Reduced spatial frequency differentiation and sex‐related specificities in fearful face detection in autism: Insights from EEG and the predictive brain model

International audienceFace processing relies on predictive processes driven by low spatial frequencies (LSF) that convey coarse information prior to fine information conveyed by high spatial frequencies. However, autistic individuals might have atypical predictive processes, contributing to facial processing difficulties. This may be more normalized in autistic females, who often exhibit better socio‐communicational abilities than males. We hypothesized that autistic females would display a more typical coarse‐to‐fine processing for socio‐emotional stimuli compared to autistic males. To test this hypothesis, we asked adult participants (44 autistic, 51 non‐autistic) to detect fearful faces among neutral faces, filtered in two orders: from coarse‐to‐fine (CtF) and from fine‐to‐coarse (FtC). Results show lower d’ values and longer reaction times for fearful detection in autism compared to non‐autistic (NA) individuals, regardless of the filtering order. Both groups presented shorter P100 latency after CtF compared to FtC, and larger amplitude for N170 after FtC compared to CtF. However, autistic participants presented a reduced difference in source activity between CtF and FtC in the fusiform. There was also a more spatially spread activation pattern in autistic females compared to NA females. Finally, females had faster P100 and N170 latencies, as well as larger occipital activation for FtC sequences than males, irrespective of the group. Overall, the results do not suggest impaired predictive processes from LSF in autism despite behavioral differences in fear detection. However, they do indicate reduced brain modulation by spatial frequency in autism. In addition, the findings highlight sex differences that warrant consideration in understanding autistic females

Toward individualized medicine in stroke-The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke.

Despite recent improvements, complete motor recovery occurs in <15% of stroke patients. To improve the therapeutic outcomes, there is a strong need to tailor treatments to each individual patient. However, there is a lack of knowledge concerning the precise neuronal mechanisms underlying the degree and course of motor recovery and its individual differences, especially in the view of brain network properties despite the fact that it became more and more clear that stroke is a network disorder. The TiMeS project is a longitudinal exploratory study aiming at characterizing stroke phenotypes of a large, representative stroke cohort through an extensive, multi-modal and multi-domain evaluation. The ultimate goal of the study is to identify prognostic biomarkers allowing to predict the individual degree and course of motor recovery and its underlying neuronal mechanisms paving the way for novel interventions and treatment stratification for the individual patients. A total of up to 100 patients will be assessed at 4 timepoints over the first year after the stroke: during the first (T1) and third (T2) week, then three (T3) and twelve (T4) months after stroke onset. To assess underlying mechanisms of recovery with a focus on network analyses and brain connectivity, we will apply synergistic state-of-the-art systems neuroscience methods including functional, diffusion, and structural magnetic resonance imaging (MRI), and electrophysiological evaluation based on transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) and electromyography (EMG). In addition, an extensive, multi-domain neuropsychological evaluation will be performed at each timepoint, covering all sensorimotor and cognitive domains. This project will significantly add to the understanding of underlying mechanisms of motor recovery with a strong focus on the interactions between the motor and other cognitive domains and multimodal network analyses. The population-based, multi-dimensional dataset will serve as a basis to develop biomarkers to predict outcome and promote personalized stratification toward individually tailored treatment concepts using neuro-technologies, thus paving the way toward personalized precision medicine approaches in stroke rehabilitation

Emotional processing in Parkinson's disease and anxiety: an EEG study of visual affective word processing

A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems