Refraining from spontaneous face touch is linked to personality traits, reduced memory performance and EEG changes.

Spontaneous touches of one's face (sFST) were suggested to serve cognitive-emotional regulation processes. During the pandemic, refraining from face-touching was recommended, yet, accompanying effects and the influence of personal attributes remain unclear. Ninety participants (45 female, 45 male) filled out a questionnaire concerning personality, anxiety screening and ADHD screening. Subsequently, they performed a delayed verbal memory recall task four times. After two times, sixty participants were instructed to refrain from face-touching (experimental group). Thirty participants did not receive behavioral instructions (control group). To identify face-touches and conduct further analysis, Video, EMG, and EEG data were recorded. Two samples were formed, depending on the adherence to completely refrain from face-touching (adherent, non-adherent sample) and compared to each other and the control group. EEG analyses uncovered that refraining from face-touching is accompanied by increased beta-power at sensorimotor sites and, exclusively in the non-adherent sample, at frontal sites. Decreased memory performance was found exclusively in subsamples, who non-adherently touched their face while retaining words. In terms of questionnaire results, lower Conscientiousness and higher ADHD screening scores were revealed by the non-adherent compared to the adherent sample. No differences were found among the subsamples. The presented results indicate that refraining from face-touching is related to personal attributes, accompanied by neurophysiological shifts and for a portion of humans by lower memory performance, supporting the notion that sFST serve processes beyond sensorimotor.

Spontaneity matters! Network alterations before and after spontaneous and active facial self-touches: An EEG functional connectivity study.

BACKGROUND: Despite humans frequently performing spontaneous facial self-touches (sFST), the function of this behavior remains speculative. sFST have been discussed in the context of self-regulation, emotional homeostasis, working memory processes, and attention focus. First evidence indicates that sFST and active facial self-touches (aFST) are neurobiologically different phenomena. The aim of the present analysis was to examine EEG-based connectivity in the course of sFST and aFST to test the hypotheses that sFST affect brain network interactions relevant for other than sensorimotor processes. METHODS: To trigger spontaneous FST a previously successful setting was used: 60 healthy participants manually explored two haptic stimuli and held the shapes of the stimuli in memory for a 14 min retention interval. Afterwards the shapes were drawn on a sheet of paper. During the retention interval, artifact-free EEG-data of 97 sFST by 32 participants were recorded. At the end of the experiment, the participants performed aFST with both hands successively. For the EEG-data, connectivity was computed and compared between the phases before and after sFST and aFST and between the respective before-and the after-phases. RESULTS: For the before-after comparison, brainwide distributed significant connectivity differences (p < .00079) were observed for sFST, but not for aFST. Additionally, comparing the before- and after-phases of sFST and aFST, respectively, revealed increased similarity between the after-phases than between the before-phases. CONCLUSION: The results support the assumption that sFST and aFST are neurobiologically different phenomena. Furthermore, the aligned network properties of the after-phases compared to the before-phases indicate that sFST serve self-regulatory functions that aFST do not serve.

Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology.

Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.

Effects of Rubber Hand Illusion and Excitatory Theta Burst Stimulation on Tactile Sensation: A Pilot Study.

Synchronous visuotactile stimulation on the own hidden hand and a visible fake limb can alter bodily self-perception and influence spontaneous neuroplasticity. The rubber hand illusion (RHI) paradigm experimentally produces an illusion of rubber hand ownership and arm shift by simultaneously stroking a rubber hand in view and a participant's visually occluded hand. The aim of this cross-over, placebo-controlled, single-blind study was to assess whether RHI, in combination with high-frequency repetitive transcranial magnetic stimulation (rTMS) given as intermittent (excitatory) theta burst stimulation (iTBS) applied over the hand area of the primary sensory region (S1) can enhance tactile sensation in a group of 21 healthy subjects and one patient with cervical spinal cord injury. Four sessions covered all combinations of real and sham stimulations of the RHI and the TBS: real TBS and real RHI, real TBS and sham RHI, sham TBS and real RHI, and both conditions sham. The condition sham TBS and real RHI shows the greatest effect on the proprioceptive drift (median 2.3 cm, IQR 2) and on the score of RHI questionnaires (median 3, IQR 2) in the control group as well as in the real-real condition (median 2, IQR 2). The sham TBS and real RHI condition also shows the best results in the electrical perception test of the patient (median 1.9 mA). Conversely, the upregulation of the cortical excitability of S1 via TBS seems to impair the effect of the RHI. This might be due to a strengthening of the top-down connection between the central nervous system and the periphery, diminishing the RHI. This finding helps in understanding the mechanisms of top-down and bottom-up mechanisms in healthy subjects and patients with spinal cord injury. The RHI paradigm could represent an interesting therapeutic approach in improving tactile sensation and rTMS techniques could modulate these effects. Yet, further studies are needed, to examine the direction of the interaction effect of TMS and RH.

