Multimodal assessment of autonomic dysfunction in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with predominant progressive degeneration of motor neurons and motor deficits, but non-motor symptoms (NMS) such as cognitive and behavioural deficits are frequent and underestimated in current diagnostic pathways. Autonomic dysfunction has occasionally been described, although its frequency and relevance are unclear. The aim of this study was to investigate the role of the autonomic nervous system in ALS using a multimodal approach. METHODS: Thirty-seven ALS patients and 40 healthy sex- and age-matched controls were included. NMS were studied with the NMS assessment scale for Parkinson's disease and an autonomic subscale was calculated. Cardioautonomic innervation at rest and whilst standing was assessed by different parameters of heart rate variability. Morphological changes (cross-sectional area) of the vagus and median nerves for control were measured with high-resolution ultrasound. RESULTS: Non-motor symptoms in general were more frequent in ALS patients and correlated inversely with the ALS Functional Rating Scale whereas the autonomic subscore of the NMS assessment scale for Parkinson's disease did not differ between the two groups and was not related to functional impairment. Cardioautonomic assessment solely revealed an increased heart rate at rest in ALS patients, whereas the other heart rate variability parameters did not differ from controls. Structural sonographic investigation of the vagus and median nerves was similar in both groups. CONCLUSIONS: Using a multimodal approach evidence was found for a rather mild cardio-sympathetic overactivity in ALS patients. Overall, autonomic dysfunction seems to be subtle and is not related to the functional state of ALS patients.

Differentiating neurodegenerative parkinsonian syndromes using vestibular evoked myogenic potentials and balance assessment.

OBJECTIVE: Vestibular evoked myogenic potentials (VEMP) were investigated to differentiate between parkinsonian syndromes. We correlated balance and VEMP parameters to investigate the VEMP brainstem circuits as possible origin for postural instability. METHODS: We assessed clinical status, ocular and cervical VEMP (oVEMP, cVEMP) and conducted a balance assessment (posturography, Activities-specific Balance Confidence Scale, Berg Balance Scale, modified Barthel Index) in 76 subjects: 30 with Parkinson's disease (PD), 16 with atypical parkinsonism (AP) and 30 healthy controls. VEMP were elicited by using a mini-shaker on the forehead. RESULTS: Patients with PD had a prolonged oVEMP n10 in comparison to controls and prolonged p15 compared to controls and AP. Patients with AP showed reduced oVEMP amplitudes compared to PD and controls. CVEMP did not differ between groups. Postural impairment was higher in AP compared to controls and PD, particularly in the rating scales. No correlations between VEMP and posturography were found. A support vector machine classifier was able to automatically classify controls and patient subgroups with moderate to good accuracy based on oVEMP latencies and balance questionnaires. CONCLUSIONS: Both oVEMP and posturography, but not cVEMP, may be differentially affected in PD and AP. We did not find evidence that impairment of the cVEMP or oVEMP pathways is directly related to postural impairment. SIGNIFICANCE: OVEMP and balance assessment could be implemented in the differential diagnostic work-up of parkinsonian syndromes.

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders.

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network-CNN, Support Vector Machine-SVM, K-Nearest Neighbors-KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: "healthy" and "patient") was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

Correlation between sonographic morphology and function of the cervical vagus nerves.

The heart receives parasympathetic and to a lesser degree sympathetic input via the vagus nerve. Here, we investigated whether morphological changes of the cervical vagus nerves (VN) as assessed by high-resolution ultrasound (HRUS) correlated with the autonomic cardiac innervation. Measurement of heart rate variability (HRV) and HRUS of the VNs were performed in 88 healthy subjects (50 female; mean age 56 ±â€¯18 years). HRV parameters and the cross-sectional area (CSA) of the VNs correlated both inversely with age. We also found an inverse correlation between the left VN-CSA and HRV as well as parasympathetic parameters. The results imply an asymmetric parasympathetic (vagal) innervation of the heart.

Dual-Site Transcranial Magnetic Stimulation for the Treatment of Parkinson's Disease.

Abnormal oscillatory activity in the subthalamic nucleus (STN) may be relevant for motor symptoms in Parkinson's disease (PD). Apart from deep brain stimulation, transcranial magnetic stimulation (TMS) may be suitable for altering these oscillations. We speculated that TMS to different cortical areas (primary motor cortex, M1, and dorsal premotor cortex, PMd) may activate neuronal subpopulations within the STN via corticofugal neurons projecting directly to the nucleus. We hypothesized that PD symptoms can be ameliorated by a lasting decoupling of STN neurons by associative dual-site repetitive TMS (rTMS). Associative dual-site rTMS (1 Hz) directed to PMd and M1 ("ADS-rTMS") was employed in 20 PD patients treated in a blinded, placebo-controlled cross-over design. Results: No adverse events were noted. We found no significant improvement in clinical outcome parameters (videography of MDS-UPDRS-III, finger tapping, spectral tremor power). Variation of the premotor stimulation site did not induce beneficial effects either. A single session of ADS-rTMS was tolerated well, but did not produce a clinically meaningful benefit on Parkinsonian motor symptoms. Successful treatment using TMS targeting subcortical nuclei may require an intervention over several days or more detailed physiological information about the individual brain state and stimulation-induced subcortical effects.

Neural correlates of the DemTect in Alzheimer's disease and frontotemporal lobar degeneration - A combined MRI & FDG-PET study.

Valid screening devices are critical for an early diagnosis of dementia. The DemTect is such an internationally accepted tool. We aimed to characterize the neural networks associated with performance on the DemTect's subtests in two frequent dementia syndromes: early Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). Voxel-based group comparisons of cerebral glucose utilization (as measured by F-18-fluorodeoxyglucose positron emission tomography) and gray matter atrophy (as measured by structural magnetic resonance imaging) were performed on data from 48 subjects with AD (n = 21), FTLD (n = 14) or subjective cognitive impairment (n = 13) as a control group. We performed group comparisons and correlation analyses between multimodal imaging data and performance on the DemTect's subtests. Group comparisons showed regional patterns consistent with previous findings for AD and FTLD. Interestingly, atrophy dominated in FTLD, whereas hypometabolism in AD. Across diagnostic groups performance on the "wordlist" subtest was positively correlated with glucose metabolism in the left temporal lobe. The "number transcoding" subtest was significantly associated with glucose metabolism in both a predominantly left lateralized frontotemporal network and a parietooccipital network including parts of the basal ganglia. Moreover, this subtest was associated with gray matter density in an extensive network including frontal, temporal, parietal and occipital areas. No significant correlates were observed for the "supermarket task" subtest. Scores on the "digit span reverse" subtest correlated with glucose metabolism in the left frontal cortex, the bilateral putamen, the head of caudate nucleus and the anterior insula. Disease-specific correlation analyses could partly verify or extend the correlates shown in the analyses across diagnostic groups. Correlates of gray matter density were found in FTLD for the "number transcoding" subtest and the "digit span reverse" subtest. Correlates of glucose metabolism were found in AD for the "wordlist" subtest and in FTLD for the "digit span reverse" subtest. Our study contributes to the understanding of the neural correlates of cognitive deficits in AD and FTLD and supports an external validation of the DemTect providing preliminary conclusions about disease-specific correlates.