Biology subjects

Eickhoff, S. B.

Publications and source records attributed to Eickhoff, S. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Hormonal Modulation of Subjective Sleep Quality and Perivascular Space Volume Across the Menstrual Cycle: An Observational Dense-Sampling Study

Background Hormonal fluctuations during the menstrual cycle (and their suppression) are accompanied by altered mood, cognition, and sleep, yet their relationship to subjective sleep quality and glymphatic function - a sleep-dependent brain waste-clearance pathway that involves perivascular spaces (PVS) - is unclear. Methods We examined daily oestradiol, progesterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH), subjective sleep quality, and MRI-derived PVS volume (PVSV) in a single healthy woman across two 30-day periods: during her physiological menstrual cycle and, one year later, under oral contraceptive pill (OCP) use. We employed linear regression models to examine whether hormones are associated with sleep quality and PVSV, and whether they moderate the sleep quality-PVSV association during the physiological menstrual cycle. Subsequently, we conducted permutation-based Spearman rank correlations to assess whether day-to-day changes in sleep quality were associated with corresponding changes in PVSV across the physiological menstrual cycle and OCP use. False discovery rate (FDR) correction was used to control for multiple comparisons. Findings Higher LH levels were associated with poorer sleep quality during the physiological cycle (b=2.15, 95% CI 0.64 to 3.67, pFDR=0.028), while FSH moderated the sleep quality-PVSV association (b=-92.98, 95% CI -160.15 to -25.81, pFDR=0.034), suggesting that lower FSH was associated with lower PVSV, whereas this association reversed at elevated FSH levels characteristic of the periovulatory window. During the physiological menstrual cycle, day-to-day decreases in sleep quality were associated with synchronous increases in PVSV, driven by centrum semiovale PVSV ({rho}=0.55, 95% CI 0.30 to 0.67, pperm=0.0095). This association was absent during OCP use and differed between hormonal milieus ({Delta} {rho}=0.72, p=0.040). Interpretation Ovulation is characterized by poor sleep quality and reduced PVSV, potentially reflecting noradrenergic modulation. The association between sleep quality and PVSV day-to-day changes is shaped by the hormonal milieu, being absent under OCP. PVSV increase associated with poor sleep may reflect elevated sleep pressure promoting vasomotor-mediated cerebrospinal fluid inflow.

neuroscience↗

bioRxiv2026

Distinct spatial associations of adversity with hippocampal macro- and microstructure in early adolescence

Youth adversity has been associated with alterations in hippocampal structure; however, it remains unclear whether different forms of adversity relate distinctly to its organization across the anterior-posterior and proximal-distal axes. Here, we investigated the associations of different types of adversity with multiple structural properties of the hippocampus in 5,263 early adolescents from the ABCD Study. Hippocampal macrostructure was characterized using volume, thickness, and gyrification, whereas T1w/T2w ratio served as an in vivo proxy for microstructure. Adversity was assessed at the family level using questionnaires on family environment and parenting, and at the socioeconomic level using income-to-needs ratio and neighborhood disadvantage measured by the Area Deprivation Index. Associations were examined for each adversity type separately in multi-variate analyses and for cumulative adversity exposure in univariate models. Hippocampal features were obtained using HippUnfold, an advanced automatic segmentation approach that accounts for interindividual folding variability, and were analyzed globally as well as across the hippocampal anterior-posterior and proximal-distal axes. Socioeconomic measures showed widespread associations with hippocampal macrostructure across the whole hippocampus and both anatomical axes, whereas associations with T1w/T2w ratio were limited and observed only for neighborhood disadvantage along the anterior-posterior axis. Cumulative adversity exposure was consistently associated with alterations in CA1 and subiculum across volume, thickness, and gyrification, but not T1w/T2w ratio. Together, these findings suggest that different types of adversity exhibit distinct spatial associations across complementary hippocampal macro- and microstructural features, highlighting regional variation in the susceptibility of the developing hippocampus to environmental adversity.

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bioRxiv2026

Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation

The human thalamus is composed of multiple nuclei that differ in structure and function. From early development onwards, these nuclei form reciprocal, nucleus-specific connections with the cerebral cortex, contributing to sensory and cognitive processing. In childhood and adolescence, a key period of neurocognitive development, these connections undergo widespread refinement, yet how developmental trajectories of thalamocortical connections vary across nuclei remains unknown. Here, we leveraged the Human Connectome Project in Development dataset (HCP-D, N = 604, age range 8-21) and segmented 10 thalamic nuclei using a segmentation approach optimized for intrathalamic contrast. Applying probabilistic tractography, we reconstructed nucleus-specific thalamocortical connections and charted their maturational profiles based on changes in fractional anisotropy (FA) using generalized additive models. We found FA to increase in thalamocortical connections, with nucleus-specific variation in temporal profiles and magnitude of age effects. Connections of core-cell-rich, sensory-projecting nuclei, such as the lateral geniculate nucleus, showed earlier maturational plateaus, whereas matrix-cell-rich, association-projecting nuclei, such as ventral anterior nucleus, showed more sustained maturation. This links maturational heterochronicity to thalamic organization of cell distribution and connectivity embedding. In parallel, functional thalamocortical connectivity decreased with age, with FA and functional connectivity age effects coupled in nucleus-connections showing prolonged maturation. Finally, concordant age effects in connectivity and nucleus volumes suggest that intra-nucleus remodeling may support refinement of structural connections while reducing thalamocortical functional synchrony. Together, our work reveals that thalamocortical maturation is anchored in the developmental and organizational heterogeneity of thalamic nuclei, offering a framework for understanding how diverse thalamic nuclei contribute to neurocognitive development.

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bioRxiv2026

A distributed neural architecture of sustained affect across external and internal experience

AO_SCPLOWBSTRACTC_SCPLOWSustained affect shapes well-being, yet its neural architecture across externally elicited and internally generated experience remains unclear. Using whole-brain functional connectivity during minutes-long naturalistic movie viewing, we derived positive and negative affective experience signatures and their underlying neural architecture. These signatures predicted valence-specific affective intensity and generalized to independent movie-viewing data and internally generated affect, discriminating sad memory and rumination from neutral distraction while tracking subjective experience. Importantly, their expression showed little relation to vigilance or cognitive demand. Characterization of these signatures revealed coherent community structure and a shared distributed backbone, alongside valence-preferential components, consistent with a partially separable architecture. Extending beyond experimentally evoked states, in four resting-state depression cohorts, these signatures distinguished patients from controls with reduced positive and elevated negative signature expression, and predicted symptom burden and anhedonia. These findings identify a generalizable distributed architecture bridging external and internal affective experience and extending to clinically relevant affective dysregulation.

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bioRxiv2026

Individual differences reveal distinct age and pubertal contributions to the refinement of the functional cortical hierarchy during adolescence

The development of the functional cortical hierarchy, spanning sensorimotor to association systems, is exclusively studied as a function of age. During adolescence, this overlooks puberty as a major neurodevelopmental driver and source of variability. We studied sensorimotor-association axis refinement longitudinally (6323 observations across 4919 subjects), leveraging individual differences to disentangle chronological age from pubertal effects. We derived low dimensional features of sensorimotor-association axis development from resting-state functional connectomes, revealing substantial inter-individual heterogeneity in maturational trajectories that challenge group-level developmental trends and milestones. Then, we demonstrate independent effects of age and pubertal stage on sensorimotor-association axis refinement through the polarization of the cortical hierarchy. We further show that coordinated system-level shifts in network topology reflect an ongoing specialization of functional connectivity profiles across all major functional networks. Our findings frame adolescent hierarchical functional cortical maturation as an individualized, multifactorial phenomenon shaped by distinct chronological age and pubertal processes.

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bioRxiv2026

Linking human brain functional connectivity to underlying neurotransmission

The human brain is organized into interacting functional systems. Their underlying neurobiological mechanisms remain difficult to study in vivo. Here, we adopt a topological framework to quantify the association between neurobiology and brain functional connectivity derived from both resting-state fMRI and MEG. Across six healthy adult cohorts (n = 19-112), regional variation in fMRI connectivity robustly aligns with the distribution of neurotransmitter receptors and transporters. These patterns are present in every single subject, replicate across all cohorts, and are mirrored in MEG. Most prominently, noradrenergic modulation of connectivity in a sensorimotor-insular network is consistently detected across individuals and linked to autonomic arousal. In pharmacological and clinical samples, associations are sensitive to neurotransmitter system manipulation and are altered in early psychosis, aligning with clinical symptomatology. These findings provide biological insight into typical and atypical functional organization of the human brain using a framework linking underlying neurobiology to the functional connectome (NEOFC).

