Biology subjects

Beckmann, C.

Publications and source records attributed to Beckmann, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Population-wide variation in connectopic organization of cerebral language hubs

The uniquely human capacity for language is supported by a distributed brain network with cerebral cortical hubs in the left inferior frontal gyrus (LIFG) and left superior temporal sulcus (LSTS). Individual differences in the graded spatial organization of function across these regions may provide sensitive phenotypes for behavioural and genetic association analyses, to better understand the biological underpinnings of language. In 41,437 UK Biobank participants we applied connectopic mapping to the LIFG and LSTS. This approach maps spatial changes across a region in terms of connectivity with the rest of the brain, based on resting state functional imaging. On average, we found that Brodmann Area (BA) 44, BA 45 and the posterior LSTS were strongly connected to the superior and inferior parietal lobe and the dorsal attention and frontoparietal networks, whereas BA 47 and the medial LSTS were strongly connected to superior frontal areas BA 8B and BA 9, and the default mode network more broadly. However, there was extensive population-wide variation in these graded connectopic patterns, which significantly mediated the effects of polygenic scores for reading ability and educational attainment on performance on a vocabulary task. Furthermore we identified 3 genomic loci displaying multivariate association patterns with these connectopic maps, one of which coincides with a long non-coding RNA, LINC01165, with potential relevance in human evolution. Overall, connectopic mapping produced fewer significant genetic association findings and lower heritability (range from 0.3 to 9.4%) than previous parcel-based approaches for mapping language network connectivity. These observations suggest higher genetic complexity and/or prominent roles for environmental or random developmental effects on these connectopic maps.

neuroscience↗

bioRxiv2026

Protocol Update: The Normative Modelling Paradigm for Computational Psychiatry

Normative Modelling ( brain growth charting) is now a well-established method for computational psychiatry and involves charting centiles of variation across a population in terms of mappings between biology and behavior, providing statistical inferences at the level of the individual. These models have helped the field to move away from case-control analysis toward individual-level analysis. Correspondingly, normative modelling has now been applied to chart brain development and ageing in many populations and has been used to quantify individual deviations across various neurological and psychiatric conditions. This has been supported by large-scale models that are openly accessible for diverse brain imaging modalities. As normative modelling continues to grow, several recent methodological developments, such as non-Gaussian models, longitudinal models, and federated learning, have been implemented in different software tools, including the Predictive Clinical Neuroscience toolkit (PCNtoolkit). In this protocol update, we provide: (i) a revised overview of this methodological landscape; (ii) an update to our 2022 standardised analytical protocol for normative modelling of neuroimaging data, including options for federated and longitudinal normative models; (iii) practical guidance suited to both novice and experienced practitioners supported by open-source code examples implemented in the refactored version of PCNtoolkit; and (iv) updated models for cortical thickness, surface area, volumetric data, functional connectivity and diffusion-weighted imaging for use by the community.

neuroscience↗

bioRxiv2026

ACPC-MAP: A protocol for manually aligning structural T1-weighted Magnetic Resonance Images to the Anterior Commissure - Posterior Commissure plane according to clinical standards.

Realigning structural MR images to stereotactic AC-PC space is a standard procedure that enhances anatomical consistency both within and across neuroimaging studies, allowing for precise spatial localization and reliable cross-subject comparisons in both clinical and research contexts. However, different versions of AC-PC spaces, stemming from varying definitions of the AC-PC axis as outlined in common stereotactic atlases like Talairach and Schaltenbrand, often lead to discrepancies between the neurosurgical and neuroimaging communities. Manual realignment of structural MR images to the clinically used version of the AC-PC plane is often necessary to validate the results of automated methods or to realign the image when these methods fail. Furthermore, manual realignment provides a critical ground truth for the development of new automated tools. However, such manual interventions are typically performed in a non-standardised manner by domain experts who possess the specialized knowledge of neuroanatomy and neuroimaging required to ensure accurate alignment. To address these challenges, we have developed and validated a standardized protocol for manually realigning structural MR images to the clinically used Schaltenbrand AC-PC plane, using a set of visual criteria to ensure accurate realignment. This protocol can be used to manually align images, verify results, validate existing automated methods, or generate ground truth data for developing new automated techniques.

neuroscience↗

bioRxiv2025

Characterizing Co-Circulating Respiratory Virus Genomic Diversity in Switzerland with Hybrid-Capture Sequencing and Phylogenetic Reconstructions: Insights into the 2023/24 Season

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built phylogenies for all viruses with more than 40 new high-quality genomes from this study. We detected 981 viruses and recovered 461 high-quality genomes, including 18 co-infections, from 437 PCR-positive and 6 PCR-negative samples. All viruses detected by PCR were also detected by sequencing in 56% of samples. The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

genetics↗

bioRxiv2025

The Art of Not Knowing: Accommodating Structured Missingness in Biomedical Research

