Cerebellar mutism is linked to midbrain volatility and desynchronization from speech cortices

Abstract

Cerebellar mutism syndrome is a disorder of speech, movement and affect that can occur after tumour removal from the posterior fossa. Projections from the fastigial nuclei to the periaqueductal grey area were recently implicated in its pathogenesis, but the functional consequences of damaging these projections remain poorly understood. Here, we examine functional MRI data from patients treated for medulloblastoma to identify functional changes in key brain areas that comprise the motor system for speech, which occur along the timeline of acute speech impairment in cerebellar mutism syndrome. One hundred and twenty-four participants, all with medulloblastoma, contributed to the study: 45 with cerebellar mutism syndrome, 11 patients with severe postoperative deficits other than mutism, and 68 without either (asymptomatic). We first performed a data-driven parcellation to spatially define functional nodes relevant to the cohort that align with brain regions critical for the motor control of speech. We then estimated functional connectivity between these nodes during the initial postoperative imaging sessions to identify functional deficits associated with the acute phase of the disorder. We further analysed how functional connectivity changed over time within a subset of participants that had suitable imaging acquired over the course of recovery. Signal dispersion was also measured in the periaqueductal grey area and red nuclei to estimate activity in midbrain regions considered key targets of the cerebellum with suspected involvement in cerebellar mutism pathogenesis. We found evidence of periaqueductal grey dysfunction in the acute phase of the disorder, with abnormal volatility and desynchronization with neocortical language nodes. Functional connectivity with periaqueductal grey was restored in imaging sessions that occurred after speech recovery and was further shown to be increased with left dorsolateral prefrontal cortex. The amygdalae were also broadly hyperconnected with neocortical nodes in the acute phase. Stable connectivity differences between groups were broadly present throughout the cerebrum, and one of the most substantial differences-between Broca's area and the supplementary motor area-was found to be inversely related to cerebellar outflow pathway damage in the mutism group. These results reveal systemic changes in the speech motor system of patients with mutism, centred on limbic areas tasked with the control of phonation. These findings provide further support for the hypothesis that periaqueductal grey dysfunction (following cerebellar surgical injury) contributes to the transient postoperative non-verbal episode commonly observed in cerebellar mutism syndrome but highlights a potential role of intact cerebellocortical projections in chronic features of the disorder.

Keywords: fastigial nuclei; functional MRI; midbrain; posterior fossa syndrome.

Figure 1

Nodes of the speech motor network and primary analyses. (A) Simplified model of the speech motor system used for connectivity analysis, based on functional anatomy of the central speech motor system described by Holstege and Subramanian, and results of the independent component analysis parcellation. Values beneath node projections indicate z-scores used for visualization and approximate location of node peak. (B) Additional nodes chosen for trajectory analysis due to involvement in speech and motor planning. (C) Analysis of activity in midbrain targets of the cerebellum showing hyperactivity in the acute phase of cerebellar mutism syndrome (CMS). Although group differences decline, only periaqueductal grey area (PAG) shows reduced activity upon recovery of speech faculties. (D) Analysis of connectivity in the speech motor system in initial postoperative scans. Arrows indicate anatomical connections denoted by the model, while dashed lines indicate indirect functional connections. Colour corresponds to t-value as shown in the bar. PAG shows a deficit in connectivity with Broca’s area and the medial frontal cortices. Connectivity measures between nodes on the volitional (right-sided) branch of the motor system appear consistent with controls. Amyg = amygdalae; Broca = Broca’s area; L-DLPFC = left dorsolateral prefrontal cortex; L-SMC = left supramarginal cortex; mFC = medial frontal cortex; RNs = red nuclei; sM1 = primary somatomotor area for speech; SMA = supplementary motor area.

Figure 2

Analysis of connectivity trajectory in CMS and asymptomatic participants. (A) Connectivity examples for select node pairs with statistically significant differences in trajectory. Box and whisker plots show group distributions for each epoch. Connected dots show individual participant trajectories. Examples are shown of reactive hyperconnectivity, mixed reactive deficit with adaptive hyperconnectivity, and adaptive hyperconnectivity, respectively from left to right. (B) Plot of trajectory t-values over the epoch attributability value for each significant node pair. Outline and fill of each circle indicate the node pair. Labelled points i–iii correspond to the examples in A. Black arrow indicates the true centre position of the SMA-DLPFC pair, which was moved to improve clarity of the figure. (C) Visualization of results in B separated by epoch attributability and illustrated on the speech system model. Arrows indicate anatomical connections denoted by the model, while dashed lines indicate indirect functional connections. Colour corresponds to t-value as shown in the bar. Anatomical connections with no significant group difference are shown in black for greater visibility. PAG shows a deficit in connectivity with Broca’s area and the medial frontal cortices. PAG shows initial hypoconnectivity with mFC and volitional speech nodes, including the left supramarginal cortex. PAG shows adaptive hyperconnectivity with the left DLPFC with speech recovery, and speech motor cortices show adaptive hyperconnectivity with planning-related speech nodes. Amyg = amygdalae; Broca = Broca’s area; L-DLPFC = left dorsolateral prefrontal cortex; mFC = medial frontal cortex; sM1 = primary somatomotor area for speech; SMA = supplementary motor area; SMC = supramarginal cortex.

Figure 3

Persistent effects of surgery on connectivity. (A) Visualization of stable connectivity differences on the speech system model. Cortico-cortical connectivity is broadly increased on a persistent basis. Connectivity between the amygdalae (Amyg) and periaqueductal grey area (PAG) is uniquely reduced. (B) Overlap of spatially-mapped surgical damage with putative white matter of the superior cerebellar peduncles (SCP, shown in blue). Slice shown demonstrates the maximum extent of overlap between participants, with up to 60% overlap in voxels along the medial edge of the right superior cerebellar peduncle. Rendered slice is shown in neurological convention, rotated −17° about the x-axis to show cross section of the pathway. (C) Connectivity of supplementary motor area (SMA) and Broca’s area (Broca) in cerebellar mutism syndrome (CMS) plotted over the lesion load of the right superior cerebellar peduncle. Lesion load is generally low in the current CMS group and increasing lesion load is associated with diminished hyperconnectivity. L-DLPFC = left dorsolateral prefrontal cortex; mFC = medial frontal cortex; sM1 = primary somatomotor area for speech; SMC = supramarginal cortex.