Reliability of EEG Measures of Interaction: A Paradigm Shift Is Needed to Fight the Reproducibility Crisis.

Measures of interaction (connectivity) of the EEG are at the forefront of current neuroscientific research. Unfortunately, test-retest reliability can be very low, depending on the measure and its estimation, the EEG-frequency of interest, the length of the signal, and the population under investigation. In addition, artifacts can hamper the continuity of the EEG signal, and in some clinical situations it is impractical to exclude artifacts. We aimed to examine factors that moderate test-retest reliability of measures of interaction. The study involved 40 patients with a range of neurological diseases and memory impairments (age median: 60; range 21-76; 40% female; 22 mild cognitive impairment, 5 subjective cognitive complaints, 13 temporal lobe epilepsy), and 20 healthy controls (age median: 61.5; range 23-74; 70% female). We calculated 14 measures of interaction based on the multivariate autoregressive model from two EEG-recordings separated by 2 weeks. We characterized test-retest reliability by correlating the measures between the two EEG-recordings for variations of data length, data discontinuity, artifact exclusion, model order, and frequency over all combinations of channels and all frequencies, individually for each subject, yielding a correlation coefficient for each participant. Excluding artifacts had strong effects on reliability of some measures, such as classical, real valued coherence (~0.1 before, ~0.9 after artifact exclusion). Full frequency directed transfer function was highly reliable and robust against artifacts. Variation of data length decreased reliability in relation to poor adjustment of model order and signal length. Variation of discontinuity had no effect, but reliabilities were different between model orders, frequency ranges, and patient groups depending on the measure. Pathology did not interact with variation of signal length or discontinuity. Our results emphasize the importance of documenting reliability, which may vary considerably between measures of interaction. We recommend careful selection of measures of interaction in accordance with the properties of the data. When only short data segments are available and when the signal length varies strongly across subjects after exclusion of artifacts, reliability becomes an issue. Finally, measures which show high reliability irrespective of the presence of artifacts could be extremely useful in clinical situations when exclusion of artifacts is impractical.

Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases.

Alterations of interaction (connectivity) of the EEG reflect pathological processes in patients with neurologic disorders. Nevertheless, it is questionable whether these patterns are reliable over time in different measures of interaction and whether this reliability of the measures is the same across different patient populations. In order to address this topic we examined 22 patients with mild cognitive impairment, five patients with subjective cognitive complaints, six patients with right-lateralized temporal lobe epilepsy, seven patients with left lateralized temporal lobe epilepsy, and 20 healthy controls. We calculated 14 measures of interaction from two EEG-recordings separated by 2 weeks. In order to characterize test-retest reliability, we correlated these measures for each group and compared the correlations between measures and between groups. We found that both measures of interaction as well as groups differed from each other in terms of reliability. The strongest correlation coefficients were found for spectrum, coherence, and full frequency directed transfer function (average rho > 0.9). In the delta (2-4 Hz) range, reliability was lower for mild cognitive impairment compared to healthy controls and left lateralized temporal lobe epilepsy. In the beta (13-30 Hz), gamma (31-80 Hz), and high gamma (81-125 Hz) frequency ranges we found decreased reliability in subjective cognitive complaints compared to mild cognitive impairment. In the gamma and high gamma range we found increased reliability in left lateralized temporal lobe epilepsy patients compared to healthy controls. Our results emphasize the importance of documenting reliability of measures of interaction, which may vary considerably between measures, but also between patient populations. We suggest that studies claiming clinical usefulness of measures of interaction should provide information on the reliability of the results. In addition, differences between patient groups in reliability of interactions in the EEG indicate the potential of reliability to serve as a new biomarker for pathological memory decline as well as for epilepsy. While the brain concert of information flow is generally variable, high reliability, and thus, low variability may reflect abnormal firing patterns.

(S)-Ketamine in Refractory and Super-Refractory Status Epilepticus: A Retrospective Study.

OBJECTIVE: The aim was to describe the safety and efficacy of (S)-ketamine [(S)-KET] in a series of patients with refractory and super-refractory status epilepticus (RSE and SRSE) in a specialized neurological intensive care unit (NICU). METHODS: We retrospectively analyzed the data of patients with RSE and SRSE treated with (S)-KET in the NICU, Salzburg, Austria, from 2011 to 2015. Data collection included demographic features, clinical presentation, diagnosis, electroencephalogram (EEG) data, anticonvulsant treatment, timing, and duration of treatment with (S)-KET. Outcomes were seizure control and death. RESULTS: A total of 42 patients (14 women) with RSE and SRSE were treated with (S)-KET. The median duration of status epilepticus (SE) was 10 days [first quartile (Q1) 5.0, Q3 21.0]; the median latency from SE onset to the first administration of (S)-KET was 3 days (Q1 2.0, Q3 6.8). Prior to (S)-KET administration, patients had received a median of two (Q1 2.0, Q3 3.0) anesthetics and three (Q1 2.0, Q3 4.0) antiepileptic drugs. Forty percent of patients (17/42) received propofol: 65 % prior to (S)-KET; 35 % at the same time with (S)-KET. Seven patients received a median bolus of (S)-KET of 200 mg (Q1 200, Q3 250) followed by a continuous infusion, while 35 started with a continuous infusion (maximum rate median 2.55 mg/kg/h; Q1 2.09, Q3 3.22). In 64 % of patients (27/42), (S)-KET was the last drug before SE cessation; in five patients, it was given with propofol at the same time. Median duration of administration was 4 days (Q1 2.0, Q3 6.8). Overall (S)-KET treatment was well tolerated, adverse effects were not observed, and overall mortality was 45.2 %. CONCLUSIONS: Treatment of SRSE in adult patients with (S)-KET led to resolution of status in 64 %. No adverse events were found, indicating a favorable safety profile.

