Detection of Language Lateralization Using Spectral Analysis of EEG.

PURPOSE: Language lateralization relies on expensive equipment and can be difficult to tolerate. We assessed if lateralized brain responses to a language task can be detected with spectral analysis of electroencephalography (EEG). METHODS: Twenty right-handed, neurotypical adults (28 ± 10 years; five males) performed a verb generation task and two control tasks (word listening and repetition). We measured changes in EEG activity elicited by tasks (the event-related spectral perturbation [ERSP]) in the theta, alpha, beta, and gamma frequency bands in two language (superior temporal and inferior frontal [ST and IF]) and one control (occipital [Occ]) region bilaterally. We tested whether language tasks elicited (1) changes in spectral power from baseline (significant ERSP) at any region or (2) asymmetric ERSPs between matched left and right regions. RESULTS: Left IF beta power (-0.37±0.53, t = -3.12, P = 0.006) and gamma power in all regions decreased during verb generation. Asymmetric ERSPs (right > left) occurred between the (1) IF regions in the beta band (right vs. left difference of 0.23±0.37, t(19) = -2.80, P = 0.0114) and (2) ST regions in the alpha band (right vs. left difference of 0.48±0.63, t(19) = -3.36, P = 0.003). No changes from baseline or hemispheric asymmetries were noted in language regions during control tasks. On the individual level, 16 (80%) participants showed decreased left IF beta power from baseline, and 16 showed ST alpha asymmetry. Eighteen participants (90%) showed one of these two findings. CONCLUSIONS: Spectral EEG analysis detects lateralized responses during language tasks in frontal and temporal regions. Spectral EEG analysis could be developed into a readily available language lateralization modality.

Multi-omic profiling reveals early immunological indicators for identifying COVID-19 Progressors.

We sought to better understand the immune response during the immediate post-diagnosis phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. In lymphocytes, the CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. These early stage observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. BACKGROUND: Much of the literature on immune response post-SARS-CoV-2 infection has been in the acute and post-acute phases of infection. TRANSLATIONAL SIGNIFICANCE: We found differences at early time points of infection in approximately 160 participants. We compared multi-omic signatures in immune cells between individuals progressing to needing more significant medical intervention and non-progressors. We observed widespread evidence of a state of increased inflammation associated with progression, supported by a range of epigenomic, transcriptomic, and proteomic signatures. The signatures we identified support other findings at later time points and serve as the basis for prognostic biomarker development or to inform interventional strategies.

Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19.

Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning.

BACKGROUND: Individuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can measure the visual-motor function. However, few studies examined the potential changes in the color painting behavior in these individuals. The current study aims to discriminate schizophrenia patients from healthy controls (HCs) and predict PANSS scores of schizophrenia patients according to their paintings. METHODS: In the present study, we retrospectively analyzed the paintings colored by 281 chronic schizophrenia patients and 35 HCs. The images were scanned and processed using series of computational analyses. RESULTS: The results showed that schizophrenia patients tend to use less color and exhibit different strokes compared to HCs. Using a deep learning residual neural network (ResNet), we were able to discriminate patients from HCs with over 90% accuracy. Further, we developed a novel convolutional neural network to predict PANSS positive, negative, general psychopathology, and total scores. The Root Mean Square Error (RMSE) of the prediction was low, which indicates higher accuracy of prediction. CONCLUSION: In conclusion, the deep learning paradigm showed the large potential to discriminate schizophrenia patients from HCs based on color paintings. Besides, this color painting-based paradigm can effectively predict clinical symptom severity for chronic schizophrenia patients. The color paintings by schizophrenia patients show potential as a tool for clinical diagnosis and prognosis. These findings show potential as a tool for clinical diagnosis and prognosis among schizophrenia patients.

Disturbed temporal dynamics of episodic retrieval activity with preserved spatial activity pattern in amnestic mild cognitive impairment: A simultaneous EEG-fMRI study.

Episodic memory (EM) deficit is the core cognitive dysfunction of amnestic mild cognitive impairment (aMCI). However, the episodic retrieval pattern detected by functional MRI (fMRI) appears preserved in aMCI subjects. To address this discrepancy, simultaneous electroencephalography (EEG)-fMRI recording was employed to determine whether temporal dynamics of brain episodic retrieval activity were disturbed in patients with aMCI. Twenty-six aMCI and 29 healthy control (HC) subjects completed a word-list memory retrieval task during simultaneous EEG-fMRI. The retrieval success activation pattern was detected with fMRI analysis, and the familiarity- and recollection-related components of episodic retrieval activity were identified using event-related potential (ERP) analysis. The fMRI-constrained ERP analysis explored the temporal dynamics of brain activity in the retrieval success pattern, and the ERP-informed fMRI analysis detected fMRI correlates of the ERP components related to familiarity and recollection processes. The two groups exhibited similar retrieval success patterns in the bilateral posteromedial parietal cortex, the left inferior parietal lobule (IPL), and the left lateral prefrontal cortex (LPFC). The fMRI-constrained ERP analysis showed that the aMCI group did not exhibit old/new effects in the IPL and LPFC that were observed in the HC group. In addition, the aMCI group showed disturbed fMRI correlate of ERP recollection component that was associated with inferior EM performance. Therefore, in this study, we identified disturbed temporal dynamics in episodic retrieval activity with a preserved spatial activity pattern in aMCI. Taken together, the simultaneous EEG-fMRI technique demonstrated the potential to identify individuals with a high risk of cognitive deterioration.

Effect of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Impulse Inhibition in Abstinent Patients With Methamphetamine Addiction: A Randomized Clinical Trial.

