower than that of the HCP-A, indicating that effect sizes were lower for all measures in the younger cohort. Visually, while the effect size distributions were similar for handedness and footedness between the HCP-A and HCP-D, the effect size distribution of footedness for the HCP-D was much narrower than that for the HCP-A. Overall, the effect sizes for footedness in the HCP-D are smaller and closer to that of eyedness. In contrast, the effect sizes for footedness in the HCP-A are much larger and more closely resemble handedness.

Figure 3.
Figure 3.

Effect size quantifications and correlations for all edges in each sidedness measure. Top, Effect sizes (Cohen's d) for all 35,778 were calculated and plotted on a histogram for each sidedness measure and cohort separately. A wider histogram indicates that more edges show a larger effect size. Bottom, Pairs of sidedness measures from the top were further compared individually and accompanied with calculations of covariances for the HCP-D and the HCP-A, separately.

Additionally, in the HCP-A, edges strongly associated with handedness were also strongly associated with footedness (Fig. 3; r = 0.60, p < 0.001), suggesting that similar edges are involved in handedness and footedness. This correlation was significantly weaker in the HCP-D (Fig. 3; HCP-D, r = 0.22, p < 0.001; Z = 6.704, p < 0.001). In both cohorts, edges with strong eyedness associations were independent of those with strong associations with handedness or footedness.

Comparison between handedness and footedness associations with connectivity

When comparing brain–handedness and brain–footedness associations, 3,035 edges exhibited significantly different effect sizes in the HCP-A, and 4,143 edges exhibited significantly different effect sizes in the HCP-D. As expected, the number of edges was significantly greater in the HCP-D than in the HCP-A (Fig. 4A; x2 = 8.1, p < 0.001), suggesting a mismatch of handedness and footedness. Surprisingly, for both cohorts, most of these edges were located in the right hemisphere (χ2 = 870.26, p < 0.0001). The two cohorts primarily differed in edge distribution in the prefrontal lobe, temporal lobe, and cerebellum (Fig. 4B,C).

Figure 4.
Figure 4.

Edges with significantly different effect sizes in handedness and footedness for each cohort separately. A, The number of edges with significantly different effect sizes for brain–handedness and brain–footedness associations, separated by cohort. As expected, the number of edges was significantly greater in the HCP-D than in the HCP-A (x2 = 8.1, p < 0.001). B, These edges were located throughout the brain. Results for all visualizations were thresholded as follows to aid with interpretability (70 for HCP-D and 50 for HCP-A). C, Edges with significantly different effect sizes for brain–handedness and brain–footedness associations split by hemispheres and networks. * indicates networks where the HCP-D exhibited significantly more edges that differed in effect size between handedness and footedness.

Discussion

We investigated how lateral preferences—extending beyond handedness preferences—associate with the brain's functional organization across two distinct cohorts and generations using data from the HCP-Development and HCP-Aging datasets. Behavioral associations between handedness, footedness, and eyedness showed weaker correlations in the younger cohort (HCP-D) than in the older cohort (HCP-A). Footedness and handedness associations strengthened in the older cohorts, while eyedness exhibited no associations between the other two measures. For both cohorts, significant associations between handedness and connectivity were observed. Notable patterns include increased connectivity for left-handedness in the posterior temporal areas and right-handedness in cerebellar regions. In contrast, significant associations between footedness and connectivity were observed only in the HCP-A. These connectivity patterns were similar to the handedness results. No significant associations between eyedness and connectivity were observed for either group. Finally, we compared the effect size between brain–handedness and brain–footedness associations. A greater difference was found in the HCP-D. The two cohorts primarily differed in edge distribution in the prefrontal lobe, temporal lobe, and cerebellum. Overall, in adults, brain–handedness and brain–footedness associations were similar. However, in children to adolescents, brain–handedness and brain–footedness associations diverge, suggesting a developmental shift. Characterizing sidedness associations with whole-brain connectomes provides a starting point for benchmarking normative variations.

Across analyses, the cerebellum and prefrontal cortex consistently showed differential connectivity patterns associated with handedness and footedness. The cerebellum is known to develop substantially postnatally (Friede, 1973), and both the cerebellum and the prefrontal cortex are heavily influenced by environmental factors (Rossi et al., 1997; Angelucci et al., 2009; Hodel, 2018). Previous studies have highlighted the cerebellum involvement in identifying left- and right-handed groups (Tejavibulya et al., 2022) and fine motor development (Kusano et al., 2021; Ashida et al., 2022; Saadon-Grosman et al., 2022). Notably, these regions extend beyond regions classically associated with laterality preferences, like language ( Knecht et al., 2000; Wiberg et al., 2019), motor (Dassonville et al., 1997; Solodkin et al., 2001; Klöppel et al., 2010), and somatosensory areas (Sörös et al., 1999; Jung et al., 2008).

Most individuals have two hands, feet, and eyes, allowing laterality preferences to occur naturally. Even so, whether we use our left or right side differently for a particular activity depends on many factors (Coren, 1993). We can simultaneously use our hands for different purposes, contributing to how strongly we prefer our left or right hands for a specific activity (Provins and Magliaro, 1993). In other words, an individual who writes with their right hand may not necessarily throw with their right hand. Similarly, most humans have uneven eyesight (Ehrenstein et al., 2005; Dupierrix et al., 2014). Purely studying handedness provides a biased perspective of laterality preferences, as proportions of right-handedness are far more polarizing than other measures of laterality (Germono, 1965; Tran et al., 2014). These variations in how an individual balances left- or right-handed tendencies across different measures are often overlooked in handedness research despite studies indicating notable differences (Good et al., 2001; Sun and Walsh, 2006; Gut et al., 2007). Previous studies have shown how and when handedness is measured greatly affects neuroscience studies on brain–handedness associations (Tejavibulya et al., 2024). Thus, genuinely capturing how lateral preferences shape the brain's functional organization requires assessments beyond handedness. Though our findings are only based on one measure of body laterality, further research using other laterality measures will be needed to confirm our results.

Despite recent advancements, left-handed individuals remain excluded mainly from human neuroimaging studies. Research has yet to determine effective methods for controlling these differences in brain function and when these differences matter to the question. Nevertheless, this large-scale exclusion is counter to broader inclusive best practices in neuroimaging (Ricard et al., 2023; Wu et al., 2024). Studies like ours that characterize the associations between handedness, footedness, eyedness, and other laterality preferences and brain function are the first step in better involving left-handed individuals in neuroimaging studies.

We showed that footedness connectivity profiles converge onto that of handedness for the older but not the younger cohort. Overall, an overlooked aspect of studies on lateral preferences in the brain is the role of developmental trajectories and critical periods (Gabbard and Iteya, 1996). The development trajectories of laterality preferences occur on distinct timelines for different body parts (Fig. 3). Speculatively, these results may offer insights into potential comorbidities between handedness and psychiatric conditions (Logue et al., 2015; Mallet et al., 2022; Mundorf et al., 2024). Adolescence is a sensitive and critical period in brain maturation, during which the risk for mental illness is significantly heightened (Chambers et al., 2003; Casey and Jones, 2010; Nixon and McClain, 2010; Casey et al., 2011; Reynolds et al., 2019). Similarly, our results suggest that it may also be a critical period for laterality preferences and their associations in the brain. Further investigation into the associations between laterality preferences, mental health, and brain development may be warranted.

Our study has several notable strengths. We investigated how different measures of body laterality, beyond handedness, differentially affect the brain's functional organization. We also used large cohorts with harmonized imaging protocols and sidedness measures. Nevertheless, our study has several limitations. One limitation of our work is the precision of measures of footedness and eyedness. In contrast to handedness, which was summed over several questions, they were reduced to a single preference question. In particular, for eyedness, participants were asked which eye individuals prefer to remain open if one were to look through a telescope. While it was the standard measure in two human connectome projects, it may be a poor measure of eyedness. Other measures of eyedness will likely give strong associations with the connectome. Thus, while we failed to show a significant eyedness association with functional connectivity, we cannot conclude for certain that brain–eyedness associations do not exist. While the brain–footedness differences between cohorts suggest a development change, we lack longitudinal data over childhood to fully elucidate potential critical periods. Many studies over different generations are likely needed to map out generational effects fully. Longitudinal datasets [such as the Adolescent Brain Cognitive Development Study (Casey et al., 2018)] may represent a starting point for further investigations.

Lateral preferences are associated with the brain's functional organization. However, studies associating brain connectivity to these predominant preferences have often been oversimplified to handedness. Although approximately 90% of the population is right-handed, other lateral preferences—like footedness and eyedness—show much lower and more varied distributions. Our study provides a broader view of lateral preferences and functional connectivity, offering a more nuanced view. Characterizing sidedness associations with whole-brain connectomes provides a starting point for understanding how and when handedness, footedness, and eyedness need to be accounted for in fMRI analyses.

Footnotes

  • The authors declare no competing financial interests.

  • This work was supported by the Gruber Foundation (TGF), National Institute of Mental Health (T32GM007205), National Institutes of Health (K23AG059919), Alzheimer's Association (2019-AACSF-644153), Women’s Health Research at Yale, Yale School of Medicine, and National Institute of Mental Health (R01 MH121095).

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

References

  1. ↵
    1. Angelucci F,
    2. De Bartolo P,
    3. Gelfo F,
    4. Foti F,
    5. Cutuli D,
    6. Bossù P,
    7. Caltagirone C,
    8. Petrosini L
    (2009) Increased concentrations of nerve growth factor and brain-derived neurotrophic factor in the rat cerebellum after exposure to environmental enrichment. Cerebellum 8:499–506. https://doi.org/10.1007/s12311-009-0129-1
    OpenUrlCrossRefPubMed
  2. ↵
    1. Ashida R,
    2. Walsh P,
    3. Brooks JCW,
    4. Cerminara NL,
    5. Apps R,
    6. Edwards RJ
    (2022) Sensory and motor electrophysiological mapping of the cerebellum in humans. Sci Rep 12:177. https://doi.org/10.1038/s41598-021-04220-9 pmid:34997137
    OpenUrlPubMed
  3. ↵
    1. Bookheimer SY, et al.
    (2019) The lifespan human connectome project in aging: an overview. Neuroimage 185:335–348. https://doi.org/10.1016/j.neuroimage.2018.10.009 pmid:30332613
    OpenUrlCrossRefPubMed
  4. ↵
    1. Bourassa DC,
    2. McManus IC,
    3. Bryden MP
    (1996) Handedness and eye-dominance: a meta-analysis of their relationship. Laterality 1:5–34. https://doi.org/10.1080/713754206
    OpenUrlCrossRefPubMed
  5. ↵
    1. Casey BJ,
    2. Jones RM,
    3. Somerville LH
    (2011) Braking and accelerating of the adolescent brain. J Res Adolesc 21:21–33. https://doi.org/10.1111/j.1532-7795.2010.00712.x pmid:21475613
    OpenUrlCrossRefPubMed
  6. ↵
    1. Casey BJ, et al.
    (2018) The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev Cogn Neurosci 32:43–54. https://doi.org/10.1016/j.dcn.2018.03.001 pmid:29567376
    OpenUrlCrossRefPubMed
  7. ↵
    1. Casey BJ,
    2. Jones RM
    (2010) Neurobiology of the adolescent brain and behavior: implications for substance use disorders. J Am Acad Child Adolesc Psychiatry 49:1189–1201. https://doi.org/10.1016/j.jaac.2010.08.017 pmid:21093769
    OpenUrlCrossRefPubMed
  8. ↵
    1. Chambers RA,
    2. Taylor JR,
    3. Potenza MN
    (2003) Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am J Psychiatry 160:1041–1052. https://doi.org/10.1176/appi.ajp.160.6.1041 pmid:12777258
    OpenUrlCrossRefPubMed
  9. ↵
    1. Coren S
    (1993) The lateral preference inventory for measurement of handedness, footedness, eyedness, and earedness: norms for young adults. Bull Psychon Soc 31:1–3. https://doi.org/10.3758/bf03334122
    OpenUrlCrossRef
  10. ↵
    1. Dassonville P,
    2. Zhu XH,
    3. Uurbil K,
    4. Kim SG,
    5. Ashe J
    (1997) Functional activation in motor cortex reflects the direction and the degree of handedness. Proc Natl Acad Sci U S A 94:14015–14018. https://doi.org/10.1073/pnas.94.25.14015 pmid:9391144
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Deep-Soboslay A,
    2. Hyde TM,
    3. Callicott JP,
    4. Lener MS,
    5. Verchinski BA,
    6. Apud JA,
    7. Weinberger DR,
    8. Elvevåg B
    (2010) Handedness, heritability, neurocognition and brain asymmetry in schizophrenia. Brain 133:3113–3122. https://doi.org/10.1093/brain/awq160 pmid:20639549
    OpenUrlCrossRefPubMed
  12. ↵
    1. Dupierrix E,
    2. de Boisferon AH,
    3. Méary D,
    4. Lee K,
    5. Quinn PC,
    6. Di Giorgio E,
    7. Simion F,
    8. Tomonaga M,
    9. Pascalis O
    (2014) Preference for human eyes in human infants. J Exp Child Psychol 123:138–146. https://doi.org/10.1016/j.jecp.2013.12.010 pmid:24581972
    OpenUrlPubMed
  13. ↵
    1. Ehrenstein WH,
    2. Arnold-Schulz-Gahmen BE,
    3. Jaschinski W
    (2005) Eye preference within the context of binocular functions. Graefes Arch Clin Exp Ophthalmol 243:926–932. https://doi.org/10.1007/s00417-005-1128-7
    OpenUrlCrossRefPubMed
  14. ↵
    1. Elias LJ,
    2. Bryden MP,
    3. Bulman-Fleming MB
    (1998) Footedness is a better predictor than is handedness of emotional lateralization. Neuropsychologia 36:37–43. https://doi.org/10.1016/S0028-3932(97)00107-3
    OpenUrlCrossRefPubMed
  15. ↵
    1. Esteves M,
    2. Ganz E,
    3. Sousa N,
    4. Leite-Almeida H
    (2021) Asymmetrical brain plasticity: physiology and pathology. Neuroscience 454:3–14. https://doi.org/10.1016/j.neuroscience.2020.01.022
    OpenUrl
  16. ↵
    1. Faurion A,
    2. Cerf B,
    3. Van De Moortele PF,
    4. Lobel E,
    5. Mac Leod P,
    6. Le Bihan D
    (1999) Human taste cortical areas studied with functional magnetic resonance imaging: evidence of functional lateralization related to handedness. Neurosci Lett 277:189–192. https://doi.org/10.1016/S0304-3940(99)00881-2
    OpenUrlCrossRefPubMed
  17. ↵
    1. Friede RL
    (1973) Dating the development of human cerebellum. Acta Neuropathol 23:48–58. https://doi.org/10.1007/BF00689004
    OpenUrlCrossRefPubMed
  18. ↵
    1. Gabbard C,
    2. Iteya M
    (1996) Foot laterality in children, adolescents, and adults. Laterality 1:199–205. https://doi.org/10.1080/713754236
    OpenUrlPubMed
  19. ↵
    1. Germono JF
    (1965) A comparative study of the performances of stutterers and nonstutterers on tests of handedness, eyedness, footedness, and earedness.
  20. ↵
    1. Good CD,
    2. Johnsrude I,
    3. Ashburner J,
    4. Henson RN,
    5. Friston KJ,
    6. Frackowiak RS
    (2001) Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage 14:685–700. https://doi.org/10.1006/nimg.2001.0857
    OpenUrlCrossRefPubMed
  21. ↵
    1. Gut M,
    2. Urbanik A,
    3. Forsberg L,
    4. Binder M,
    5. Rymarczyk K,
    6. Sobiecka B,
    7. Kozub J,
    8. Grabowska A
    (2007) Brain correlates of right-handedness. Acta Neurobiol Exp 67:43–51. https://doi.org/10.55782/ane-2007-1631
    OpenUrlPubMed
  22. ↵
    1. Habib M,
    2. Gayraud D,
    3. Oliva A,
    4. Regis J,
    5. Salamon G,
    6. Khalil R
    (1991) Effects of handedness and sex on the morphology of the corpus callosum: a study with brain magnetic resonance imaging. Brain Cogn 16:41–61. https://doi.org/10.1016/0278-2626(91)90084-L
    OpenUrlCrossRefPubMed
  23. ↵
    1. Hammond G
    (2002) Correlates of human handedness in primary motor cortex: a review and hypothesis. Neurosci Biobehav Rev 26:285–292. https://doi.org/10.1016/S0149-7634(02)00003-9
    OpenUrlCrossRefPubMed
  24. ↵
    1. Harms MP, et al.
    (2018) Extending the human connectome project across ages: imaging protocols for the lifespan development and aging projects. Neuroimage 183:972–984. https://doi.org/10.1016/j.neuroimage.2018.09.060 pmid:30261308
    OpenUrlCrossRefPubMed
  25. ↵
    1. Hatta T
    (2007) Handedness and the brain: a review of brain-imaging techniques. Magn Reson Med Sci 6:99–112. https://doi.org/10.2463/mrms.6.99
    OpenUrlCrossRefPubMed
  26. ↵
    1. Hirnstein M,
    2. Hugdahl K
    (2014) Excess of non-right-handedness in schizophrenia: meta-analysis of gender effects and potential biases in handedness assessment. Br J Psychiatry 205:260–267. https://doi.org/10.1192/bjp.bp.113.137349
    OpenUrlAbstract/FREE Full Text
  27. ↵
    1. Hodel AS
    (2018) Rapid infant prefrontal cortex development and sensitivity to early environmental experience. Dev Rev 48:113–144. https://doi.org/10.1016/j.dr.2018.02.003 pmid:30270962
    OpenUrlCrossRefPubMed
  28. ↵
    1. Hoshiyama M,
    2. Kakigi R
    (1999) Changes of somatosensory evoked potentials during writing with the dominant and non-dominant hands. Brain Res 833:10–19. https://doi.org/10.1016/S0006-8993(99)01443-2
    OpenUrlCrossRefPubMed
  29. ↵
    1. Joshi A,
    2. Scheinost D,
    3. Okuda H,
    4. Belhachemi D,
    5. Murphy I,
    6. Staib LH,
    7. Papademetris X
    (2011) Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9:69–84. https://doi.org/10.1007/s12021-010-9092-8 pmid:21249532
    OpenUrlCrossRefPubMed
  30. ↵
    1. Jung P,
    2. Baumgärtner U,
    3. Magerl W,
    4. Treede R-D
    (2008) Hemispheric asymmetry of hand representation in human primary somatosensory cortex and handedness. Clin Neurophysiol 119:2579–2586. https://doi.org/10.1016/j.clinph.2008.04.300
    OpenUrlCrossRefPubMed
  31. ↵
    1. Klar AJ
    (1999) Genetic models for handedness, brain lateralization, schizophrenia, and manic-depression. Schizophr Res 39:207–218. https://doi.org/10.1016/S0920-9964(99)00075-4
    OpenUrlCrossRefPubMed
  32. ↵
    1. Klöppel S,
    2. Mangin J-F,
    3. Vongerichten A,
    4. Frackowiak RSJ,
    5. Siebner HR
    (2010) Nurture versus nature: long-term impact of forced right-handedness on structure of pericentral cortex and basal ganglia. J Neurosci 30:3271–3275. https://doi.org/10.1523/JNEUROSCI.4394-09.2010 pmid:20203186
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Knecht S,
    2. Dräger B,
    3. Deppe M,
    4. Bobe L,
    5. Lohmann H,
    6. Flöel A,
    7. Ringelstein EB,
    8. Henningsen H
    (2000) Handedness and hemispheric language dominance in healthy humans. Brain 123:2512–2518. https://doi.org/10.1093/brain/123.12.2512
    OpenUrlCrossRefPubMed
  34. ↵
    1. Kusano T,
    2. Kurashige H,
    3. Nambu I,
    4. Moriguchi Y,
    5. Hanakawa T,
    6. Wada Y,
    7. Osu R
    (2021) Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation. Brain Struct Funct 226:2307–2319. https://doi.org/10.1007/s00429-021-02330-8 pmid:34236531
    OpenUrlPubMed
  35. ↵
    1. Lee IA,
    2. Preacher KJ
    (2013) Calculation for the test of the difference between two dependent correlations with one variable in common. Quant Psy. Available at: https://quantpsy.org/corrtest/corrtest2.htm
  36. ↵
    1. Logue DD,
    2. Logue RT,
    3. Kaufmann WE,
    4. Belcher HME
    (2015) Psychiatric disorders and left-handedness in children living in an urban environment. Laterality 20:249–256. https://doi.org/10.1080/1357650X.2014.961927
    OpenUrl
  37. ↵
    1. Lutkenhoff ES,
    2. Rosenberg M,
    3. Chiang J,
    4. Zhang K,
    5. Pickard JD,
    6. Owen AM,
    7. Monti MM
    (2014) Optimized brain extraction for pathological brains (optiBET). PLoS One 9:e115551. https://doi.org/10.1371/journal.pone.0115551 pmid:25514672
    OpenUrlCrossRefPubMed
  38. ↵
    1. Mallet J, et al.
    (2022) Handedness as a neurodevelopmental marker in schizophrenia: results from the FACE-SZ cohort. World J Biol Psychiatry 23:525–536. https://doi.org/10.1080/15622975.2021.2013094
    OpenUrl
  39. ↵
    1. Mundorf A, et al.
    (2024) Handedness in schizophrenia and affective disorders: a large-scale cross-disorder study. Eur Arch Psychiatry Clin Neurosci 275:767–783. https://doi.org/10.1007/s00406-024-01833-9 pmid:38914850
    OpenUrlPubMed
  40. ↵
    1. Muraleedharan A,
    2. Ragavan S,
    3. Devi R
    (2020) Are footedness and lateral postures better predictors of hemispheric dominance than handedness: a cross-sectional questionnaire-based clinical and pedigree study. J Neurosci Rural Pract 11:130–134. https://doi.org/10.1055/s-0039-3400699 pmid:32140016
    OpenUrlPubMed
  41. ↵
    1. Nixon K,
    2. McClain JA
    (2010) Adolescence as a critical window for developing an alcohol use disorder: current findings in neuroscience. Curr Opin Psychiatry 23:227–232. https://doi.org/10.1097/YCO.0b013e32833864fe pmid:20224404
    OpenUrlCrossRefPubMed
  42. ↵
    1. Ocklenburg S,
    2. Beste C,
    3. Arning L,
    4. Peterburs J,
    5. Güntürkün O
    (2014) The ontogenesis of language lateralization and its relation to handedness. Neurosci Biobehav Rev 43:191–198. https://doi.org/10.1016/j.neubiorev.2014.04.008
    OpenUrlCrossRefPubMed
  43. ↵
    1. Oldfield RC
    (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113. https://doi.org/10.1016/0028-3932(71)90067-4
    OpenUrlCrossRefPubMed
  44. ↵
    1. Perlaki G, et al.
    (2013) White-matter microstructure and language lateralization in left-handers: a whole-brain MRI analysis. Brain Cogn 82:319–328. https://doi.org/10.1016/j.bandc.2013.05.005
    OpenUrlCrossRefPubMed
  45. ↵
    1. Provins KA,
    2. Magliaro J
    (1993) The measurement of handedness by preference and performance tests. Brain Cogn 22:171–181. https://doi.org/10.1006/brcg.1993.1032
    OpenUrlCrossRefPubMed
  46. ↵
    1. Reynolds LM,
    2. Yetnikoff L,
    3. Pokinko M,
    4. Wodzinski M,
    5. Epelbaum JG,
    6. Lambert LC,
    7. Cossette M-P,
    8. Arvanitogiannis A,
    9. Flores C
    (2019) Early adolescence is a critical period for the maturation of inhibitory behavior. Cereb Cortex 29:3676–3686. https://doi.org/10.1093/cercor/bhy247 pmid:30295713
    OpenUrlCrossRefPubMed
  47. ↵
    1. Ricard JA,
    2. Parker TC,
    3. Dhamala E,
    4. Kwasa J,
    5. Allsop A,
    6. Holmes AJ
    (2023) Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. Nat Neurosci 26:4–11. https://doi.org/10.1038/s41593-022-01218-y
    OpenUrlCrossRefPubMed
  48. ↵
    1. Rosenberg MD,
    2. Zhang S,
    3. Hsu W-T,
    4. Scheinost D,
    5. Finn ES,
    6. Shen X,
    7. Constable RT,
    8. Li C-SR,
    9. Chun MM
    (2016) Methylphenidate modulates functional network connectivity to enhance attention. J Neurosci 36:9547–9557. https://doi.org/10.1523/JNEUROSCI.1746-16.2016 pmid:27629707
    OpenUrlAbstract/FREE Full Text
  49. ↵
    1. Rossi F,
    2. Bravin M,
    3. Buffo A,
    4. Fronte M,
    5. Savio T,
    6. Strata P
    (1997) Intrinsic properties and environmental factors in the regeneration of adult cerebellar axons. Prog Brain Res 114:283–296. https://doi.org/10.1016/S0079-6123(08)63370-1
    OpenUrlPubMed
  50. ↵
    1. Saadon-Grosman N,
    2. Angeli PA,
    3. DiNicola LM,
    4. Buckner RL
    (2022) A third somatomotor representation in the human cerebellum. J Neurophysiol 128:1051–1073. https://doi.org/10.1152/jn.00165.2022 pmid:36130164
    OpenUrlCrossRefPubMed
  51. ↵
    1. Sainburg RL,
    2. Duff SV
    (2006) Does motor lateralization have implications for stroke rehabilitation? J Rehabil Res Dev 43:311–322. https://doi.org/10.1682/jrrd.2005.01.0013 pmid:17041817
    OpenUrlCrossRefPubMed
  52. ↵
    1. Sato M,
    2. Stryker MP
    (2008) Distinctive features of adult ocular dominance plasticity. J Neurosci 28:10278–10286. https://doi.org/10.1523/JNEUROSCI.2451-08.2008 pmid:18842887
    OpenUrlAbstract/FREE Full Text
  53. ↵
    1. Scheinost D,
    2. Kwon SH,
    3. Lacadie C,
    4. Vohr BR,
    5. Schneider KC,
    6. Papademetris X,
    7. Constable RT,
    8. Ment LR
    (2017) Alterations in anatomical covariance in the prematurely born. Cereb Cortex 27:534–543. https://doi.org/10.1093/cercor/bhv248 pmid:26494796
