Neuroimage. Reports最新文献

{"title":"Exploring interhemispheric connectivity using the directional tract density patterns of the corpus callosum","authors":"Ali Demir ,&nbsp;H. Diana Rosas","doi":"10.1016/j.ynirp.2023.100174","DOIUrl":"10.1016/j.ynirp.2023.100174","url":null,"abstract":"<div><p>The corpus callosum (CC) is one of the most important interhemispheric white matter tracts that connects interrelated regions of the cerebral cortex. Its disruption has been investigated in previous studies and has been found to play an important role in several neurodegenerative disorders. Currently available methods to assess the interhemispheric connectivity of the CC have several limitations: i) they require the <em>a priori</em> identification of specific cortical regions as targets or seeds, ii) they are limited by the characterization of only small components of the structure, primarily voxels that constitute the mid-sagittal slice, and iii) they use global measures of microstructural integrity, which provide only limited characterization. In order to address some of these limitations, we developed a novel method that enables the characterization of white matter tracts covering the structure of CC, from the mid-sagittal plane to corresponding regions of cortex, using directional tract density patterns (dTDPs). We demonstrate that different regions of CC have distinctive dTDPs that reflect a unique regional topology. We conducted a pilot study using this approach to evaluate two different datasets collected from healthy subjects, and we demonstrate that this method is reliable, reproducible, and independent of diffusion acquisition parameters, suggesting its potential applicability to clinical applications.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/63/nihms-1909522.PMC10310067.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9743318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex differences in brain volumes and psychological distress: The first hundred brains cohort of the longitudinal adolescent brain study","authors":"Jacob M. Levenstein ,&nbsp;Christina Driver ,&nbsp;Amanda Boyes ,&nbsp;Marcella Parker ,&nbsp;Zack Shan ,&nbsp;Jim Lagopoulos ,&nbsp;Daniel F. Hermens","doi":"10.1016/j.ynirp.2023.100167","DOIUrl":"10.1016/j.ynirp.2023.100167","url":null,"abstract":"<div><p>Neurodevelopment during early childhood and adolescence are recognised as critical periods, with potential life-long lasting impacts on mental health and wellbeing. The time-frame of these neurodevelopmental changes also correspond to one in five individuals aged 9–17 years old being diagnosed with a mental health condition. Furthermore, sex-based differences in the diagnosed prevalence of mental health conditions are also well characterised and can be leveraged to differentiate development of brain structures between sexes throughout childhood and adolescence. During adolescence, early observed mental health symptoms, alongside measures of brain development, may provide utility toward understanding both the onset timing of various mental conditions, and a neurobiological explanation for disproportionate prevalence's among sexes. This study aims to determine sex differences in psychological distress levels and structural brain volume relationships in early adolescents. To address this question, we first present and then utilise the ‘first hundred brains’ (FHB) cohort, a multimodal dataset of 12-to-13 year-olds individuals enrolled in the Longitudinal Adolescent Brain Study (LABS). The FHB dataset consists of 101 unique individuals (47 female), aged 13.01 ± 0.55 years. Psychological distress was measured using the Kessler-10, a self-report questionnaire probing recent experiences of anxiety and depression symptoms. All participants underwent 3T MRI brain scans. T1-weighted structural scans were processed using FreeSurfer's Sequence Adaptive Multimodal segmentation pipeline, with volume measurements from 39 regions of interest included in the analyses. Findings revealed that compared to age matched males, early adolescent females have significantly higher psychological distress as well as significantly larger hippocampi and ventral diencephalon, bilaterally. Correlational analyses revealed a significant positive association between psychological distress scores and right amygdala volumes for males, but not in females, or the combined cohort. In this initial analysis of the FHB dataset, we have identified significant sex differences in psychological distress, brain volumes, and the relationships between these two metrics. With the peak age-of-onset for many psychiatric disorders occurring during adolescence, research focused on youth mental health vulnerability and opportunity for early detection, prevention and improvement is vitally important.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49410211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural processing of goal and non-goal-directed movements on the smartphone","authors":"Ruchella Kock,&nbsp;Enea Ceolini,&nbsp;Lysanne Groenewegen,&nbsp;Arko Ghosh","doi":"10.1016/j.ynirp.2023.100164","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100164","url":null,"abstract":"<div><p>The discrete behavioral events captured on the smartphone touchscreen may help unravel real-world neural processing. We find that neural signals (EEG) surrounding a touchscreen event show a distinctly contralateral motor preparation followed by visual processing, and the consolidation of information. We leveraged these events in conjunction with kinematic recordings of the thumb and an artificial neural network to separate highly similar movements according to whether they resulted in a smartphone touch (goal-directed) or not (non-goal-directed). Despite their kinematic similarity, the signatures of neural control of movement and the post-movement processing were substantially dampened for the non-goal-directed movements, and these movements uniquely evoked error-related signals. We speculate that these apparently unnecessary movements are common in the real world and although inconsequential the brain provides limited motor preparation and tracks the action outcome. The neural signals surrounding discrete smartphone events can enable the study of neural processes that are difficult to capture in conventional laboratory-based tasks.