Importance: Impulsivity during periods of abstinence is a critical symptom of patients who use methamphetamine (MA). Objective: To evaluate changes in impulse inhibition elicited by repetitive transcranial magnetic stimulation (rTMS) in patients with MA addiction. Design, Setting, and Participants: This randomized clinical trial was conducted in Da Lian Shan Addiction Rehabilitation Center, Nanjing, China, from December 1, 2018, to April 20, 2019. Effects of the intervention were examined at 3 time points: after a single session (day 1), 24 hours after 10 repeated sessions (day 11), and at 3 weeks of follow-up (day 31). Men with MA addiction and healthy male control participants were recruited for this study. Data analysis was performed from March 2019 to October 2019. Interventions: Patients who use MA were randomized to undergo sham rTMS (36 patients) and or 1-Hz rTMS (37 patients) to the left prefrontal cortex, receiving daily TMS treatments for 10 consecutive days. Main Outcomes and Measures: The primary outcome was impulse inhibition, which is primarily embodied by accuracy reduction (ie, accuracy cost) from standard to deviant trials in a 2-choice oddball task (80% standard and 20% deviant trials). Result: The study included 73 men with MA addiction (mean [SD] age, 38.49 [7.69] years) and 33 male healthy control participants without MA addiction (mean [SD] age, 35.15 [9.68] years). The mean (SD) duration of abstinence for the men with MA addiction was 9.27 (4.61) months. Compared with the control group, patients with MA addiction exhibited greater impulsivity (accuracy cost, 3.3% vs 6.2%). The single session of 1-Hz rTMS over the left prefrontal cortex significantly increased accuracy from 91.4% to 95.7% (F1,36 = 9.58; P < .001) and reaction time delay from 50 milliseconds to 77 milliseconds (F1,36 = 22.66; P < .001) in deviant trials. These effects were seen consistently after 10 sessions of 1-Hz rTMS treatment (day 11 vs day 1, t26 = 1.59; P = .12), and the behavioral improvement was maintained at least for 3 weeks after treatment (day 31 vs day 1, t26 = 0.26; P = .80). These improvement effects of impulse inhibition were coupled with a reduction in addictive symptoms as measured by cue-induced craving. The pretest accuracy cost was positively correlated with the change in impulse inhibition (r = 0.615; P < .001) and change in craving (r = 0.334; P = .01), suggesting that these 2 behaviors may be modified simultaneously. Conclusions and Relevance: These findings suggest that repeated rTMS sessions have sustained effects on impulse inhibition in patients with MA addiction and provide novel data on impulsivity management strategies for addiction rehabilitation. Trial Registration: ChiCTR-ROC-16008541.

An electroencephalographic signature predicts antidepressant response in major depression.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning.

Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.

Dissociating the Neural Correlates of Experiencing and Imagining Affective Touch.

This functional magnetic resonance imaging (fMRI) study examined experiencing and imagining gentle arm and palm touch to determine whether these processes activate overlapping or distinct brain regions. Although past research shows brain responses to experiencing and viewing touch, this study investigates neural processing of touch absent of visual stimulation. C-tactile (CT) nerves, present in hairy skin, respond specifically to caress-like touch. CT-targeted touch activates "social brain" regions including insula, right posterior superior temporal sulcus, amygdala, temporal poles, and orbitofrontal cortex ( McGlone et al. 2012). We addressed whether activations reflect sensory input-driven mechanisms, cognitive-based mechanisms, or both. We identified a functional dissociation between insula regions. Posterior insula responded during experienced touch. Anterior insula responded during both experienced and imagined touch. To isolate stimulus-independent mechanisms recruited during physical experience of CT-targeted touch, we identified regions active to experiencing and imagining such touch. These included amygdala and temporal pole. We posit that the dissociation of insula function suggests posterior and anterior insula involvement in distinct yet interacting processes: coding physical stimulation and affective interpretation of touch. Regions active during experiencing and imagining CT-targeted touch are associated with social processes indicating that imagining touch conjures affective aspects of experiencing such touch.

How to design PET experiments to study neurochemistry: application to alcoholism.

Positron Emission Tomography (PET) (and the related Single Photon Emission Computed Tomography) is a powerful imaging tool with a molecular specificity and sensitivity that are unique among imaging modalities. PET excels in the study of neurochemistry in three ways: 1) It can detect and quantify neuroreceptor molecules; 2) it can detect and quantify changes in neurotransmitters; and 3) it can detect and quantify exogenous drugs delivered to the brain. To carry out any of these applications, the user must harness the power of kinetic modeling. Further, the quality of the information gained is only as good as the soundness of the experimental design. This article reviews the concepts behind the three main uses of PET, the rationale behind kinetic modeling of PET data, and some of the key considerations when planning a PET experiment. Finally, some examples of PET imaging related to the study of alcoholism are discussed and critiqued.

Oxytocin enhances brain function in children with autism.

Following intranasal administration of oxytocin (OT), we measured, via functional MRI, changes in brain activity during judgments of socially (Eyes) and nonsocially (Vehicles) meaningful pictures in 17 children with high-functioning autism spectrum disorder (ASD). OT increased activity in the striatum, the middle frontal gyrus, the medial prefrontal cortex, the right orbitofrontal cortex, and the left superior temporal sulcus. In the striatum, nucleus accumbens, left posterior superior temporal sulcus, and left premotor cortex, OT increased activity during social judgments and decreased activity during nonsocial judgments. Changes in salivary OT concentrations from baseline to 30 min postadministration were positively associated with increased activity in the right amygdala and orbitofrontal cortex during social vs. nonsocial judgments. OT may thus selectively have an impact on salience and hedonic evaluations of socially meaningful stimuli in children with ASD, and thereby facilitate social attunement. These findings further the development of a neurophysiological systems-level understanding of mechanisms by which OT may enhance social functioning in children with ASD.