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Background. Electroencephalography is the main technique for assessing the functional state of the brain. Indications for EEG are diagnosis of paroxysmal states, prediction of the outcome of a pathological state, evaluation of bioelectrical activity if brain death is suspected. Up to 90 % of the native EEG in calm wakefulness in healthy individuals is occupied by “alpha activity”. In children in active wakefulness, the EEG pattern depends to a great extent on their age.Objective. The aim of the work was to assess EEG parameters in children aged 3–4 years in eyes-open resting state. Design and methods. 31 healthy participants aged 3–4 years were enrolled. EEG was registered for 30 minutes in a state of passive wakefulness in the supine position with open eyes. Average values of the power of the spectra for the alpha-rhythm, delta-rhythm and theta-rhythm in the frontal and temporal leads, as well as the ratio of the average power of alpha/theta and alpha/delta rhythms in the frontal and temporal leads were calculated.Results. Average power of the alpha-rhythm was significantly higher over the right frontal lobe than over the right frontal-temporal area, as well as average amplitude of it was significantly higher in F3-A1 than F7-A1, F4-A2 than F8-A2, which is associated with the articulatory praxis. Average alpha-rhythm power was significantly higher in T5-A1 than T3-A1 and T6-A2 than T4-A2, which corresponds to the recognition and naming of objects optically. Significant differences according to the total average power of the alpha- and theta-rhythms above the frontal and frontal-temporal regions reflect the relationship between the frontal cortex temporal lobes and the premotor zones, i.e. arcuate bundle, responsible for the “speech system”.Conclusion. The identified patterns can reflect the characteristics of the state of active wakefulness in a 3–4-year-old child and can be used for comparison in the future (both in the course of behavioral experiments and observation of patients with certain pathological processes).

Previous studies have shown that deep brain stimulation (DBS) can improve the level of consciousness of comatose patients with traumatic brain injuries (TBIs). However, the most suitable targets for DBS are unknown, and the mechanisms underlying recovery remain to be determined. The aim of the present study was to assess the effects of lateral hypothalamic area-DBS (LHA-DBS) in comatose rats with TBIs.A total of 55 Sprague-Dawley rats were randomly assigned to 5 groups: the control group, TBI group, stimulated (TBI+LHA-DBS) group, antagonist (TBI+SB334867+LHA-DBS) group, and antagonist control (TBI+saline+LHA-DBS) group. The rats in the control group had undergone a sham operation and anesthesia, without coma induction. Coma was induced using a free-fall drop method. The rats in the stimulated group received bilateral LHA stimulation (frequency, 200 Hz; voltage, 2-4 V; pulse width, 0.1 ms) for 1 hour, with 5-minute intervals between subsequent stimulations, which were applied alternately to the left and right sides of the lateral hypothalamus. The comatose rats in the antagonist group received an intracerebroventricular injection with an orexins receptor type 1 (OX1R) antagonist (SB334867) and then received LHA-DBS. A I-VI consciousness scale and electroencephalography were used to assess the level of consciousness in each group of rats after LHA-DBS. Western blotting and immunofluorescence were used to detect OX1R expression in the LHA and α1-adrenoceptor (α1-AR) subtype and gamma-aminobutyric acid β receptor (GABABR) expression in the prefrontal cortex.In the TBI, stimulated, antagonist, and antagonist control groups, 5, 10, 6, and 9 rats were awakened. The electroencephalographic readings indicated that the proportion of δ waves was lower in the stimulated group than in the TBI and antagonist groups (P0.05). Western blotting and immunofluorescence analysis showed that OX1R expression was greater in the stimulated group than in the TBI group (P0.05). The expression of α1-AR was also greater in the stimulated group than in the TBI and antagonist groups (P0.05). In contrast, the GABABR levels in the stimulated group were lower than those in the TBI and antagonist groups (P0.05). A statistically significant difference was found between the antagonist and antagonist control groups.Taken together, these results suggest that LHA-DBS promotes the recovery of consciousness in comatose rats with TBIs. Upregulation of α1-AR expression and downregulation of GABABR expression in the prefrontal cortex via the orexins and OX1R pathways might be involved in the wakefulness-promoting effects of LHA-DBS.

Aim. To estimate psychophysiological changes during workspace virtualization.Materials and Methods. We evaluated the psychophysiological profile of 10 healthy right-handed males aged 25 to 45 years before, during and after the working in a virtual reality (VR) headset. All participants had higher education, normal or corrected to normal vision, and were experienced computer users. Psychometric testing included a neurological examination, assessment of functional and feedback-related brain activity (reaction time, errors, and missed signals) and attention span, quantification of processed symbols in the 1st and 4th minutes of Bourdon test, analysis of short-term memory (10 words, 10 numbers and 10 meaningless syllables memorization) and spatial perception, and multi-channel electroencephalography recording in rest.Results. Deterioration of psychometric indicators after a cognitive load in a VR headset was documented only in the most difficult tasks: the number of errors increased by 93% in the brain performance test and by 65% in the attention distribution test. The analysis of electroencephalography data showed that the delta rhythm and theta1 rhythm activity decreased by 28 and 13%, respectively, after working in a VR headset as compared to baseline values, while alpha1 rhythm activity increased by 96%. