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•Cerebellar Fractional Anisotropy modulates cognition in healthy individuals.•Cerebellar microstructure is linked to cognitive performance in healthy population.•Crus I Fractional Anisotropy is linked to language-related executive functions.•Lobule IX and Vermis IX Fractional Anisotropy associate with long-term memory.•Lobule V Fractional Anisotropy correlates with abstract reasoning. The cerebellum is recognized for its role in motor control. However, it also plays a crucial part in modulating circuits involved in cognition and affect. While studies conducted on people with cerebellar disorders highlight both structural and functional links with cognition, research on cerebellar structure in the healthy population remains sparse. To better clarify the cerebellum’s role and operational mode in cognition, this multi-scale study explores the relationship between cognitive functions and cerebellar macrostructure and microstructure in healthy individuals. Macrostructural analysis focused on grey matter (GM) and white matter (WM) volumes, while microstructural evaluation used fractional anisotropy (FA) values. Using a large normative cohort, the study examined cerebellar GM and WM volumes in 151 participants and FA in 83 participants. Cerebellar GM and WM volumes and FA values were correlated voxel-wise against the following cognitive domains: long-term memory, abstract reasoning, language-related executive functions, processing speed, and impulsive decision-making. Significant positive correlations were found between FA in specific cerebellar regions and long-term memory (p = 0.030), abstract reasoning (p = 0.048), and language-related executive functions (p = 0.043). Additionally, cerebellar FA values negatively correlated in several clusters with reaction time (p = 0.001; p = 0.026; p = 0.045), indicating faster processing speed with higher FA. No significant associations were found between cerebellar GM/WM volumes and cognitive performance after Family Wise Error correction, except for a positive correlation between WM and reaction time (p = 0.023). These findings highlight the cerebellum's microstructure role in cognition. FA may reflect the efficiency of communication between cerebellar and cortical regions, thus allowing the cerebellum to improve cognitive performance by updating internal models and correcting discrepancies between predictions and outcomes.

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands, in comparison to 0.45 for the analytical model. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants ( for the deep-learning model, in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project ( https://osf.io/qaesm/ ). Data were classified by raters as either “include,” “uncertain,” or “exclude.” There was moderate to substantial agreement between raters for “include” and “exclude,” but little to no agreement for “uncertain.” In most cases only a single rater used the “uncertain” classification for a given participant’s data, with the remaining raters showing agreement for “include”/“exclude” decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for “uncertain” cases, aiding classification consistency.

This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18–63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.

Abstract Context Children born to mothers with gestational hypothyroidism or hyperthyroidism may have increased risk of adverse neurodevelopmental outcomes. However, the effects of maternal thyroid status on offspring brain development are unclear. Objective This work aimed to establish whether adolescent brain morphology is affected by suboptimal gestational thyroid function (SGTF). Methods The Controlled Antenatal Thyroid Screening (CATS) study randomly assigned mothers with SGTF to levothyroxine or no supplementation from approximately 12 weeks’ gestation. At age 9, children born to mothers who were overtreated with levothyroxine had a higher risk of conduct and hyperactivity traits. For the current CATS III study, children underwent neuroimaging studies, including T1-weighted structural magnetic resonance imaging (MRI). A total of 85 children aged 11 to 16 years had usable T1-weighted MRI data (exposed to untreated SGTF [n = 21], normal GTF [n = 24], or treated SGTF [optimally treated (n = 21), overtreated (n = 20)]). The primary outcome was to examine the association of SGTF and its treatment with global brain volumes. Secondary and exploratory outcomes were to investigate the association of maternal thyrotropin (TSH) and free thyroxine (FT4) levels with global and subregional brain volumes. Results were adjusted for age, sex, and pubertal scores. Results There were no significant differences in global brain volumetric measures between groups, including total gray matter volume (P = .373). Weak positive correlations were found between maternal TSH, but not FT4, levels and several brain volumes, but these did not survive testing for multiple comparisons. Conclusion We found no evidence that SGTF was associated with differences in adolescent brain morphology, and no effect of levothyroxine supplementation.

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants (p < 2 × 10−4 for the deep-learning model, p < 3 × 10−3 in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neural networks to obtain high-resolution dMRI scans. Here we hypothesise that the inclusion of structural MRI images, which can be acquired at much higher resolutions, can be used as a guide to obtaining a more accurate high-resolution dMRI output. To test our hypothesis, we have constructed a novel framework that incorporates structural MRI scans together with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate the validity of our claim through various configurations and compare the performance of our approach against a unimodal approach. Our results show that the inclusion of structural MRI scans do lead to an improvement in high-resolution image prediction when T1w data is incorporated into the model input.

Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neural networks to obtain high-resolution dMRI scans. Here we hypothesise that the inclusion of structural MRI images, which can be acquired at much higher resolutions, can be used as a guide to obtaining a more accurate high-resolution dMRI output. To test our hypothesis, we have constructed a novel framework that incorporates structural MRI scans together with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate the validity of our claim through various configurations and compare the performance of our approach against a unimodal approach. Our results show that the inclusion of structural MRI scans do lead to an improvement in high-resolution image prediction when T1w data is incorporated into the model input.