Missing data remain a ubiquitous and critical challenge in large-scale clinical studies. Despite advances in imputation, most existing methods fail to address structured missingness, where data are missing according a deterministic pattern and which arise due to systematic patterns introduced by experimental design, site protocols, or cohort differences. These patterns violate key assumptions of most imputation algorithms, yet their impact is rarely evaluated. We demonstrate that structured missingness is a fundamental challenge to drawing valid inferences from standard imputation techniques. First, we present a comprehensive framework for understanding and accommodating the effects of structured missingness. Next, we show through simulations and real-world psychometric data with structured missingness, that widely used algorithms optimised for numerical precision (e.g., Extra Trees, AutoComplete) underperform due to site effects, while donor-based methods (e.g., MICE, hierarchical MICE) better preserve multivariate structure. We propose a novel hierarchical approach that provides optimal performance in simulated and experimental data. Finally, we show that commonly used accuracy metrics, such as mean squared error can obscure these failures, and are therefore inadequate for the evaluation of structured missingness. In contrast, other divergence-based metrics offer a more sensitive and interpretable alternative. We apply this approach to harmonising psychometric data across cohorts, which provides excellent item-level alignment across different instruments. Our study highlights the need for a paradigm shift in handling missing data within biomedical research, moving beyond conventional imputation frameworks to develop tools that can account for structured missingness. This shift is essential for ensuring reliable inference in multi-site clinical studies, precision medicine, and large-scale population analyses.

neuroscience↗

bioRxiv2025

The Neural Architecture of Dream Recall Frequency: Insights from Interindividual Variations in Brain Structure and Function

Dreaming represents a complex and universal aspect of human sleep, yet it remains an intriguing phenomenon, with the neural mechanisms underlying dream experiences and their frequency not fully understood. This study employs a multimodal neuroimaging approach, integrating quantitative multi-parameter mapping, diffusion tensor imaging, and resting-state functional MRI, to investigate the neural correlates of dream recall frequency (DRF) in a large cohort of 258 healthy individuals. By employing Linked Independent Component Analysis (LICA), we were able to discern distinctive patterns of brain structure and function that correlated with variations in DRF. Our findings elucidate a complex relationship between dream recall and brain microstructure integrity, particularly in white matter regions of the orbitofrontal cortex, parahippocampal gyrus, superior parietal lobule, and occipital cortex. Higher DRF was related to increased white matter microstructure integrity in these regions and decreased gray matter volume in occipital and temporal areas. In terms of functional measures, higher DRF was associated with reduced connectivity across a range of resting-state networks, including the default mode, visual, and dorsal attention networks. This was particularly evident in the right precuneus and posterior cingulate cortex. These results suggest that enhanced dream recall may be related to the organization of higher-order visual and cognitive processing areas, supporting a top-down model of dreaming. This study contributes to a more comprehensive understanding of the neural substrates underlying individual differences in dream recall, offering a foundation for future investigations into the neurobiology and causal relationships of dreaming.

neuroscience↗

bioRxiv2024

Linking functional and structural brain organisation with behaviour in healthy adults

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

neuroscience↗

bioRxiv2024

Consequences of Cancer on Zebrafish Danio rerio: Insights into Sex Determination, Sex Ratio, and Offspring Survival.

Offspring sex ratio has been proposed as an indicator of the risk of developing certain cancers in humans, but offspring sex ratio may also be a consequence of the disease. In this study, we delve into this subject using the fish Danio rerio as a model system. First, we explore whether inducing skin cancer at an early stage of the hosts life (embryonic stage) has the potential to influence sex determination and/or sex-specific mortality. Second, we investigate whether the sex ratio in offspring produced by tumor-bearing adult females differs from that of healthy females. Third, we compare the survival (until sexual maturity) of offspring produced by cancerous and non-cancerous females. We found that skin cancer did not influence sex ratio in both experiments. However, consistent with previous studies on other model systems, the survival of offspring from cancerous females was higher, suggesting that diseased females allocate more resources to current reproductive efforts compared to their healthy counterparts. This study makes a significant contribution to our understanding of the ecological and evolutionary consequences of host-tumor interactions in animals.

evolutionary biology↗

bioRxiv2023

Mapping Cerebellar Anatomical Heterogeneity in Mental and Neurological Illnesses

The cerebellum has been linked to motor coordination, cognitive and affective processing, in addition to a wide range of clinical illnesses. To enable robust quantification of individual cerebellar anatomy relative to population norms, we mapped the normative development and aging of the cerebellum across the lifespan using brain scans of > 54k participants. We estimated normative models at voxel-wise spatial precision, enabling integration with cerebellar atlases. Applying the normative models in independent samples revealed substantial heterogeneity within five clinical illnesses: autism spectrum disorder, mild cognitive impairment, Alzheimers disease, bipolar disorder, and schizophrenia. Notably, individuals with autism spectrum disorder and mild cognitive impairment exhibited increased numbers of both positive and negative extreme deviations in cerebellar anatomy, while schizophrenia and Alzheimers disease predominantly showed negative deviations. Finally, extreme deviations were associated with cognitive scores. Our results provide a voxel-wise mapping of cerebellar anatomy across the human lifespan and clinical illnesses, demonstrating cerebellums nuanced role in shaping human neurodiversity across the lifespan and in different clinical illnesses.