    OpenUrlCrossRefPubMed
  54. ↵
    1. Shen X,
    2. Tokoglu F,
    3. Papademetris X,
    4. Constable RT
    (2013) Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82:403–415. https://doi.org/10.1016/j.neuroimage.2013.05.081 pmid:23747961
    OpenUrlCrossRefPubMed
  55. ↵
    1. Smith SM, et al.
    (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
    OpenUrlCrossRefPubMed
  56. ↵
    1. Solodkin A,
    2. Hlustik P,
    3. Noll DC,
    4. Small SL
    (2001) Lateralization of motor circuits and handedness during finger movements. Eur J Neurol 8:425–434. https://doi.org/10.1046/j.1468-1331.2001.00242.x
    OpenUrlCrossRefPubMed
  57. ↵
    1. Somerville LH, et al.
    (2018) The lifespan human connectome project in development: a large-scale study of brain connectivity development in 5–21 year olds. Neuroimage 183:456–468. https://doi.org/10.1016/j.neuroimage.2018.08.050 pmid:30142446
    OpenUrlCrossRefPubMed
  58. ↵
    1. Sörös P,
    2. Knecht S,
    3. Imai T,
    4. Gürtler S,
    5. Lütkenhöner B,
    6. Ringelstein EB,
    7. Henningsen H
    (1999) Cortical asymmetries of the human somatosensory hand representation in right- and left-handers. Neurosci Lett 271:89–92. https://doi.org/10.1016/S0304-3940(99)00528-5
    OpenUrlCrossRefPubMed
  59. ↵
    1. Steiger JH
    (1980) Tests for comparing elements of a correlation matrix. Psychol Bull 87:245–251. https://doi.org/10.1037/0033-2909.87.2.245
    OpenUrlCrossRef
  60. ↵
    1. Sun T,
    2. Walsh CA
    (2006) Molecular approaches to brain asymmetry and handedness. Nat Rev Neurosci 7:655–662. https://doi.org/10.1038/nrn1930
    OpenUrlCrossRefPubMed
  61. ↵
    1. Szaflarski JP,
    2. Binder JR,
    3. Possing ET,
    4. McKiernan KA,
    5. Ward BD,
    6. Hammeke TA
    (2002) Language lateralization in left-handed and ambidextrous people: fMRI data. Neurology 59:238–244. https://doi.org/10.1212/WNL.59.2.238
    OpenUrlCrossRefPubMed
  62. ↵
    1. Taylor PA,
    2. Reynolds RC,
    3. Calhoun V,
    4. Gonzalez-Castillo J,
    5. Handwerker DA,
    6. Bandettini PA,
    7. Mejia AF,
    8. Chen G
    (2023) Highlight results, don’t hide them: enhance interpretation, reduce biases and improve reproducibility. Neuroimage 274:120138. https://doi.org/10.1016/j.neuroimage.2023.120138 pmid:37116766
    OpenUrlCrossRefPubMed
  63. ↵
    1. Tejavibulya L,
    2. Peterson H,
    3. Greene A,
    4. Gao S,
    5. Rolison M,
    6. Noble S,
    7. Scheinost D
    (2022) Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. Neuroimage 252:119040. https://doi.org/10.1016/j.neuroimage.2022.119040 pmid:35272202
    OpenUrlCrossRefPubMed
  64. ↵
    1. Tejavibulya L,
    2. Horien C,
    3. Fredricks C,
    4. Ficek-Tani B,
    5. Westwater M,
    6. Scheinost D
    (2024) Brain-handedness associations depend on how and when handedness is measured. bioRxiv. Available at: https://www.nature.com/articles/s41598-025-94036-8
  65. ↵
    1. Tran US,
    2. Stieger S,
    3. Voracek M
    (2014) Evidence for general right-, mixed-, and left-sidedness in self-reported handedness, footedness, eyedness, and earedness, and a primacy of footedness in a large-sample latent variable analysis. Neuropsychologia 62:220–232. https://doi.org/10.1016/j.neuropsychologia.2014.07.027
    OpenUrlCrossRefPubMed
  66. ↵
    1. Van Essen DC, et al.
    (2012) The human connectome project: a data acquisition perspective. Neuroimage 62:2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 pmid:22366334
    OpenUrlCrossRefPubMed
  67. ↵
    1. Wiberg A,
    2. Ng M,
    3. Al Omran Y,
    4. Alfaro-Almagro F,
    5. McCarthy P,
    6. Marchini J,
    7. Bennett DL,
    8. Smith S,
    9. Douaud G,
    10. Furniss D
    (2019) Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics. Brain 142:2938–2947. https://doi.org/10.1093/brain/awz257 pmid:31504236
    OpenUrlCrossRefPubMed
  68. ↵
    1. Wilberg MJ,
    2. Thorson JT,
    3. Linton BC,
    4. Berkson J
    (2009) Incorporating time-varying catchability into population dynamic stock assessment models. Rev Fish Sci 18:7–24. https://doi.org/10.1080/10641260903294647
    OpenUrl
  69. ↵
    1. Wu KC,
    2. Hong S,
    3. Cross FL,
    4. Sypher I,
    5. McLoyd VC,
    6. Huntley ED,
    7. Hyde LW,
    8. Mitchell C,
    9. Monk CS
    (2024) Increasing diversity in neuroimaging research: participant-driven recommendations from a qualitative study of an under-represented sample. Dev Cogn Neurosci 70:101474. https://doi.org/10.1016/j.dcn.2024.101474 pmid:39541798
    OpenUrlPubMed
  70. ↵
    1. Zalesky A,
    2. Fornito A,
    3. Bullmore ET
    (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53:1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
    OpenUrlCrossRefPubMed

