The road less traveled: including the brainstem and cerebellum for mapping cognitive pathways

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. Here we study how functional activity throughout the brainstem aligns with cortical function. We use high-resolution 7 Tesla resting-state fMRI, via a novel brainstem-optimized acquisition protocol and processing pipeline, to image the whole-brainstem across 58 anatomically defined nuclei spanning midbrain, pons and medulla. First, we identify hubs of brainstem-cortex functional connectivity (FC) and link brainstem-cortex FC with cytoarchitectonic classes, laminar differentiation, and electrophysiological signatures of neural oscillations. Next, we cluster brainstem nuclei with respect to their cortical FC. We identify five modules of brainstem nuclei with distinct patterns of cortical FC related to memory, cognitive control, multisensory coordination, perception and movement, and emotion. Furthermore, we use PET-estimated brain maps for 18 neurotransmitter receptors and transporters to determine whether neurotransmitter systems are mediating the link between neuromodulatory brainstem nuclei and cortical functional activation patterns. Finally, we find that cortical regions are functionally connected with the brainstem along a unimodal-transmodal hierarchy, indicating that the putative cortical functional gradient can be traced back to the brainstem. To ensure all findings are robust, we replicate the analyses using 3 Tesla data acquired in the same individuals, using an alternative parcellation resolution, and in the subcortex. Altogether, using simultaneous in vivo human imaging of brainstem and cortical functional activity, this study extends our perspective of cortical function---including dynamics, cognitive function, and the unimodal-transmodal cortical functional gradient---to the brainstem, demonstrating how cortical functional architecture consistently reflects the brainstem. 

Although several lines of evidence establish the involvement of the medial and vestibular parts of the cerebellum in adaptive motor control, the role of the lateral cerebellum in motor control remains unclear. Ascending projections from the lateral cerebellum to the frontal and parietal association cortices are consistent with the role of these pathways in higher-order behavioral control. Consistent with this, we found that the dentate nucleus, the output structure of the lateral cerebellum, is involved in cognitive functions such as action strategy and movement timing (Kunimatsu et al., 2016, 2018). Especially, the sustained activity in the dentate nucleus before movement onset proactively regulates these behaviors. Recently, we found that these sustained activities are also involved in associative learning. In this study, we recorded the activity of neurons in the dentate nucleus while monkeys performed the object-motor association task in two conditions: a learning condition in which novel objects were used and an over-trained condition in which well-learned objects were used. Many neurons in the dentate nucleus showed sustained activity for the delay period after the objects were presented, and it was greater in the learning condition than in the over-trained condition. Interestingly, the enhancement of sustained activity was diminished when the correct direction was randomly determined, even though the correct rate was equivalent to the early phase of the learning condition. Thus, these sustained activities were only observed when the monkey was aware of the need to learn the visuomotor association. The sustained activity in the dentate nucleus may work as proactive signals to the learning, and the signal may interact with the cerebral cortex. 

Coordinated-based meta-analyses such as activation likelihood estimation (ALE) have contributed greatly to the understanding that the cerebellum plays important roles across diverse motor, cognitive, and emotional behaviors. However, cerebellar imaging is faced with unique challenges. One is that it has historically been forgotten about in literature, especially in non-motor domains. This leads to stark differences in reporting rates of superior and inferior (underreported) activations. In turn, this renders ALE’s null-hypothesis invalid and calls into question the accuracy of previous cerebellar meta-analyses. Here, we introduce cerebellum-specific ALE (C-SALE), that overcomes differences in reporting rates across the cerebellum. It does so by updating its null model to represent voxel-wise probabilities of finding activation foci. The null model was created by combining all studies in the extensive, manually indexed BrainMap database. We first compare the classic ALE method to our new C-SALE method, showing much improved specificity of the latter. C-SALE finds convergence in twelve of forty task datasets, including aspects of action, memory, social cognition, emotion, and perception. Having access to many moderate-to-large-sized tasks, we were able to extensively characterize C-SALE stability using repeated subsampling. We show that mapping stability increases across task domains quite similarly as subsampling proportions increase. However, the Action domain stands out, with high subsample consistency leading to convergence across most subsamples. We next compare C-SALE maps to existing cerebellar parcellations, mappings, and gradients, often finding significant spatial correspondence between behaviorally related C-SALE and target maps. Lastly, we apply the same unbiased framework to reveal brain-wide coactivation networks across action, working memory, and vision task sets. Together, our findings provide important refinement of the cerebellar behavioral topography. Our method can be flexibly extended to any volumetric brain region-of-interest. Full code to do this (github.com/NevMagi/cerebellum_specific_ALE) including a fast GPU implementation of ALE and C-SALE (speed-ups ~100x; github.com/amnsbr/nimare-gpu) are openly shared. Concluding, our study furthers understanding of cerebellar and brain subregional functions, suggesting regions suitable for study of basic and clinical applications. 

The ability to adapt to changes in the environment is essential for skilled performance, especially in competitive sports and events, where experts consistently perform at the highest level, rapidly adapting to unpredictable conditions. Our ability to manipulate the contents of our working memory is also integral for our daily lives, whether it be organising our schedule, combining ingredients while we cook dinner or solving problems at work. While both of these capacities are typically linked to information processing through specialised cortical networks, I will demonstrate a series of combined neuroimaging and computational modelling approaches that link these capacities to distributed networks that link the cerebral cortex with a key set of subcortical regions – including the basal ganglia, thalamus and cerebellum. Overall, the results provide evidence that cerebellar and subcortical regions facilitate manipulation of working memory and skilled performance, and thus suggest a generalised mechanism through which the distributed subcomponents of our brain work together to support cognitive functions.