Mapping pontocerebellar connectivity with diffusion MRI

Multiscale gradients of corticopontine structural connectivity

The cerebellum's involvement in a range of cognitive, emotional, and motor processes has become increasingly evident. Given the uniformity of the cerebellar cortex's cellular architecture its contributions to varied processes are thought be partially mediated by its patterns of reciprocal connectivity with the rest of the brain. A better understanding of these connections is therefore fundamental to disentangling the cerebellum's contribution to cognition and behavior. While these connections have been studied extensively in non-human animals using invasive methods, we have limited knowledge of these connections in humans. The current work reconstructed the corticopontine projection, the first segment of downstream connections between the cerebral and cerebellar cortices, with diffusion MRI tractography in human in-vivo whole brain data and an independent higher resolution postmortem brainstem dataset. Dimensionality reduction was used to characterize the pattern of connectivity of cerebral cortical projections to the pons as two overlapping gradients that were consistent across participants and datasets: medial to lateral and core to belt. Our findings align with invasive work done in animals and advance our understanding of this connection in humans - providing valuable context to a growing body of cerebellar research, offering insights into impacts of damage along the pathway, and informing clinical interventions.

Patterns of Cerebellar-Cortical Structural Covariance Mirror Anatomical Connectivity of Sensorimotor and Cognitive Networks

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions. To test this hypothesis, we examined covariation between the volumes of anterior sensorimotor and lateral cognitive lobules of the cerebellum and measures of cortical thickness (CT) and surface area (SA) across the whole brain in a sample of 270 young adults drawn from the HCP dataset. We observed that patterns of cerebellar-cortical covariance differed between sensorimotor and cognitive networks. Anterior motor lobules of the cerebellum showed greater covariance with sensorimotor regions of the cortex, while lobules Crus I and Crus II showed greater covariance with frontal and temporal regions. Interestingly, cerebellar volume showed predominantly negative relationships with CT and predominantly positive relationships with SA. Individual differences in SA are thought to be largely under genetic control while CT is thought to be more malleable by experience. This suggests that cerebellar-cortical covariation for SA may be a more stable feature, whereas covariation for CT may be more affected by development. Additionally, similarity metrics revealed that the pattern of covariance showed a gradual transition between sensorimotor and cognitive lobules, consistent with evidence of functional gradients within the cerebellum. Taken together, these findings are consistent with known patterns of structural and functional connectivity between the cerebellum and cortex. They also shed new light on possibly differing relationships between cerebellar volume and cortical thickness and surface area. Finally, our findings are consistent with the interactive specialization framework which proposes that structurally and functionally connected brain regions develop in concert.

Cerebello-Hippocampal Interactions in the Human Brain: A New Pathway for Insights Into Aging

The cerebellum is recognized as being important for optimal behavioral performance across task domains, including motor function, cognition, and affect. Decades of work have highlighted cerebello-thalamo-cortical circuits, from both structural and functional perspectives. However, these circuits of interest have been primarily (though not exclusively) focused on targets in the cerebral cortex. In addition to these cortical connections, the circuit linking the cerebellum and hippocampus is of particular interest. Recently, there has been an increased interest in this circuit, thanks in large part to novel findings in the animal literature demonstrating that neuronal firing in the cerebellum impacts that in the hippocampus. Work in the human brain has provided evidence for interactions between the cerebellum and hippocampus, though primarily this has been in the context of spatial navigation. Given the role of both regions in cognition and aging, and emerging evidence indicating that the cerebellum is impacted in age-related neurodegenerative disease such as Alzheimer's, I propose that further attention to this circuit is warranted. Here, I provide an overview of cerebello-hippocampal interactions in animal models and from human imaging and outline the possible utility of further investigations to improve our understanding of aging and age-related cognitive decline.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning

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.

The Arterial Anatomy of the Cerebellum-A Comprehensive Review

The cerebellum, a major feature of the hindbrain, lies posterior to the pons and medulla and inferior to the posterior part of the cerebrum. It lies beneath the tentorium cerebelli in the posterior cranial fossa and consists of two lateral hemispheres connected by the vermis. The cerebellum is primarily supplied by three arteries originating from the vertebrobasilar system: the superior cerebellar artery (SCA), the anterior inferior cerebellar artery (AICA), and the posterior inferior cerebellar artery (PICA). However, variations of the cerebellar arteries may occur, such as duplication of the SCA, SCA creating a common trunk with the posterior cerebral artery, triplication of the AICA, and agenesis of PICA, amongst others. Knowledge of the arterial anatomy of the cerebellum is crucial, as inadequate blood supply to this region can result in diminished motor functioning, significantly impacting the quality of life for patients. The present study demonstrated the importance of adequate anatomical knowledge of the arteries supplying the cerebellum. The PubMed and Embase databases were searched to gather articles on the anatomical characteristics and variations of the arterial supply of the cerebellum. It is the most comprehensive and up-to-date review available in the literature. The possible variations of these vessels may be clinically silent or present with clinical symptoms such as neurovascular compression syndromes of the cranial nerves and aneurysms. With a comprehensive understanding of the cerebellar arterial system, physicians can enhance their diagnostic and treatment capabilities, ultimately leading to more effective management of cerebellar vascular-related issues and other neurological deficits.

White matter tract crossing and bottleneck regions in the fetal brain

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

A detailed spatio-temporal atlas of the white matter tracts for the fetal brain

This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.

Spatially regularized low-rank tensor approximation for accurate and fast tractography

Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain. Many tractography methods rely on models of multiple fiber compartments, but the local dMRI information is not always sufficient to reliably estimate the directions of secondary fibers. Therefore, we introduce two novel approaches that use spatial regularization to make multi-fiber tractography more stable. Both represent the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor, and recover multiple fiber orientations via low-rank approximation. Our first approach computes a joint approximation over suitably weighted local neighborhoods with an efficient alternating optimization. The second approach integrates the low-rank approximation into a current state-of-the-art tractography algorithm based on the unscented Kalman filter (UKF). These methods were applied in three different scenarios. First, we demonstrate that they improve tractography even in high-quality data from the Human Connectome Project, and that they maintain useful results with a small fraction of the measurements. Second, on the 2015 ISMRM tractography challenge, they increase overlap, while reducing overreach, compared to low-rank approximation without joint optimization or the traditional UKF, respectively. Finally, our methods permit a more comprehensive reconstruction of tracts surrounding a tumor in a clinical dataset. Overall, both approaches improve reconstruction quality. At the same time, our modified UKF significantly reduces the computational effort compared to its traditional counterpart, and to our joint approximation. However, when used with ROI-based seeding, joint approximation more fully recovers fiber spread.