Connectivity biomarkers can differentiate patients with different levels of consciousness.

OBJECTIVE: In the present study, we searched for resting-EEG biomarkers that distinguish different levels of consciousness on a single subject level with an accuracy that is significantly above chance. METHODS: We assessed 44 biomarkers extracted from the resting EEG with respect to their discriminative value between groups of minimally conscious (MCS, N=22) patients, vegetative state patients (VS, N=27), and - for a proof of concept - healthy participants (N=23). We applied classification with support vector machines. RESULTS: Partial coherence, directed transfer function, and generalized partial directed coherence yielded accuracies that were significantly above chance for the group distinction of MCS vs. VS (.88, .80, and .78, respectively), as well as healthy participants vs. MCS (.96, .87, and .93, respectively) and VS (.98, .84, and .96, respectively) patients. CONCLUSIONS: The concept of connectivity is crucial for determining the level of consciousness, supporting the view that assessing brain networks in the resting state is the golden way to examine brain functions such as consciousness. SIGNIFICANCE: The present results directly show that it is possible to distinguish patients with different levels of consciousness on the basis of resting-state EEG.

Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness.

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53-.94) and power spectra (mean = .69; range = .40-.85). The coherence patterns in healthy participants did not match the expectation of central modulated [Formula: see text]-rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p[Formula: see text]0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

EEG-response consistency across subjects in an active oddball task.

The active oddball paradigm is a candidate task for voluntary brain activation. Previous research has focused on group effects, and has largely overlooked the potential problem of interindividual differences. Interindividual variance causes problems with the interpretation of group-level results. In this study we want to demonstrate the degree of consistency in the active oddball task across subjects, in order to answer the question of whether this task is able to reliably detect conscious target processing in unresponsive patients. We asked 18 subjects to count rare targets and to ignore frequent standards and rare distractors in an auditory active oddball task. Event-related-potentials (ERPs) and time-frequency data were analyzed with permutation-t-tests on a single subject level. We plotted the group-average ERPs and time-frequency data, and evaluated the numbers of subjects showing significant differences between targets and distractors in certain time-ranges. The distinction between targets/distractors and standards was found to be significant in the time-range of the P300 in all participants. In contrast, significant differences between targets and distractors in the time-range of the P3a/b were found in 8 subjects, only. By including effects in the N1 and in a late negative component there remained 2 subjects who did not show a distinction between targets and distractors in the ERP. While time-frequency data showed prominent effects for target/distractor vs. standard, significant differences between targets and distractors were found in 2 subjects, only. The results suggest that time-frequency- and ERP-analysis of the active oddball task may not be sensitive enough to detect voluntary brain activation in unresponsive patients. In addition, we found that time-frequency analysis was even less informative than ERPs about the subject's task performance. Despite suggesting the use of more sensitive paradigms and/or analysis techniques, the present results give further evidence that electroencephalographic research should rely more strongly on single-subject analysis because interpretations of group-effects may be misleading.

Real movement vs. motor imagery in healthy subjects.

Motor imagery tasks are well established procedures in brain computer interfaces, but are also used in the assessment of patients with disorders of consciousness. For testing awareness in unresponsive patients it is necessary to know the natural variance of brain responses to motor imagery in healthy subjects. We examined 22 healthy subjects using EEG in three conditions: movement of both hands, imagery of the same movement, and an instruction to hold both hands still. Single-subject non-parametric statistics were applied to the fast-Fourier transformed data. Most effects were found in the α- and ß-frequency ranges over central electrodes, that is, in the µ-rhythm. We found significant power changes in 18 subjects during movement and in 11 subjects during motor imagery. In 8 subjects these changes were consistent over both conditions. The significant power changes during movement were a decrease of µ-rhythm. There were 2 subjects with an increase and 9 subjects with a decrease of µ-rhythm during imagery. α and ß are the most responsive frequency ranges, but there is a minor number of subjects who show a synchronization instead of the more common desynchronization during motor imagery. A (de)synchronization of µ-rhythm can be considered to be a normal response.