Measuring visual electrophysiological responses in individuals with low-functioning autism: a feasibility and pilot study

Background Although visual abnormalities are considered common in individuals with autism spectrum disorders, the associated electrophysiological markers have remained elusive. One impediment has been that methodological challenges often preclude testing individuals with low-functioning autism (LFA). Methods In this feasibility and pilot study, we tested a hybrid visual evoked potential paradigm tailored to individuals with LFA that combines passively presented visual stimuli to elicit scalp-recorded evoked responses with a behavioral paradigm to maintain visual attention. We conducted a pilot study to explore differences in visual evoked response patterns across three groups: individuals with LFA, with high-functioning autism (HFA), and with typical development. Results All participants with LFA met criteria for study feasibility by completing the recordings and producing measurable cortical evoked waveform responses. The LFA group had longer (delayed) cortical response latencies on average as compared with the HFA and typical development groups. We also observed group differences in visually induced alpha spectral power: the LFA group showed little to no prestimulus alpha activity in contrast to the HFA and typical development groups that showed increased prestimulus alpha activity. This observation was confirmed by the bootstrapped confidence intervals, suggesting that the absence of prestimulus alpha power may be a potential electrophysiological marker of LFA. Conclusion Our results confirm the utility of tailoring visual electrophysiology paradigms to individuals with LFA in order to facilitate inclusion of individuals across the autism spectrum in studies of visual processing.

Enabling #EEGManyLabs: Quality in Automatically Preprocessed EEG Data and Psychopathological Associations of Frontal Alpha Asymmetry

Neuroscience is facing a replication crisis. Little effort is invested in replication projects and low power in many studies indicates a potentially poor state of research. To assess replicability of EEG research, the #EEGManyLabs project aims to reproduce the most influential original EEG studies. A spin-off to the main project shall investigate the relationship between frontal alpha asymmetries and psychopathological symptoms, the predictive qualities of which have lately been considered controversial. To ensure that preprocessing of EEG data can be conducted automatically (via Automagic), we tested 47 healthy participants in an EEG resting state paradigm and collected psychopathological measures. We analyzed reliability and quality of manual and automated preprocessing and performed multiple regressions to investigate the association of frontal alpha asymmetries and depression, worry, trait anxiety and COVID-19 related worry. We hypothesized comparably good interrater reliability of preprocessing methods and higher data quality in automatically preprocessed data. We expected associations of leftward frontal alpha asymmetries and higher depression and anxiety scores and significant associations of rightward frontal alpha asymmetries and higher worrying and COVID-19- related worrying. Interrater reliability of preprocessing methods was mostly good, automatically preprocessed data achieved higher quality scores than manually preprocessed data. We uncovered an association of relative rightward lateralization of alpha power at one electrode pair and depressive symptoms. No further associations of interest emerged. We conclude that Automagic is an appropriate tool for large-scale preprocessing. Findings regarding associations of frontal alpha asymmetries and psychopathology likely stem from sample limitations and shrinking effect sizes.

Affective disposition and cognitive processing in dysphoria: a time-frequency EEG study

To date, affective disposition and cognitive processing of emotional stimuli in individuals with depressive symptoms have not been fully explored within the same framework. Time-frequency analysis of electroencephalographic activity allows to disentangle the brain's parallel processing of information. The present study employed a time-frequency approach to simultaneously examine affective disposition and cognitive processing during the viewing of emotional stimuli in dysphoria. Time-frequency event-related changes were examined during the viewing of pleasant, neutral and unpleasant pictures in 24 individuals with dysphoria and 24 controls. Affective disposition was indexed by delta and alpha power, while theta power was employed as a correlate of cognitive elaboration of the stimuli. Cluster-based statistics revealed a centro-parietal reduction in delta power for pleasant stimuli in individuals with dysphoria than controls. Also, dysphoria was characterized by an early fronto-central increase in theta power for unpleasant stimuli relative to neutral and pleasant. Instead, controls were characterized by a late fronto-central and occipital reduction in theta power for unpleasant stimuli relative to neutral and pleasant. The present study granted novel insights on the interrelated facets of affective elaboration in dysphoria, mainly characterized by an hypoactivation of the approach-related motivational system and a sustained facilitated cognitive processing of unpleasant stimuli.

