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The cerebellum is thought to help detect and correct errors between intended and executed commands 1 , 2 and is critical for social behaviours, cognition and emotion 3 – 6 . Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise 7 . Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network’s first layer 8 – 13 . However, maximizing encoding capacity reduces the resilience to noise 7 . To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks. Mapping of the mouse cerebellar cortex using 3D reconstruction from electron microscopy, as well as numerical simulation of neuronal activity, shows non-random redundancy of connectivity that may favour resilient learning over encoding capacity.

There is compelling evidence that the human cerebellum is engaged in a wide array of motor and cognitive tasks. A fundamental question centers on whether the cerebellum is organized into distinct functional subregions. To address this question, we employed a rich task battery designed to tap into a broad range of cognitive processes. During four functional MRI sessions, participants performed a battery of 26 diverse tasks comprising 47 unique conditions. Using the data from this multi-domain task battery, we derived a comprehensive functional parcellation of the cerebellar cortex and evaluated it by predicting functional boundaries in a novel set of tasks. The new parcellation successfully identified distinct functional subregions, providing significant improvements over existing parcellations derived from task-free data. Lobular boundaries, commonly used to summarize functional data, did not coincide with functional subdivisions. The new parcellation provides a functional atlas to guide future neuroimaging studies.

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package. Single-cell transcriptomics enhanced our ability to profile heterogeneous cell populations. It is not known which statistical frameworks are performant to detect subpopulation-level responses. Here, the authors developed a simulation framework to evaluate various methods across a range of scenarios.

There has been accumulating evidence for a regionalized organization of the cerebellum, which was mostly deduced from anatomical mapping of axonal projections of cerebellar afferents. A likewise regionalization of the cerebellar output has been suggested from lesion studies and dye-tracer experiments, but its physiological targets as well as the functional relevance of such an output regionalization are less clear. Ideally, such functional regionalization should be proven noninvasively in vivo. We here provide evidence for such a regionalization of the output from the cerebellar cortex by genetically encoded transneuronal mapping of efferent circuits of zebrafish Purkinje neurons. These identified circuits correspond to distinct regionalized Purkinje cell activity patterns in freely behaving zebrafish larvae during the performance of cerebellar-dependent behaviors. Furthermore, optogenetic interrogation of selected Purkinje cell regions during animal behavior confirms the functional regionalization of Purkinje cell efferents and reveals their contribution to behavior control as well as their function in controlling lateralized behavioral output. Our findings reveal how brain compartments serve to fulfill a multitude of functions by dedicating specialized efferent circuits to distinct behavioral tasks.

This was a study of 12 cerebellar cortical dysplasias (CCDs) fetuses, these cases were characterized by a disorder of cerebellar fissures. Historically, CCD diagnosis was primarily performed using postnatal imaging. Unique to this study was the case series of CCD for prenatal diagnosis using prenatal ultrasound, as well as we found that AXIN1 and FOXC1 mutations may be related to CCD.

Cerebellar information processing is critically shaped by several types of inhibitory interneurons forming various intra-cerebellar feed-forward and feed-back loops. Evidence gathered over the past decades has focused interest on a non-uniform set of cortical inhibitory interneurons distinct from “classical” Golgi, basket or stellate cells, summarily referred to as PLIs (for Purkinje cell layer interneurons). Similarly, cerebellar nuclear inhibitory interneurons have gained increasing attention. Our understanding of the functions of these cells is still fragmentary. For humans, we lack functional data, and even any dependable morphological classification for these cells. Here, I used publicly available single cell based gene expression data to compare inhibitory interneurons from the cerebellar cortex and inhibitory nuclear neurons of humans and mice. Integration of nuclear and cortical cells revealed transcriptomic similarities between subsets of these cells and suggest known characteristics of cortical cell types may be helpful to devise strategies for the further characterization of nuclear inhibitory interneurons. Comparison of human and murine PLIs indicate that these strongly differ by the expression of genes used to characterize these cells in mice. This limits their utility to identify and classify human PLIs, and leaves the question open as to the number and characteristics of non-Golgi inhibitory interneurons resident in the cerebellar granule cell and Purkinje cell layers in humans.

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.

The cerebellum has a medial, cortico-nuclear zone consisting of the cerebellar vermis and the fastigial nucleus. Functionally, this zone is concerned with whole-body posture and locomotion. The vermis classically is thought to be included within the \"spinocerebellum\" and to receive somatic sensory input from ascending spinal pathways. In contrast, the lateral zone of the cerebellum is included in the \"cerebro-cerebellum\" because it is densely interconnected with the cerebral cortex. Here we report the surprising result that a portion of the vermis receives dense input from the cerebral cortex. We injected rabies virus into lobules VB–VIIIB of the vermis and used retrograde transneuronal transport of the virus to define disynaptic inputs to it. We found that large numbers of neurons in the primary motor cortex and in several motor areas on the medial wall of the hemisphere project to the vermis. Thus, our results challenge the classical view of the vermis and indicate that it no longer should be considered as entirely isolated from the cerebral cortex. Instead, lobules VB–VIIIB represent a site where the cortical motor areas can influence descending control systems involved in the regulation of whole-body posture and locomotion. We argue that the projection from the cerebral cortex to the vermis is part of the neural substrate for anticipatory postural adjustments and speculate that dysfunction of this system may underlie some forms of dystonia.

The authors use developmental changes in chromatin accessibility to identify thousands of enhancer elements that are active at different postnatal developmental stages in granule neurons of the cerebellum. Zic transcription factors were found to promote gene expression patterns key for neuronal maturation by binding to late-acting enhancer elements. To identify chromatin mechanisms of neuronal differentiation, we characterized chromatin accessibility and gene expression in cerebellar granule neurons (CGNs) of the developing mouse. We used DNase-seq to map accessibility of cis -regulatory elements and RNA-seq to profile transcript abundance across postnatal stages of neuronal differentiation in vivo and in culture. We observed thousands of chromatin accessibility changes as CGNs differentiated, and verified, using H3K27ac ChIP-seq, reporter gene assays and CRISPR-mediated activation, that many of these regions function as neuronal enhancers. Motif discovery in differentially accessible chromatin regions suggested a previously unknown role for the Zic family of transcription factors in CGN maturation. We confirmed the association of Zic with these elements by ChIP-seq and found, using knockdown, that Zic1 and Zic2 are required for coordinating mature neuronal gene expression patterns. Together, our data reveal chromatin dynamics at thousands of gene regulatory elements that facilitate the gene expression patterns necessary for neuronal differentiation and function.

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843–1864 (1960)] and [J. Sawada, D. S. Modha, “Synapse: Scalable energy-efficient neurosynaptic computing” in Application of Concurrency to System Design (ACSD) (2013), pp. 14–15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 10⁸-fold discrepancy between biological and lowest possible values of a neuron’s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).