Postural Ataxia in Cerebellar Downbeat Nystagmus: Its Relation to Visual, Proprioceptive and Vestibular Signals and Cerebellar Atrophy

<div><p>Background</p><p>The cerebellum integrates proprioceptive, vestibular and visual signals for postural control. Cerebellar patients with downbeat nystagmus (DBN) complain of unsteadiness of stance and gait as well as blurred vision and oscillopsia.</p><p>Objectives</p><p>The aim of this study was to elucidate the differential role of visual input, gaze eccentricity, vestibular and proprioceptive input on the postural stability in a large cohort of cerebellar patients with DBN, in comparison to healthy age-matched control subjects.</p><p>Methods</p><p>Oculomotor (nystagmus, smooth pursuit eye movements) and postural (postural sway speed) parameters were recorded and related to each other and volumetric changes of the cerebellum (voxel-based morphometry, SPM).</p><p>Results</p><p>Twenty-seven patients showed larger postural instability in all experimental conditions. Postural sway increased with nystagmus in the eyes closed condition but not with the eyes open. Romberg’s ratio remained stable and was not different from healthy controls. Postural sway did not change with gaze position or graviceptive input. It increased with attenuated proprioceptive input and on tandem stance in both groups but Romberg’s ratio also did not differ. Cerebellar atrophy (vermal lobule VI, VIII) correlated with the severity of impaired smooth pursuit eye movements of DBN patients.</p><p>Conclusions</p><p>Postural ataxia of cerebellar patients with DBN cannot be explained by impaired visual feedback. Despite oscillopsia visual feedback control on cerebellar postural control seems to be preserved as postural sway was strongest on visual deprivation. The increase in postural ataxia is neither related to modulations of single components characterizing nystagmus nor to deprivation of single sensory (visual, proprioceptive) inputs usually stabilizing stance. Re-weighting of multisensory signals and/or inappropriate cerebellar motor commands might account for this postural ataxia.</p></div

Postural sway as a function of horizontal gaze and head position (gravity).

<p>(<b>A</b>) Postural sway speed (PSS in cm/s) is shown for the gaze straight ahead position and right and left gaze positions (20°). PSS differed between groups but not within groups, i.e. PSS was not gaze dependent. (<b>B</b>) PSS is shown for different head positions in the straight ahead gaze position: head forward (45°), erect, and backward (30°) bended head position. PSS differed between groups but not within groups, i.e. PSS was not gravity dependent.</p

Postural sway as a function of target visibility.

<p>Original recordings of postural medio-lateral (ML, purple) and anterior-posterior (AP, green) center of displacement (CoP in mm) of a healthy control subject (upper trace) and a DBN patient (lower trace) on solid platform with eyes open (<b>A</b>) and closed (<b>B</b>). Group means ± standard error (SEM) are shown in (<b>C</b>) indicating significant larger PSS in patients but an indistinguishable increase of PSS in both groups on eye closure. Accordingly, Romberg’s ratio (<b>D</b>) is not different between groups.</p

Relation of behavioral parameters to cerebellar grey matter volume changes.

<p>Significant (FWE-corrected) gray matter volume reductions in DBN patients (healthy controls > patients) are depicted in cerebellar vermis (lobules VI and VIII and deep cerebellar nuclei) and cerebellar hemispheric lobules (V-VI) in axial and sagittal slices (p<0.001). GMV reduction (ROI of vermal cluster) increases with stronger impairment (gain) of smooth pursuit eye movements (B). Blue (healthy control) and red (patients) circles show individual data, crosses indicate the mean of gain and ROI-based volume ± standard deviation.</p

Study design.

<p>Experimental conditions are illustrated by symbols: visual input (eyes open/closed), gaze position (straight ahead, eccentric target positions), graviceptive (head down, erect, head up) and proprioceptive (platform vs. foam) input and, finally, different demands on postural control (parallel vs. tandem stance).</p

Altered Resting State Brain Networks in Parkinson’s Disease

<div><p>Parkinson’s disease (PD) is a neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra leading to dysfunctional cortico-striato-thalamic-cortical loops. In addition to the characteristic motor symptoms, PD patients often show cognitive impairments, affective changes and other non-motor symptoms, suggesting system-wide effects on brain function. Here, we used functional magnetic resonance imaging and graph-theory based analysis methods to investigate altered whole-brain intrinsic functional connectivity in PD patients (n = 37) compared to healthy controls (n = 20). Global network properties indicated less efficient processing in PD. Analysis of brain network modules pointed to increased connectivity within the sensorimotor network, but decreased interaction of the visual network with other brain modules. We found lower connectivity mainly between the cuneus and the ventral caudate, medial orbitofrontal cortex and the temporal lobe. To identify regions of altered connectivity, we mapped the degree of intrinsic functional connectivity both on ROI- and on voxel-level across the brain. Compared to healthy controls, PD patients showed lower connectedness in the medial and middle orbitofrontal cortex. The degree of connectivity was also decreased in the occipital lobe (cuneus and calcarine), but increased in the superior parietal cortex, posterior cingulate gyrus, supramarginal gyrus and supplementary motor area. Our results on global network and module properties indicated that PD manifests as a disconnection syndrome. This was most apparent in the visual network module. The higher connectedness within the sensorimotor module in PD patients may be related to compensation mechanism in order to overcome the functional deficit of the striato-cortical motor loops or to loss of mutual inhibition between brain networks. Abnormal connectivity in the visual network may be related to adaptation and compensation processes as a consequence of altered motor function. Our analysis approach proved sensitive for detecting disease-related localized effects as well as changes in network functions on intermediate and global scale.</p></div

Effect of Mild Thyrotoxicosis on Performance and Brain Activations in a Working Memory Task - Fig 1

<p><b>(A) Regions of BOLD signal intensity in the whole brain for effect of difficulty level in n-back task.</b> Results for whole brain analysis for all subjects (n = 28), threshold at p<0.05 corrected for FWE. BOLD signal intensity attenuation especially in parahippocampal gyrus, supplementary motor area, dorsolateral prefrontal cortex, rolandic operculum, bilateral anterior cingulate cortex and posteriorcerebellum. The difficulty of the n-back task condition is the difference in difficulty of 0-back (= control task), 1-back (= easier task) and 2-back (= harder task) condition. <b>(B) Difficuly level in n-back task and thyroid hormone intake interaction.</b> Results for whole brain analysis with one-way ANOVA in all subjects (n = 28). A cluster in the right parahippocampal region and in the right prefrontal cortex survived the statistical threshold at p<0.005, uncorrected.</p

Between-group differences in voxel degree.

<p><b>Notes:</b> Clusters where differences in the voxel degree between patients and controls are observed (cluster defining threshold p<0.005; the 0.05 FDR-corrected critical cluster size was 184). Anatomical region, cluster level probability (0.05 FDR-corrected), number of voxels per cluster (k), local maxima in MNI coordinates and peak T-scores are listed.</p

Demographic data on patients and healthy controls.

<p>Notes: UPDRS = Unified Parkinson’s Disease Rating Scale.</p

Comparison of the two parcellation approaches.

<p>A) The mean voxel correlation (Fisher transformed) within the ROIs for the parcellation according to the AAL atlas and the parcellation derived from the modular structure within the AAL regions (sub-AAL). B) The number of voxels per ROI for AAL and sub-AAL parcellation is plotted. C) ROIs significantly correlated to the left posterior cingulate cortex (PCC) ROI for the AAL parcellation. D) ROIs significantly correlated to the seed ROI in the left PCC for the sub-AAL ROIs.</p