When to Choose Paroxetine Treatment in Skin-Picking Disorder: A Case Report

Skin picking disorder (SPD) characterized by repetitive compulsive scratching in the absence of a primary skin disease is strongly associated with psychiatric comorbidities, including obsessive-compulsive disorder (OCD) and depression (MDD). Selective serotonin reuptake inhibitors (SSRIs) have been used in the treatment of SPD with variable success. Nevertheless, the optimum treatment choice for SPD is an issue for clinicians. This case report presents a 32-year-old female SPD patient treated with four-week paroxetine monotherapy. Based upon the clinical interview and standardized questionnaires, the patient was diagnosed with OCD with depressive features and Skin Picking Disorder. In addition to symptom severity scales, quantitative electroencephalography (qEEG) was also applied. Paroxetine treatment was started (titrated from 5 to 40 mg/day) and doubled each week. After four-week paroxetine monotherapy, OCD symptoms were diminished, and skin lesions were completely regressed leaving solely post inflammatory hyperpigmentation. Post-treatment qEEG assessment also showed a normalization of frontal alpha power and amplitude asymmetry. It can be concluded that if OCD includes SPD with abnormal EEG patterns; then the treatment success using paroxetine will be very high.

Deciding on Optical Illusions: Reduced Alpha Power in Body Dysmorphic Disorder

Background: Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by excessive preoccupation with imagined defects in appearance. Optical illusions induce illusory effects that distort the presented stimulus thus leading to ambiguous percepts. Using electroencephalography (EEG), we investigated whether BDD is related to differentiated perception during illusory percepts. Methods: 18 BDD patients and 18 controls were presented with 39 optical illusions together with a statement testing whether or not they perceived the illusion. After a delay period, they were prompted to answer whether the statement is right/wrong and their degree of confidence for their answer. We investigated differences of BDD on task performance and self-reported confidence and analysed the brain oscillations during decision-making using nonparametric cluster statistics. Results: Behaviorally, the BDD group exhibited reduced confidence when responding incorrectly, potentially attributed to higher levels of doubt. Electrophysiologically, the BDD group showed significantly reduced alpha power at mid-central scalp areas, suggesting impaired allocation of attention. Interestingly, the lower the alpha power of the identified cluster, the higher the BDD severity, as assessed by BDD psychometrics. Conclusions: Results evidenced that alpha power during illusory processing might serve as a quantitative EEG biomarker of BDD, potentially associated with reduced inhibition of task-irrelevant areas.

Cortical and STN spectral changes during limb movements in PD patients with and without dystonia

Background: Dystonia is a prevalent yet under-studied motor feature of Parkinson disease (PD). Although considerable efforts have focused on brain oscillations related to the cardinal symptoms of PD, whether dystonia is associated with specific electrophysiological features is unclear. Objectives: To investigate subcortical and cortical field potentials at rest and during contralateral hand and foot movements in PD patients with versus without dystonia. Methods: We examined the prevalence and somatotopy of dystonia in PD patients undergoing deep brain stimulation (DBS) surgery. We recorded intracranial electrophysiology from sensorimotor cortex and directional DBS electrodes in subthalamic nucleus (STN), during both rest and voluntary contralateral limb movements. We used wavelet transforms and linear mixed models to characterize spectral content in patients with and without dystonia (n=25). Results: Dystonia was highly prevalent at enrollment (61%) and most common in the foot (78%). PD patients with dystonia display greater subthalamic theta and alpha power during movement (p < 0.05) but not at rest. Regardless of dystonia status, cortical recordings display prominent beta desynchronization (13-30 Hz) during movement, whereas STN signals show increases in spectral power at lower frequencies (4-20 Hz), with peaks at 6.0 +/- 3.3 and 4.2 +/- 2.9 Hz during hand and foot movements, respectively (p < 0.03). Conclusions: Whereas cortex was characterized by beta desynchronization during hand and foot movements similarly, STN showed limb-specific low frequency activity which was increased in PD patients with dystonia. These findings may help elucidate why PD-related dystonia is most common in the foot and help guide future closed-loop DBS devices.