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bioRxiv2026

Multiscale reorganization of brain and behavior under large-scale electrical perturbation

Large-scale electrical perturbation of the human brain provides a unique model for understanding how multiscale biological constraints shape behaviorally relevant reorganization. Here, we integrate longitudinal neuroimaging coordinates from 148 experiments ({approx}2,300 subjects) with normative connectomics, chemoarchitecture, intrinsic electrophysiology, and transcriptomics to identify cross-scale principles governing human brain reconfiguration under strong perturbation. Convergent hubs of structural and functional plasticity embed within default-mode and salience systems and show complementary coupling to visual networks, linking perturbation-induced change to large-scale circuits supporting affective regulation, memory, interoception, and psychosis-relevant processes. These macroscopic patterns align with intrinsic cortical dynamics and chemoarchitectural gradients dominated by 5-HT1A receptors, with additional contributions from D2, -opioid and GABAA systems, and are enriched for astrocytic and microglial gene expression, implicating glial plasticity in systems-level reorganization. Finally, in a separate intervention dataset, regularized statistical-learning models demonstrate that this multiscale signature tracks behaviorally relevant symptom change specifically under strong electrical perturbation. Together, these results outline general organizing principles linking molecular, cellular and network-level constraints to human behavioral adaptation, providing a computational framework for understanding how large-scale perturbations reshape brain systems across levels of biological organization.

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bioRxiv2025

Disentangling Brain-Psychopathology Associations: A Systematic Evaluation of Transdiagnostic Bifactor Models

Identifying robust brain-psychopathology associations with neuroimaging remains difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic latent factor models aim to address this structure by separating shared and unique symptom variance. However, it remains unclear whether latent factor modeling translates into stronger and more interpretable brain-psychopathology associations within contemporary whole-brain prediction frameworks. Using two large developmental cohorts, we systematically compared transdiagnostic bifactor models, correlated factor models, and typical summary scores derived from the Child Behaviour Checklist (CBCL) in their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). General psychopathology, internalising, externalising, and attention dimensions could be significantly predicted from resting-state connectivity but not cortical thickness. We found no consistent evidence that latent factors (bifactor or correlated factor models) strengthened reliability or brain-psychopathology associations, relative to summary scores. Neural signatures were also highly consistent across all scoring methods, with triple network (DMN, FPN, CO) involvement in general, internalizing, and externalising psychopathology. Bifactor scores did, however, display more distinct neural signatures between general, internalising, and externalising dimensions than did summary or correlated factor scores. Collectively, results suggest that additional phenotypic modelling of psychopathology alone does not systematically strengthen the predictive utility of neuroimaging, possibly reflecting fundamental limits on the amount of explainable symptom variance by brain features. While latent factor models may aid in distinguishing neural correlates between constructs, improving phenotypic assessment may be necessary for improvements to brain-psychopathology association strength.

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bioRxiv2025

Unveiling the Multifaceted Networks of the Left DLPFC for Precision TMS Targeting

The left dorsolateral prefrontal cortex (lDLPFC) is the standard transcranial magnetic stimulation (TMS) target for treatment-resistant depression (TRD), yet non-response rates remain high. TMS efficacy has been linked to the stimulation sites functional connectivity, particularly its anti-correlation with the subgenual cingulate cortex (SGC). While this pragmatic strategy has demonstrated clinical utility, it offers limited insight into how the lDLPFCs network interactions contribute to site-dependent variability in treatment response. Here, we used connectivity-based parcellation within a region of interest encompassing common TMS targets in the left prefrontal cortex (TMS-PFC) to delineate functionally distinct subregions and characterize their large-scale network connectivity and behavioral associations. Our results revealed a hierarchical organization: a coarse two-pole antagonism between anterior-central and superior-posterior subregions and a finer nine-cluster architecture exposing the heterogeneity along anterior-posterior and ventral-dorsal axes within TMS-PFC. Anterior-central areas were strongly anti-correlated with SGC and default-mode network, positively connected with salience, dorsal attention, and control networks, and associated with cognitive control. In contrast, superior-posterior subregions displayed the inverse pattern, while ventral clusters engaged somatomotor and visual networks, and language-related processes. Central and superior-anterior clusters showed differentiated profiles, including associations with inhibition, social cognition, and perceptual functions. To aid clinical translation, we derived a likelihood map integrating granularities, highlighting the anterior-central subregion as the strongest TMS candidate given its connectivity and behavioral relevance in depression, while indicating that neighboring subregions have distinct functions. These findings underscore the hierarchical and heterogeneous organization of TMS-PFC and provide a network-informed reference for developing individualized, symptom-specific TMS interventions. HighlightsO_LITMS targets within the left PFC encompass nine functionally distinct subregions C_LIO_LISubregions differ in SGC, DMN, salience, attention, and control network connectivity C_LIO_LIAnterior-central and superior-posterior subregions show antagonistic profiles C_LIO_LIA likelihood map identifies the anterior-central subregion as optimal TMS target C_LIO_LIFindings provide a network-informed reference for symptom-specific TMS targeting C_LI