Synthesis

Reviewing Editor: Christine Portfors, Washington State University

Decisions are customarily a result of the Reviewing Editor and the peer reviewers coming together and discussing their recommendations until a consensus is reached. When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. The following reviewer(s) agreed to reveal their identity: NONE. Note: If this manuscript was transferred from JNeurosci and a decision was made to accept the manuscript without peer review, a brief statement to this effect will instead be what is listed below.

The reviewers agree that this is an interesting paper. The writing is clear. Some clarifications in the methods and results would be helpful.

The premise of the paper is very interesting and seeks to fill a gap in the field regarding the correlation between handedness, footedness, eyedness, and those individuals' connectomes. The introduction appears to be well researched and well cited. The discussion is also thoughtful. The overall writing of the paper was easy to read. Items that need addressing are below, regarding methods and results:

1. Please clarify some demographic data (is possible) of the HCP-A data set. In the HCP-A group, did you exclude any individuals that had suffered traumatic brain injuries, for example, or any individuals who were displaying neurodegenerative or cognitive impairments? Essentially ,did you take into account medical histories? Additionally, the age range of that data set is 36-100 years. Would it have been worth subdividing those age ranges into smaller subgroups? If not, why? Also, I assumed but it was not specifically stated that the behavioral measure of sidedness was collected from subjects whose data was used in HCP-D and HCP-A.

2. Figure 2 - I found this figure particularly confusing to understand. It was unclear to me how the figure is showing that edges differ between left and right-sided individuals. I think the confusion stems from the fact that you seemingly overlaid right sided dominant (purple) with the left sided dominant (green) on a single brain image for both groups. It is just hard to see. Also, would there be a way to quantify these edge differences and represent that in a graphical way? I also found it really confusing that the eyedness and one of the footedness images are blurred out, or gray scaled. If you are obscuring them, why include them? I do think they should be included. Finally, there is a typo in the figure legend itself - you indicate both left- and right-sided groups as represented by purple.

3. Figure 3 - The graphs should be bigger, and the coloring choices make it hard to distinguish between the foot and eye groups.

Figure 4- I had no problems with this figure and I thought it was the best laid out and easiest to follow of all of them.

Author Response

The reviewers agree that this is an interesting paper. The writing is clear. Some clarifications in the methods and results would be helpful.