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50173411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Successful reproduction of a large EEG study across software packages","authors":"Aya Kabbara ,&nbsp;Nina Forde ,&nbsp;Camille Maumet ,&nbsp;Mahmoud Hassan","doi":"10.1016/j.ynirp.2023.100169","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100169","url":null,"abstract":"<div><p>As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results.</p><p>In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al., 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting event-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between software packages. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100169"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50173414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting neurodevelopmental outcomes from neonatal cortical microstructure: A conceptual replication study","authors":"Andrea Gondová ,&nbsp;Sara Neumane ,&nbsp;Yann Leprince ,&nbsp;Jean-François Mangin ,&nbsp;Tomoki Arichi ,&nbsp;Jessica Dubois","doi":"10.1016/j.ynirp.2023.100170","DOIUrl":"10.1016/j.ynirp.2023.100170","url":null,"abstract":"<div><p>Machine learning combined with large-scale neuroimaging databases has been proposed as a promising tool for improving our understanding of the behavioural emergence and early prediction of the neurodevelopmental outcome. A recent example of this strategy is a study by Ouyang et al. (2020) which suggested that cortical microstructure quantified by diffusion MRI through fractional anisotropy (FA) metric in preterm and full-term neonates can lead to effective prediction of language and cognitive outcomes at 2 years of corrected age as assessed by <em>Bayley Scales of Infant and Toddler Development, Third Edition</em> (BSID-III) composite scores. Given the important need for robust and generalisable tools which can reliably predict the neurodevelopmental outcome of preterm infants, we aimed to replicate the conclusions of this work using a larger independent dataset from the <em>developing Human Connectome Project</em> dataset (dHCP, third release) with early MRI and BSID-III evaluation at 18 months of corrected age. We then aimed to extend the validation of the proposed predictive pipeline through the study of different cohorts (the largest one included 295 neonates, with gestational age between 29 and 42 week and post-menstrual age at MRI between 31 and 45 weeks). This allowed us to evaluate whether some limitations of the original study (mainly small sample size and limited variability in the input and output features used in the predictive models) would influence the prediction results. In contrast to the original study that inspired the current work, our prediction results did not outcompete the random levels. Furthermore, these negative results persisted even when the study settings were expanded. Our findings suggest that the cortical microstructure close to birth described by DTI-FA measures might not be sufficient for a reliable prediction of BSID-III scores during toddlerhood, at least in the current setting, i.e. generally older cohorts and a different processing pipeline. Our inability to conceptually replicate the results of the original study is in line with the previously reported replicability issues within the machine learning field and demonstrates the challenges in defining the good set of practices for the implementation and validation of reliable predictive tools in the neurodevelopmental (and other) fields.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48731985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain working memory network indices as landmarks of intelligence","authors":"Mohammadreza Khodaei ,&nbsp;Paul J. Laurienti ,&nbsp;Dale Dagenbach ,&nbsp;Sean L. Simpson","doi":"10.1016/j.ynirp.2023.100165","DOIUrl":"10.1016/j.ynirp.2023.100165","url":null,"abstract":"<div><p>Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22–35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant ass","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/53/01/nihms-1909521.PMC10327823.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10167693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population modeling with machine learning can enhance measures of mental health - Open-data replication","authors":"Ty Easley ,&nbsp;Ruiqi Chen ,&nbsp;Kayla Hannon ,&nbsp;Rosie Dutt ,&nbsp;Janine Bijsterbosch","doi":"10.1016/j.ynirp.2023.100163","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100163","url":null,"abstract":"<div><p>Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotype's distribution through undersampling of majority scores, and identified data-driven subtypes to investigate the impact of between-participant heterogeneity. Our results replicated prior results from Dadi et al. (2021) in a larger sample. Each data manipulation further led to small but consistent improvements in prediction accuracy, which were largely additive when combining multiple data manipulations. Combining data manipulations (i.e., extended fMRI features, averaged target phenotype, balanced target phenotype distribution) led to a three-fold increase in prediction accuracy for fluid intelligence compared to prior work. These findings highlight the benefit of several relatively easy and low-cost data manipulations, which may positively impact future work.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50173413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Music interventions in 132 healthy older adults enhance cerebellar grey matter and auditory working memory, despite general brain atrophy","authors":"Damien Marie ,&nbsp;Cécile A.H. Müller ,&nbsp;Eckart Altenmüller ,&nbsp;Dimitri Van De Ville ,&nbsp;Kristin Jünemann ,&nbsp;Daniel S. Scholz ,&nbsp;Tillmann H.C. Krüger ,&nbsp;Florian Worschech ,&nbsp;Matthias Kliegel ,&nbsp;Christopher Sinke ,&nbsp;Clara E. James","doi":"10.1016/j.ynirp.2023.100166","DOIUrl":"10.1016/j.ynirp.2023.100166","url":null,"abstract":"<div><p>Normal aging is associated with brain atrophy and cognitive decline. Working memory, involved in cognitive functioning and daily living, is particularly affected. Music training gained momentum in research on brain plasticity and possible transfer effects of interventions on working memory, especially in the context of healthy aging. This longitudinal voxel-based morphometry study evaluated effects of 6-month music interventions on grey matter volume plasticity and auditory working memory performance in 132 healthy older adults. This study is part of a randomized controlled trial