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bioRxiv2025

cuBNM: GPU-Accelerated Brain Network Modeling

Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility of this approach to larger cohorts and more complex models, and highlighting the need for scalable simulation implementations. Here, we introduce cuBNM, a Python package which leverages parallel processing of graphics processing units to massively accelerate simulations of brain network models. We show running simulations on graphics processing units is several hundred times faster compared to central processing units. We demonstrate the usage of cuBNM by running optimization of group-level and individualized low- and high-dimensional models. As examples of the utility of individualized models, we investigated test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset. We found simulated features were fairly reliable and significantly heritable, suggesting their biological plausibility. Overall, cuBNM enables large-scale simulations of brain network models, opening new avenues for studying latent neural processes across diverse populations, dense networks, and high-dimensional models, which was previously impractical due to computational constraints.

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bioRxiv2025

Intracortical microstructure profiling: a versatile method for indexing cortical lamination

Intracortical microstructure profiling represents a powerful, scalable approach for investigating the laminar organisation of the human cortex on both in vivo and post-mortem datasets. Building upon a long tradition of histological analysis, this method leverages surface-based intracortical sampling to generate profiles of tissue properties across cortical depths. The present work outlines a standardised workflow for intracortical microstructural profiling, newly packaged as an open-source toolbox "CortPro" (https://github.com/caseypaquola/cortpro). Here, we explore the utility of central moments as descriptors of profile shape. Using these measures, we quantify (i) the extent to which in vivo MRI can capture laminar differentiation, (ii) the test-retest reliability of profiles, and (iii) their replicability across sites and studies. Our results demonstrate that intracortical profiles are remarkably robust and effectively mitigate bias-field related limitations of non-quantitative MRI. As applications of microstructure-sensitive imaging expand across development, aging, and disease, microstructure profiling provides a principled means of linking microstructural neuroanatomy with systems-level brain organisation.

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bioRxiv2025

Benchmarking Orientation Distribution Function Estimation Methods for Tractometry in Single-Shell Diffusion Magnetic Resonance Imaging - An Evaluation of Test-Retest Reliability and Predictive Capability

Deriving white matter (WM) bundles in-vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi-shell diffusion MRI (dMRI) acquisitions that enable advanced reconstruction methods. However, these advanced acquisitions are both time-consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single-shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population-level generalizability. While legacy scans may offer a valuable, large-scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64-direction dMRI scan was acquired as two independent 32-direction runs per subject. To investigate how recently developed bundle segmentation methods generalize to this data, we evaluated the test-retest reliability of the two 32-direction scans, of WM bundle extraction across three orientation distribution function (ODF) reconstruction methods: generalized q-sampling imaging (GQI), constrained spherical deconvolution (CSD), and single-shell three-tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32-direction, single-shell acquisition scheme. The mean dice coefficient of reconstructed WM bundles was consistently higher within-subject than between-subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person-specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies (r: 0.15-0.36) across the test-retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity in external validation, a high intra-class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle segmentation can achieve robust performance even on lower angular resolution, single-shell dMRI, with particular advantages for ODF methods optimized for single-shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.

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bioRxiv2025

Disentangling the influences of pre- and postnatal periods on human cortical microstructure

During late gestation and early postnatal development a combination of intrinsic and extrinsic factors drive the maturation of the human cortex. This process is regionally heterogeneous, with cortical areas developing at different paces and trajectories. Leveraging submillimetre T1-weighted/T2w-weighted (T1w/T2w) magnetic resonance imaging (MRI) from pre- and full-term neonates (n = 599, 0-7 weeks), we sampled intracortical microstructure profiles across the cortex and characterised the profiles shapes according to their central moments. We found that gestational age at birth dominated the effects on early cortical development, with significant, global increases in microstructural density, increasing intracortical homogeneity and a bimodal change of the microstructural balance between superficial and deeper cortical layers. On the other hand, weeks since birth (i.e. postnatal age) exhibited different effects on microstructure, with density increasing at a slower pace, increasing intracortical heterogeneity, and intracortical balance only shifting towards deeper layers in posterior temporal, occipital, medial parietal areas and some prefrontal areas. These effects align with low spatial-frequency geometric eigenmodes of the human cortex, specifically the anterior-posterior, superior-inferior and central-polar axes. Our findings demonstrate that separating prenatal from postnatal influences, and analysing intracortical profiles rather than macroscale features, provides finer-grained insights into how human cortical microstructure changes during perinatal development and lays the groundwork for investigating the biological underpinnings that govern normative cortical maturation.

