Cerebellar zonal patterning relies on Purkinje cell neurotransmission.

Cerebellar circuits are patterned into an array of topographic parasagittal domains called zones. The proper connectivity of zones is critical for motor coordination and motor learning, and in several neurological diseases cerebellar circuits degenerate in zonal patterns. Despite recent advances in understanding zone function, we still have a limited understanding of how zones are formed. Here, we focused our attention on Purkinje cells to gain a better understanding of their specific role in establishing zonal circuits. We used conditional mouse genetics to test the hypothesis that Purkinje cell neurotransmission is essential for refining prefunctional developmental zones into sharp functional zones. Our results show that inhibitory synaptic transmission in Purkinje cells is necessary for the precise patterning of Purkinje cell zones and the topographic targeting of mossy fiber afferents. As expected, blocking Purkinje cell neurotransmission caused ataxia. Using in vivo electrophysiology, we demonstrate that loss of Purkinje cell communication altered the firing rate and pattern of their target cerebellar nuclear neurons. Analysis of Purkinje cell complex spike firing revealed that feedback in the cerebellar nuclei to inferior olive to Purkinje cell loop is obstructed. Loss of Purkinje neurotransmission also caused ectopic zonal expression of tyrosine hydroxylase, which is only expressed in adult Purkinje cells when calcium is dysregulated and if excitability is altered. Our results suggest that Purkinje cell inhibitory neurotransmission establishes the functional circuitry of the cerebellum by patterning the molecular zones, fine-tuning afferent circuitry, and shaping neuronal activity.

Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.

OBJECTIVE: To develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs). METHODS: Initial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively. RESULTS: We performed 5-fold cross-validation with the eligible patients (n = 103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state-of-the-art segmentation model nnU-Net (dice coefficient of 86.45%, p = 0.16). The TEP performance is significantly better than the single-task prediction model (AUC = 0.60, p = 0.01) and marginally better than a radiomics-based prediction model (AUC = 0.70, p = 0.17). CONCLUSION: The proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management. LEVEL OF EVIDENCE: NA Laryngoscope, 133:2754-2760, 2023.

Automated Detection of Vestibular Schwannoma Growth Using a Two-Dimensional U-Net Convolutional Neural Network.

OBJECTIVES/HYPOTHESIS: To determine if an automated vestibular schwannoma (VS) segmentation model has comparable performance to using the greatest linear dimension to detect growth. STUDY DESIGN: Case-control Study. METHODS: Patients were selected from an internal database who had an initial gadolinium-enhanced T1-weighted magnetic resonance imaging scan and a follow-up scan captured at least 5 months later. Two observers manually segmented the VS to compute volumes, and one observer's segmentations were used to train a convolutional neural network model to automatically segment the VS and determine the volume. The results of automatic segmentation were compared to the observer whose measurements were not used in model development to measure agreement. We then examined the sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) to compare automated volumetric growth detection versus using the greatest linear dimension. Growth detection determined by the external observer's measurements served as the gold standard. RESULTS: A total of 65 patients and 130 scans were studied. The automated method of segmentation demonstrated excellent agreement with the observer whose measurements were not used for model development for the initial scan (interclass correlational coefficient [ICC] = 0.995; 95% confidence interval [CI]: 0.991-0.997) and follow-up scan (ICC = 0.960; 95% CI: 0.935-0.975). The automated method of segmentation demonstrated increased sensitivity (72.2% vs. 63.9%), specificity (79.3% vs. 69.0%), and AUC (0.822 vs. 0.701) compared to using the greatest linear dimension for growth detection. CONCLUSIONS: In detecting VS growth, a convolutional neural network model outperformed using the greatest linear dimension, demonstrating a potential application of artificial intelligence methods to VS surveillance. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E619-E624, 2021.

Prediction of Vestibular Schwannoma Enlargement After Radiosurgery Using Tumor Shape and MRI Texture Features.

OBJECTIVE: Determine if vestibular schwannoma (VS) shape and MRI texture features predict significant enlargement after stereotactic radiosurgery (SRS). STUDY DESIGN: Retrospective case review. SETTING: Tertiary referral center. PATIENTS: Fifty-three patients were selected who underwent SRS and had a contrast-enhanced T1 sequence planning MRI scan and a follow-up contrast enhanced T1 MRI available for review. Median follow-up of 6.5 months (interquartile range/IQR, 5.9-7.4). Median pretreatment tumor volume was 1,006 mm3 (IQR, 465-1,794). INTERVENTIONS: Stereotactic radiosurgery. MAIN OUTCOME MEASURES: Texture and shape features from the SRS planning scans were extracted and used to train a linear support vector machine binary classifier to predict post-SRS enlargement >20% of the pretreatment volume. Sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and positive likelihood ratio were computed. A stratified analysis based on pretreatment tumor volume greater or less than the median volume was also performed. RESULTS: The model had a sensitivity of 92%, specificity of 65%, AUC of 0.75, and a positive likelihood ratio of 2.6 (95% CI 1.4-5.0) for predicting post-SRS enlargement of >20%. In the larger tumor subgroup, the model had a sensitivity of 87%, specificity of 73%, AUC of 0.76, and a positive likelihood ratio of 3.2 (95% CI 1.2-8.5). In the smaller tumor subgroup, the model had a sensitivity of 95%, specificity of 50%, AUC of 0.65, and a positive likelihood ratio of 1.9 (95% CI 0.8-4.3). CONCLUSIONS: VS shape and texture features may be useful inputs for machine learning models that predict VS enlargement after SRS.