neuroscience↗

bioRxiv2023

Objective QC for diffusion MRI data: artefact detection using normative modelling

Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging derived phenotypes from the UK Biobank dataset. The considered features comprised 4 microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants.

neuroscience↗

bioRxiv2023

Gradient organisation of functional connectivity within resting state networks is present from 25 weeks gestation in the human fetal brain

During the third trimester of human gestation, the structure and function of the fetal brain is developing rapidly, laying the foundation for its connectivity framework across the lifespan. During this juncture, resting state functional MRI can be used to identify resting state networks (RSNs) which mature across gestation to resemble canonical RSNs at full term. However, the emergence of finer grain organisation of connectivity within these RSNs in the fetal brain is unknown. Using in-utero resting state fMRI, we performed connectopic mapping analysis to explore the presence of gradients in functional connectivity organisation of 11 cortical RSNs, known as connectopic maps in fetuses aged 25-37 weeks gestation (GW). We hypothesised that, if present, development of connectopic maps would be network specific in the third trimester of gestation, such that this property would be present within the earlier maturing primary sensory and motor networks before those associated with higher association function. In keeping with this, we found smooth connectopic maps in all of the studied RSNs from 25 GW, with the most spatially consistency across gestational age in the primary sensory and motor networks. Voxel-wise permutation testing of the connectopic maps identified local clusters of voxels within networks that significantly covaried with age, specifically in multisensory processing areas, suggesting multisensory processing may be developing during this period. Our analysis shows that functional gradient organisation is already established in the fetal brain and develops throughout gestation, which has strong implications for understanding how cortical organisation subserves the emergence of behaviour in the ensuing period.

neuroscience↗

bioRxiv2023

Using normative models pre-trained on cross-sectional data to evaluate longitudinal changes in neuroimaging data

Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully harness the potential of longitudinal neuroimaging data, we have to develop and refine methodologies that are adapted to longitudinal designs, considering the complex interplay between population variation and individual dynamics. We build on normative modelling framework, which enables the evaluation of an individuals position compared to a population standard. We extend this framework to evaluate an individuals longitudinal change compared to the longitudinal change reflected by the (population) standard dynamics. Thus, we exploit the existing normative models pre-trained on over 58,000 individuals and adapt the framework so that they can also be used in the evaluation of longitudinal studies. Specifically, we introduce a quantitative metric termed "z-diff" score, which serves as an indicator of a temporal change of an individual compared to a population standard. Notably, our framework offers advantages such as flexibility in dataset size and ease of implementation. To illustrate our approach, we applied it to a longitudinal dataset of 98 patients diagnosed with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and one year later. Compared to cross-sectional analyses, which showed global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time. Furthermore, this result was not observed when using more traditional methods of longitudinal analysis, making our approach more sensitive to temporal changes. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

neuroscience↗

bioRxiv2021

Supervised Phenotype Discovery from Multimodal Brain Imaging

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.

neuroscience↗

bioRxiv2021

Charting Brain Growth and Aging at High Spatial Precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

neuroscience↗

bioRxiv2021

Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ( latent indices) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

neuroscience↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗

bioRxiv2020

Attenuated Anticipation of Social and Monetary Rewards in Autism Spectrum Disorders

BackgroundReward processing has been proposed to underpin atypical social behavior, a core feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social rewards in ASD. Utilizing a large sample, we aimed to assess altered reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. MethodsFunctional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6-30.5 years) and 181 typically developing (TD) participants (7.6-30.8 years). ResultsAcross social and monetary reward anticipation, whole-brain analyses (p<0.05, family-wise error-corrected) showed hypoactivation of the right ventral striatum (VS) in ASD. Further, region of interest (ROI) analysis across both reward types yielded hypoactivation in ASD in both the left and right VS. Across delivery of social and monetary reward, hyperactivation of the VS in individuals with ASD did not survive correction for multiple comparisons. Reward type by diagnostic group interactions, and a dimensional analysis of autism trait scores were not significant during anticipation or delivery. Levels of attention-deficit/hyperactivity disorder (ADHD) symptoms did not affect reward processing in ASD. ConclusionsOur results do not support current theories linking atypical social interaction in ASD to specific alterations in processing of social rewards. Instead, they point towards a generalized hypoactivity of VS in ASD during anticipation of both social and monetary rewards. We suggest that this indicates attenuated subjective reward value in ASD independent of social content and ADHD symptoms.

neuroscience↗

bioRxiv2020

Towards robust and replicable sex differences in the intrinsic brain function of autism

BackgroundMarked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data. MethodsWe addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research (GENDAAR). ResultsDiscovery analyses in ABIDE revealed significant main effects across the intrinsic functional connectivity (iFC) of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples - EU-AIMS LEAP. LimitationsGiven the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability. ConclusionsAtypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies.

neuroscience↗