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bioRxiv2025

Predicting Brain Volumes from Anthropometric and Demographic Features: Insights from UK Biobank Neuroimaging Data

Brain size measures are well-studied and often treated as a confound in volumetric neuroimaging analyses. Yet their relationship with body anthropometric measures and demographics remains underexplored. In this study, we examined those relationships alongside age- and sex-related differences in global brain volumes. Using brain magnetic resonance imaging (MRI) of healthy participants in the UK Biobank, we derived global measures of brain morphometry, including total intracranial volume (TIV), total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF). We extracted these measures using the Computational Anatomy Toolbox (CAT) and FreeSurfer. Our analyses were structured in three approaches: across-sex analysis, sex-specific analysis, and impact of age analysis. Employing machine learning (ML), we found that TIV was strongly predicted by sex (across-sex r = 0.68), reflecting sexual dimorphism. On the other hand, TBV, GMV, WMV, and CSF were more sensitive to age, with higher prediction accuracy when age was included as a feature, highlighting age-related changes in the brain structure, such as fluid expansion. Sex-specific models showed reduced TIV prediction (r {approx} 0.25) but improved TBV accuracy (r {approx} 0.44), underscoring sex-specific body-brain relationships. Anthropometrics enhanced prediction but only subsidiary to age and sex. These findings advance our understanding of brain-body scaling relationships and underscore the necessity of accounting for age and sex in neuroimaging studies of brain morphology.

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bioRxiv2025

Mapping Early Brain Maturation: Anatomical Substrates of Cortical Connectivity Shifts in Neonates

Understanding the early development of brain connectivity is essential for unraveling the mechanisms of brain maturation. In this study, we utilized diffusion MRI data from 242 term neonates to chart the developmental trajectory of cortical connectivity in infants. We quantified interareal variations in connectivity and identified three distinct global connectopies (GCs) along the rostrocaudal, dorsoventral, and mediolateral axes by using gradient mapping techniques. A pivotal age-dependent shift between the dorsoventral and mediolateral GCs occurred at 40 postmenstrual weeks. This shift was diminished by virtual thalamic fiber lesions, underscoring the critical role of thalamic projections. A longitudinal analysis disclosed a delayed structural connectivity pattern development in preterm infants who failed to exhibit the anticipated shift. This research helps to clarify the topographic principles that shape infant brain organization and emphasizes the influence of thalamic connectivity on early brain development. Our findings elucidate the anatomical underpinnings of early brain development, providing critical insights into the formation of cortical hierarchies and highlighting the influence of thalamic connectivity on developmental trajectories. These findings are essential for understanding typical brain growth and the pathogenesis of neurodevelopmental disorders.

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bioRxiv2024

Voxel-wise or Region-wise Nuisance Regression for Functional Connectivity Analyses: Does it matter?

Removal of nuisance signals (such as motion) from the BOLD time series is an important aspect of preprocessing to obtain meaningful resting-state functional connectivity (rs-FC). The nuisance signals are commonly removed using denoising procedures at the finest resolution, i.e. the voxel time series. Typically the voxel-wise time series are then aggregated into predefined regions or parcels to obtain a rs-FC matrix as the correlation between pairs of regional time series. Computational efficiency can be improved by denoising the aggregated regional time series instead of the voxel time series. However, a comprehensive comparison of the effects of denoising on these two resolutions is missing. In this study, we systematically investigate the effects of denoising at different time series resolutions (voxel- and region-level) in 370 unrelated subjects from the HCP-YA dataset. Alongside the time series resolution, we considered additional factors such as aggregation method (Mean and first eigenvariate [EV]) and parcellation granularity (100, 400, and 1,000 regions). To assess the effect of those choices on the utility of the resulting whole-brain rs-FC, we evaluated the individual specificity (fingerprinting) and the capacity to predict age and three cognitive scores. Our findings show generally equal or better performance for region-level denoising with notable differences depending on the aggregation method. Using mean aggregation yielded equal individual specificity and prediction performance for voxel- and region-level denoising. When EV was employed for aggregation, the individual specificity of voxel-level denoising was reduced compared to region-level denoising. Increasing parcellation granularity generally improved individual specificity. For the prediction of age and cognitive test scores, only fluid intelligence indicated worse performance for voxel-level denoising in the case of aggregating with the EV. Based on these results, we recommend the adoption of region-level denoising for brain-behavior investigations when using mean aggregation. This approach offers equal individual specificity and prediction capacity with reduced computational resources for the analysis of rs-FC patterns.

