Journal articles

Objectives Genetic variant classification is a challenge in rare adult-onset disorders as in SCA-PRKCG (prior spinocerebellar ataxia type 14) with mostly private conventional mutations and nonspecific phenotype. We here propose a refined approach for clinicogenetic diagnosis by including protein modeling and provide for confirmed SCA-PRKCG a comprehensive phenotype description from a German multi-center cohort, including standardized 3D MR imaging. Methods This cross-sectional study prospectively obtained neurological, neuropsychological, and brain imaging data in 33 PRKCG variant carriers. Protein modeling was added as a classification criterion in variants of uncertain significance (VUS). Results Our sample included 25 cases confirmed as SCA-PRKCG (14 variants, thereof seven novel variants) and eight carriers of variants assigned as VUS (four variants) or benign/likely benign (two variants). Phenotype in SCA-PRKCG included slowly progressive ataxia (onset at 4-50 years), preceded in some by early-onset nonprogressive symptoms. Ataxia was often combined with action myoclonus, dystonia, or mild cognitive-affective disturbance. Inspection of brain MRI revealed nonprogressive cerebellar atrophy. As a novel finding, a previously not described T2 hyperintense dentate nucleus was seen in all SCA-PRKCG cases but in none of the controls. Interpretation In this largest cohort to date, SCA-PRKCG was characterized as a slowly progressive cerebellar syndrome with some clinical and imaging features suggestive of a developmental disorder. The observed non-ataxia movement disorders and cognitive-affective disturbance may well be attributed to cerebellar pathology. Protein modeling emerged as a valuable diagnostic tool for variant classification and the newly described T2 hyperintense dentate sign could serve as a supportive diagnostic marker of SCA-PRKCG.

Axonal connections are widely regarded as faithful transmitters of neuronal signals with fixed delays. The reasoning behind this is that extracellular potentials caused by spikes travelling along axons are too small to have an effect on other axons. Here we devise a computational framework that allows us to study the effect of extracellular potentials generated by spike volleys in axonal fibre bundles on axonal transmission delays. We demonstrate that, although the extracellular potentials generated by single spikes are of the order of microvolts, the collective extracellular potential generated by spike volleys can reach several millivolts. As a consequence, the resulting depolarisation of the axonal membranes increases the velocity of spikes, and therefore reduces axonal delays between brain areas. Driving a neural mass model with such spike volleys, we further demonstrate that only ephaptic coupling can explain the reduction of stimulus latencies with increased stimulus intensities, as observed in many psychological experiments.

Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.

Background - Transcranial direct current stimulation (tDCS), a neuromodulatory non-invasive brain stimulation technique, has shown promising results in basic and clinical studies. The known interindividual variability of the effects, however, limits the efficacy of the technique. Recently we reported neurophysiological effects of tDCS applied over the primary motor cortex at the group level, based on data from twenty-nine participants who received 15min of either sham, 0.5, 1.0, 1.5 or 2.0 mA anodal, or cathodal tDCS. The neurophysiological effects were evaluated via changes in: 1) transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEP), and 2) cerebral blood flow (CBF) measured by functional magnetic resonance imaging (MRI) via arterial spin labeling (ASL). At the group level, dose-dependent effects of the intervention were obtained, which however displayed interindividual variability. - Method - In the present study, we investigated the cause of the observed inter-individual variability. To this end, for each participant, a MRI-based realistic head model was designed to 1) calculate anatomical factors and 2) simulate the tDCS- and TMS-induced electrical fields (EFs). We first investigated at the regional level which individual anatomical factors explained the simulated EFs (magnitude and normal component). Then, we explored which specific anatomical and/or EF factors predicted the neurophysiological outcomes of tDCS. - Results - The results highlight a significant negative correlation between regional electrode-to-cortex distance (rECD) as well as regional CSF (rCSF) thickness, and the individual EF characteristics. In addition, while both rCSF thickness and rECD anticorrelated with tDCS-induced physiological changes, EFs positively correlated with the effects. - Conclusion - These results provide novel insights into the dependency of the neuromodulatory effects of tDCS on individual physical factors.