The premise of the paper is very interesting and seeks to fill a gap in the field regarding the correlation between handedness, footedness, eyedness, and those individuals' connectomes. The introduction appears to be well researched and well cited. The discussion is also thoughtful. The overall writing of the paper was easy to read. Items that need addressing are below, regarding methods and results:

1. Please clarify some demographic data (is possible) of the HCP-A data set. In the HCP-A group, did you exclude any individuals that had suffered traumatic brain injuries, for example, or any individuals who were displaying neurodegenerative or cognitive impairments? Essentially ,did you take into account medical histories? No medical history was taken into account in the HCP-A dataset. The only exclusion criteria involved were with missing data and excessive head motion in the scanner.

Analyses were performed to control for age and sex, however.

Additionally, the age range of that data set is 36-100 years. Would it have been worth subdividing those age ranges into smaller subgroups? If not, why? While we attempted to subdivide 36-100 into finger grain groups, we were limited by the amount of data that would have been possible to fully examine these subgroups. Because the data were also skewed, we would have had many more subjects in certain bins than others where some bins were limited to as little as 2 or 3 subjects.

Also, I assumed but it was not specifically stated that the behavioral measure of sidedness was collected from subjects whose data was used in HCP-D and HCP-A.

That is correct.

2. Figure 2 - I found this figure particularly confusing to understand. It was unclear to me how the figure is showing that edges differ between left and right-sided individuals. I think the confusion stems from the fact that you seemingly overlaid right sided dominant (purple) with the left sided dominant (green) on a single brain image for both groups. It is just hard to see. Also, would there be a way to quantify these edge differences and represent that in a graphical way? I also found it really confusing that the eyedness and one of the footedness images are blurred out, or gray scaled. If you are obscuring them, why include them? I do think they should be included. Finally, there is a typo in the figure legend itself - you indicate both left- and right-sided groups as represented by purple.

We have changed Fig. 2 to be represented in a graphical way demonstrated by percent significant edges by networks as shown below. We have also removed the occlusions for insignificant results and have only noted this in the caption.

These changes have been reflected in lines 548-553:

Fig. 2: Edges significantly differ between left- and right-sided individuals as calculated by Network-Based Statistics. Circular bar plots showing percent of significant edges belonging to each canonical network for each sidedness measure (rows) as separated by datasets (columns). Each circular bar plot depicts percentages for left- (cyan for HCP-D and salmon for HCP-A) and right-sided edges (blue for HCP-D and red for HCP-A). Handedness yielded significance of p < 0.001 for both the HCP-A and the HCP-D, whereas footedness only yielded significance of p < 0.001 for the HCP-A. The remainder of the results show no significant differences.

Additionally, the main text has been updated to reflect these changes as follows in lines 197-203for handedness:

Notable patterns include prominent representations in prefrontal and cerebellar regions for both left- and right-handedness in both datasets. In the HCP-A, right-handedness was highly represented in the occipital network attributing to 29.58% of edges, whereas left-handedness was shown to be represented in the limbic network at 29.13% of edges (Fig. 2: top row, orange circular plot). In contrast, edges attributing to differences in handedness in the HCP-D were mostly confined within the cerebellar and prefrontal networks (Fig. 2: top row, blue circular plot).

For footedness in lines 210-217:

More specifically in the HCP-A, significant edges associated with right-footedness were distributed across multiple networks at 29.58%, 20.49%, and 21.31% for the occipital, motor, and limbic networks in right-handedness, respectively, while with left-handedness significant edges were more confined within the parietal and prefrontal networks at 24.62% and 29.66%, respectively. Although NBS results did not yield significance in the HCP-D, the limbic networks were associated with footedness differences with 30.92% and 29.31% of edges in left- and right-footedness, respectively, connected either within or between the limbic network.

For eyedness in lines 223-224:

No significant associations between eyedness and connectivity were observed for either group, however results are shown here for consistency.

3. Figure 3 - The graphs should be bigger, and the coloring choices make it hard to distinguish between the foot and eye groups.

These graphs have been modified to make the colors more distinguishable. Additionally, these plots have also been split up by pairs of sidedness measures to visualize differences between specific pairs with covariances accompanying each graph.

These changes have been reflected in lines 555-559:

Fig. 3: Effect size quantifications and correlations for all edges in each sidedness measure. (Top) Effect sizes (Cohen's d) for all 35,778 were calculated and plotted on a histogram for each sidedness measure and cohort separately. A wider histogram indicates that more edges show a larger effect size. (Bottom) Pairs of sidedness measures from top were further compared individually and accompanied with calculations of covariances for the HCP-D and the HCP-A, separately.

Figure 4- I had no problems with this figure and I thought it was the best laid out and easiest to follow of all of them.

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Functional Connectome Correlates of Laterality Preferences: Insights into Hand, Foot, and Eye Dominance across the Lifespan
Link Tejavibulya, Corey Horien, Carolyn Fredericks, Bronte Ficek, Margaret L. Westwater, Dustin Scheinost
eNeuro 5 June 2025, 12 (7) ENEURO.0580-24.2025; DOI: 10.1523/ENEURO.0580-24.2025
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