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bioRxiv2024

Procrustes Alignment in Individual-level Analyses of Functional Gradients

Functional connectivity (FC) gradients provide valuable insights into individual differences in brain organization, yet aligning these gradients across individuals poses challenges. Procrustes alignment is often employed to standardize gradients across multiple subjects, but the choice of the number of gradients used in alignment introduces complexities that may impact individual-level analyses. In this study, we systematically investigate the impact of varying gradient counts in Procrustes alignment on the principal FC gradient, using data from four resting state fMRI datasets, including the Human Connectome Project (HCP-YA), Amsterdam Open MRI Collection (AOMIC) PIOP1 and PIOP2, and Cambridge Centre for Ageing and Neuroscience (Cam-CAN). We find that increasing the number of gradients used in alignment enhances identification accuracy but can reduce differential identifiability, as additional gradients risk introducing nuisance signals such as motion back into the principal gradient. To further probe these effects, machine learning to predict fluid intelligence and age, and a motion prediction analysis, revealing that higher alignment gradient counts may leak information from lower gradients into the principal gradient for individual-level analyses. These findings highlight the trade-off between alignment precision and the potential reintroduction of noise. Key PointsO_LIGradient count used in Procrustes alignment impacts identification accuracy and differential identifiability obtained using the principal gradient C_LIO_LIThe magnitude of the Procrustes transformation correlates with motion measures, and this correlation increases with higher gradient count used in alignment. C_LIO_LIGradient count used in Procrustes alignment impacts prediction of fluid intelligence and age C_LI

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bioRxiv2024

Bias-accounting meta-analyses overcome cerebellar neglect to refine the cerebellar behavioral topography

The cerebellum plays important roles in motor, cognitive, and emotional behaviors. Previous cerebellar coordinate-based meta-analyses (CBMAs) have complemented precision-mapping and parcellation approaches by finding generalizable cerebellar activations across the largest possible set of behaviors. However, cerebellar CBMAs face challenges due to inherent methodological limitations exacerbated by historical cerebellar neglect in neuroimaging. Here, we show overrepresentation of superior activations, rendering the null hypothesis of standard activation likelihood estimation (ALE) unsuitable. Our new method, cerebellum-specific ALE (C-SALE), finds behavioral convergence beyond baseline activation rates. It does this by testing experimental activations versus null models sampled from a data-driven probability distribution of finding activations at any cerebellar location. Task-specific mappings in the BrainMap meta-analytic database illustrated improved specificity of the new method. Multiple (sub)domains reached convergence in specific cerebellar subregions, supporting dual motor representations and placing cognition in posterior-lateral regions. We show our method and findings were replicable within NeuroSynth. Across both databases, 54/138 task domains or behavioral terms, including sustained attention, somesthesis, inference, anticipation and rhythm, reached convergence in specific cerebellar subgregions. Maps largely corresponded with cerebellar atlases but also showed many complementary mappings. Repeated subsampling showed that motor behaviors, and to a lesser extent language and working memory, mapped to especially consistent cerebellar subregions. Lastly, we found that cerebellar clusters were parts of brain-wide coactivation networks with cortical and subcortical regions implied in these behaviors. Together, our method further complements and expands understanding of cerebellar involvement in human behavior, highlighting regions for future investigation in both basic and clinical applications. Highlights[bullet] Biases in reported cerebellar activations strongly favors superior regions. [bullet]A new method of meta-analysis increases cerebellar mapping specificity and accuracy. [bullet]Large-scale meta-analyses support cerebellar roles in cognitive, affective, and motor behaviors. [bullet]54 task domains/ terms converged, including sustained attention, somesthesis, inference, anticipation and rhythm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/621398v3_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@716ea0org.highwire.dtl.DTLVardef@11601fforg.highwire.dtl.DTLVardef@1dd1208org.highwire.dtl.DTLVardef@caf535_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

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