MRI Texture Features are Associated with Vestibular Schwannoma Histology.

OBJECTIVES/HYPOTHESIS: To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS). STUDY DESIGN: Retrospective case-series. METHODS: Patients were selected from an internal database of those who had a gadolinium-enhanced T1-weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut-off value. RESULTS: A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut-off values for mucin (2.3; 95% CI 1.2-4.3), hemosiderin (1.5; 95% CI 1.04-2.1), lymphocytes (3.8; 95% CI 1.2-11.7), and necrosis (1.5; 95% CI 1.1-2.2). CONCLUSIONS: MRI texture features are associated with underlying histology in VS. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E2000-E2006, 2021.

Comparing Cochlear Duct Lengths Between CT and MR Images Using an Otological Surgical Planning Software.

OBJECTIVE: We sought to examine the intra- and interobserver variability in measuring the cochlear duct length (CDL) from magnetic resonance imaging (MRI) images versus computed tomography (CT) images using an otological surgical planning software that uses measurements of the basal turn diameter and cochlear width to estimate the CDL. PATIENTS: Twenty-one adult cochlear implant patients with preoperative MRI and CT images. INTERVENTION: Three fellowship-trained neurotologists served as the raters in the study. One rater measured the CDL using preoperative CT scans to serve as the benchmark. Two of the raters measured the CDL on preoperative MRI scans. One rater also remeasured the scans using MRI images after a period of 1 week to assess intraobserver variability. MAIN OUTCOME MEASURE: Intraclass correlational coefficients were calculated to assess for intra- and interobserver agreement. RESULTS: The mean CDL measured from the CT scans was 32.7 ±â€Š2.0 mm (range 29.4 - 37.6 mm). The mean difference between the raters when measuring the CDL using MRI scans was -0.15 ±â€Š2.1 mm (range -3.2 to 4.3 mm). The intraclass correlational coefficients for inter-rater reliability of CDL determination using MRI scans was judged as fair to excellent (0.68; 95% CI 0.41-0.84). The intrarater reliability of CDL determination using MRI scans was judged at fair to excellent (0.73; 95% CI 0.491-0.866). CONCLUSION: We demonstrate that a validated otological surgical planning software for estimating the CDL preoperatively had comparable performance using MRI scans versus the gold-standard CT scans.

Purkinje cell compartmentalization in the cerebellum of the spontaneous mutant mouse dreher.

The cerebellar morphological phenotype of the spontaneous neurological mutant mouse dreher (Lmx1a(dr-J)) results from cell fate changes in dorsal midline patterning involving the roof plate and rhombic lip. Positional cloning revealed that the gene Lmx1a, which encodes a LIM homeodomain protein, is mutated in dreher, and is expressed in the developing roof plate and rhombic lip. Loss of Lmx1a causes reduction of the roof plate, an important embryonic signaling center, and abnormal cell fate specification within the embryonic cerebellar rhombic lip. In adult animals, these defects result in variable, medial fusion of the cerebellar vermis and posterior cerebellar vermis hypoplasia. It is unknown whether deleting Lmx1a results in displacement or loss of specific lobules in the vermis. To distinguish between an ectopic and absent vermis, the expression patterns of two Purkinje cell-specific compartmentation antigens, zebrin II/aldolase C and the small heat shock protein HSP25 were analyzed in dreher cerebella. The data reveal that despite the reduction in volume and abnormal foliation of the cerebellum, the transverse zones and parasagittal stripe arrays characteristic of the normal vermis are present in dreher, but may be highly distorted. In dreher mutants with a severe phenotype, zebrin II stripes are fragmented and distributed non-symmetrically about the cerebellar midline. We conclude that although Purkinje cell agenesis or selective Purkinje cell death may contribute to the dreher phenotype, our data suggest that aberrant anlage patterning and granule cell development lead to Purkinje cell ectopia, which ultimately causes abnormal cerebellar architecture in dreher.

Architecture and development of olivocerebellar circuit topography.

The cerebellum has a simple tri-laminar structure that is comprised of relatively few cell types. Yet, its internal micro-circuitry is anatomically, biochemically, and functionally complex. The most striking feature of cerebellar circuit complexity is its compartmentalized topography. Each cell type within the cerebellar cortex is organized into an exquisite map; molecular expression patterns, dendrite projections, and axon terminal fields divide the medial-lateral axis of the cerebellum into topographic sagittal zones. Here, we discuss the mechanisms that establish zones and highlight how gene expression and neural activity contribute to cerebellar pattern formation. We focus on the olivocerebellar system because its developmental mechanisms are becoming clear, its topographic termination patterns are very precise, and its contribution to zonal function is debated. This review deconstructs the architecture and development of the olivocerebellar pathway to provide an update on how brain circuit maps form and function.