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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.

Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher‐order functions in humans in vivo. Whole‐brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion‐derived metrics in an unsupervised fuzzy c‐means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule‐specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non‐motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule‐specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher‐order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non‐motor functions and that connectivity‐based and microstructure‐based atlases provide complementary information. These results represent a step‐ahead for the interpretation of pathological conditions involving cerebro‐cerebellar circuits. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity, in turn with either the cerebral or the cerebellar cortex, as well as their microstructural features, inputting them in an unsupervised fuzzy c‐means classification algorithm. The three approaches provided quite overlapping results in terms of geometrical distribution and, for the first time, indicate that more than half of the DN volumes is involved in non‐motor higher‐order functions.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data‐driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure–function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large‐scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure–function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure–function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low‐dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest‐saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency‐based parcellation framework, DeepMSP, enables multimodal, data‐driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure–function relationships of the cerebellar pathways. We introduce a deep‐learning based multimodal data‐driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. We find that it is possible to identify multiple cerebellar pathway parcels with unique structure–function saliency patterns that are stable across training folds.

Children born preterm (PT) are at risk for white matter injuries based on complications of prematurity. They learn to read but on average perform below peers born full term (FT). Studies have yet to establish whether properties of white matter pathways at the onset of learning to read are associated with individual variation later in reading development in PT children. Here, we asked whether fractional anisotropy (FA) at age 6 years is associated with reading outcome at age 8 years in PT children in the same pathways as previously demonstrated in a sample of FT children. PT ( = 34, mean gestational age = 29.5 weeks) and FT children ( = 37) completed diffusion MRI and standardized measures of non-verbal IQ, language, and phonological awareness at age 6 years. Reading skills were assessed at age 8 years. Mean tract-FA was extracted from pathways that predicted reading outcome in children born FT: left arcuate fasciculus (Arc), bilateral superior longitudinal fasciculus (SLF), and left inferior cerebellar peduncle (ICP). We explored associations in additional pathways in the PT children: bilateral inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. Linear regression models examined whether the prediction of reading outcome at age 8 years based on mean tract-FA at age 6 years was moderated by birth group. Children born PT and FT did not differ significantly in tract-FA at age 6 years or in reading at age 8 years. Sex, socioeconomic status, and non-verbal IQ at age 6 years were associated with reading outcome and were included as covariates in all models. Birth group status significantly moderated associations between reading outcome and mean tract-FA only in the left Arc, right SLF, and left ICP, before and after consideration of pre-literacy skills. Microstructural properties of these cerebral and cerebellar pathways predicted later reading outcome in FT but not in PT children. Children born PT may rely on alternative pathways to achieve fluent reading. These findings have implications for plasticity of neural organization after early white matter injury.

Postural control is a complex sensorimotor task that requires an intact network of white matter connections. The ability to weight proprioceptive signals is crucial for postural control. However, research into central processing of proprioceptive signals for postural control is lacking. This is specifically of interest in individuals with non-specific low back pain (NSLBP), because impairments in postural control have been observed as possible underlying mechanisms of NSLBP. Therefore, the objective was to investigate potential differences in sensorimotor white matter microstructure between individuals with NSLBP and healthy controls, and to determine whether the alterations in individuals with NSLBP are associated with the capacity to weight proprioceptive signals for postural control. The contribution of proprioceptive signals from the ankle and back muscles to postural control was evaluated by local muscle vibration in 18 individuals with NSLBP and 18 healthy controls. Center of pressure displacement in response to muscle vibration was determined during upright standing on a stable and unstable support surface. Diffusion magnetic resonance imaging was applied to examine whether this proprioceptive contribution was associated with sensorimotor white matter microstructure. Individuals with NSLBP showed a trend towards a reduced fractional anisotropy along the left superior cerebellar peduncle compared to healthy controls (p = 0.039). The impaired microstructural integrity of the superior cerebellar peduncle in individuals with NSLBP was significantly correlated with the response to ankle muscle vibration (p<0.003). In individuals with NSLBP, a decreased integrity of the superior cerebellar peduncle was associated with an increased reliance on ankle muscle proprioception, even on unstable support surface, which implies an impaired proprioceptive weighting capacity. Our findings emphasize the importance of the superior cerebellar peduncle in proprioceptive weighting for postural control in individuals with NSLBP.

Objective: White matter (WM) microstructural changes are increasingly recognized as a mechanism of age-related cognitive differences. This study examined the associations between patterns of WM microstructure and cognitive performance on the University of California, San Francisco (UCSF) Brain Health Assessment (BHA) subtests of memory (Favorites), executive functions and speed (Match), and visuospatial skills (Line Orientation) within a sample of older adults. Method: Fractional anisotropy (FA) in WM tracts and BHA performance were examined in 84 older adults diagnosed as neurologically healthy (47), with mild cognitive impairment (19), or with dementia (18). The relationships between FA and subtest performances were evaluated using regression analyses. We then explored whether regional WM predicted performance after accounting for variance explained by global FA. Results: Memory performance was associated with FA of the fornix and the superior cerebellar peduncle; and executive functions and speed, with the body of the corpus callosum. The fornix–memory association and the corpus callosum–executive association remained significant after accounting for global FA. Neither tract-based nor global FA was associated with visuospatial performance. Conclusions: Memory and executive functions are associated with different patterns of WM diffusivity. Findings add insight into WM alterations underlying age- and disease-related cognitive decline.

In order to deeply understand the specific patterns of volume, microstructure, and functional changes in Multiple System Atrophy patients with cerebellar ataxia syndrome (MSA-c), we perform the current study by simultaneously applying structural (T1-weighted imaging), Diffusion tensor imaging (DTI), functional (BOLD fMRI) and extended Network-Based Statistics (extended-NBS) analysis. Twenty-nine MSA-c type patients and twenty-seven healthy controls (HCs) were involved in this study. First, we analyzed the whole brain changes of volume, microstructure, and functional connectivity (FC) in MSA-c patients. Then, we explored the correlations between significant multimodal MRI features and the total Unified Multiple System Atrophy Rating Scale (UMSARS) scores. Finally, we searched for sensitive imaging biomarkers for the diagnosis of MSA-c using support vector machine (SVM) classifier. Results showed significant grey matter atrophy in cerebellum and white matter microstructural abnormalities in cerebellum, left fusiform gyrus, right precentral gyrus and lingual gyrus. Extended-NBS analysis found two significant different connected components, featuring altered functional connectivity related to left and right cerebellar sub-regions respectively. Moreover, the reduced fiber bundle counts at right Cerebellum_3 (Cbe3) and decreased fractional anisotropy (FA) values at bilateral Cbe9 were negatively associated with total UMSARS scores. Finally, the significant features at left Cbe9, Cbe1 and Cbe7b were found to be useful as sensitive biomarkers to differentiate MSA-c from HCs according to the SVM analysis. These findings advanced our understanding of the neural pathophysiological mechanisms of MSA from the perspective of multimodal neuroimaging.