Neural Dynamics of Context-Sensitive Adjustments in Cognitive Flexibility

To adaptively interact with the uncertainties of daily life, we must match our level of cognitive flexibility to contextual demands—being more flexible when frequent shifting between different tasks is required and more stable when the current task requires a strong focus of attention. Such cognitive flexibility adjustments in response to changing contextual demands have been observed in cued task-switching paradigms, where the performance cost incurred by switching versus repeating tasks (switch cost) scales inversely with the proportion of switches (PS) within a block of trials. However, the neural underpinnings of these adjustments in cognitive flexibility are not well understood. Here, we recorded 64-channel EEG measures of electrical brain activity as participants switched between letter and digit categorization tasks in varying PS contexts, from which we extracted ERPs elicited by the task cue and alpha power differences during the cue-to-target interval and the resting precue period. The temporal resolution of the EEG allowed us to test whether contextual adjustments in cognitive flexibility are mediated by tonic changes in processing mode or by changes in phasic, task cue-triggered processes. We observed reliable modulation of behavioral switch cost by PS context that was mirrored in both cue-evoked ERP and time–frequency effects but not by blockwide precue EEG changes. These results indicate that different levels of cognitive flexibility are instantiated after the presentation of task cues, rather than by being maintained as a tonic state throughout low- or high-switch contexts.

Association Between Frontal Alpha Asymmetry With Cognitive Symptoms, Depression Severity, and Insomnia

Objective: Previous studies have compared depressive episodes between bipolar disorder (BD) and major depressive disorder (MDD) using quantitative electroencephalogram (QEEG); however, there are no distinct discriminating feature between them. Here, we used QEEG to directly compare the alpha asymmetry and absolute power of each band between patients with BD and MDD.Methods: Fifty in-patients with major depressive episodes between 2019 and 2021 were retrospectively enrolled. Self-reported questionnaires including the Beck Depression Inventory (BDI), Korean version of the Childhood Trauma Questionnaire, and Adult Attention-Deficit/Hyperactivity Disorder Self Report Scale (ASRS) were used to evaluate the symptoms. The absolute power of QEEG delta, theta, alpha, beta, high beta waves, and the Z-scores of frontal alpha asymmetry were collected. A t-test and Pearson’s correlation test were conducted using these data and based on these results, an analysis of covariance was conducted.Results: There were no significant differences between MDD and BD in QEEG power or alpha asymmetry. Patients with severe depression (BDI ≥29) had higher alpha power at FP1 (p=0.037), FP2 (p=0.028), F3 (p=0.047), F4 (p=0.016), and higher right frontal alpha asymmetry at F3–F4 (p=0.039). Adult patients with features consistent with ADHD (ASRS ≥4) had higher right frontal alpha asymmetry at F3–F4 (p=0.046). Patients with insomnia had higher left frontal alpha asymmetry at F3–F4 (p=0.003).Conclusion: QEEG limited the differential diagnosis of MDD and BD. However, frontal alpha asymmetry did exist in depression and affected cognitive impairment, insomnia, and depression severity in particular. Future studies with improved methodologies are needed for a better comparison.

Automatic Clustering of Students by Level of Situational Interest Based on Their EEG Features

The usage of physiological measures in detecting student’s interest is often said to improve the weakness of psychological measures by decreasing the susceptibility of subjective bias. The existing methods, especially EEG-based, use classification, which needs a predefined class and complex computational to analyze. However, the predefined classes are mostly based on subjective measurement (e.g., questionnaires). This work proposed a new scheme to automatically cluster the students by the level of situational interest (SI) during learning-based lessons on their electroencephalography (EEG) features. The formed clusters are then used as ground truth for classification purposes. A simultaneous recording of EEG was performed on 30 students while attending a lecture in a real classroom. The frontal mean delta and alpha power as well as the frontal alpha asymmetry metric served as the input for k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithms. Using the collected data, 29 models were trained within nine domain classifiers, then the classifiers with the highest performance were selected. We validated all the models through 10-fold cross-validation. The high SI group was clustered to students having lower frontal mean delta and alpha power together with negative Frontal Alpha Asymmetry (FAA). It was found that k-means performed better by giving the maximum performance assessment parameters of 100% in clustering the students into three groups: high SI, medium SI and low SI. The findings show that the DBSCAN had reduced the performance to cluster dataset without the outlier. The findings of this study give a promising option to cluster the students by their SI level, as well as address the drawbacks of the existing methods, which use subjective measures.