comparing two interventions: piano practice (experimental group) and musical culture (musical listening awareness, active control). We report significant grey matter volume increase at whole-brain level in the caudate nucleus, Rolandic operculum and inferior cerebellum when merging both groups, but no group differences. Cerebellar grey matter increase, training intensity metrics and sleep were positively associated with tonal working memory improvement. Digit Span Backward verbal working memory performance also increased. Using region of interest analyses, we showed a group difference in the right primary auditory cortex grey matter volume, decreasing in the musical group while staying stable in the piano group. In contrast, a significant 6-month whole-brain atrophy pattern consistent with longer-term investigations of the aging brain was revealed. We argue that education for seniors should become a major policy priority in the framework of healthy aging, to promote brain plasticity and cognitive reserve, through stimulating group interventions such as music-making and active listening.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43751261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associative memory is more strongly predicted by age-related differences in the prefrontal cortex than medial temporal lobes","authors":"Tiago Guardia ,&nbsp;Negar Mazloum-Farzaghi ,&nbsp;Rosanna K. Olsen ,&nbsp;Kamen A. Tsvetanov ,&nbsp;Karen L. Campbell","doi":"10.1016/j.ynirp.2023.100168","DOIUrl":"10.1016/j.ynirp.2023.100168","url":null,"abstract":"<div><p>It is well established that episodic memory declines with age and one of the primary explanations for this decline is an age-related impairment in the ability to form new associations. At a neural level, both the medial temporal lobe (MTL) and lateral prefrontal cortex (PFC) are thought to be critical for associative memory, and grey matter volume loss in these regions has been associated with age-related declines in episodic memory. While some recent work has compared the relative contributions of grey matter volume in MTL and PFC regions to item and associative memory, studies investigating the unique and shared contributions of age-related differences in the MTL and PFC to memory differences are still rare. In this study, we use a lifespan approach to examine the relationship between grey matter volume within substructures of the MTL and PFC on the one hand and item and associative memory on the other. To this end, we used data from over 300 healthy individuals uniformly spread across the adult lifespan from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) and tested the multivariate relationship between grey matter volumes and item/associative memory scores using canonical correlation analysis. We show that structures of the PFC alone predict memory performance better than either structures of the MTL alone or PFC and MTL combined. Moreover, our results also indicate that grey matter volume in the inferior frontal gyrus - pars opercularis, superior frontal gyrus, and middle frontal gyrus relates most strongly to memory (particularly associative memory, which loaded higher than item memory) and this effect persists when controlling for age and education. Finally, we also show that the relationship between frontal grey matter volume and memory is not moderated by age or sex. Taken together, these findings emphasize the critical role of the frontal lobes, and the control processes they subserve, in determining the effects of age on associative memory.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 2","pages":"Article 100168"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45988155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hand preference and the corpus callosum: Is there really no association?","authors":"Nora Raaf,&nbsp;René Westerhausen","doi":"10.1016/j.ynirp.2023.100160","DOIUrl":"https://doi.org/10.1016/j.ynirp.2023.100160","url":null,"abstract":"<div><p>Originating from a series of morphometric studies conducted in the 1980s, it appears a widely held belief in cognitive neuroscience that the corpus callosum is larger in left or mixed handers than in right handers (RH). However, a recent meta-analysis challenges this belief by not finding significant differences in corpus callosum size between handedness groups. Yet, relying on the available published data, the meta-analysis was not able to account for a series of factors potential influencing its outcome, such as confounding effects of brain size differences and a restricted spatial resolution of previous callosal segmentation strategies. To address these remaining questions, we here analysed N = 1057 participants' midsagittal corpus callosum of from the Human Connectome Project (HCP 1200 Young Adults) to compare handedness groups based on consistency (e.g., consistent RH vs. mixed handers, MH) and direction of hand preference (e.g., dominant RH vs. dominant left handers). A possible relevance of brain-size differences was addressed by analysing callosal variability by both using forebrain volume (FBV) as covariate and utilising relative area (callosal area/thickness divided by FBV) as a dependent variable. Callosal thickness was analysed at 100 measuring points along the structure to achieve high spatial resolution to detect subregional effects. However, neither of the conducted analyses was able to find significant handedness-related differences in the corpus callosum and the respective effect-sizes estimates were small. For example, comparing MH and consistent RH, the effect sizes for difference in callosal area were below a Cohen's <em>d</em> = 0.1 (irrespective of how FBV was included), and narrow confidence intervals allowed to exclude effects above |<em>d</em>| = 0.2. Analysing thickness, effect sizes were below <em>d</em> = 0.2 with confidence intervals not extending above |<em>d</em>| = 0.3. In this, the possible range of population effect sizes of hand preference on callosal morphology appears well below the effects commonly reported for factors like age, sex, or brain size. Effects on cognition or behaviour accordingly can be considered small, questioning the common practise to attribute performance differences between handedness groups to differences in callosal architecture.</p></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"3 1","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50201112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}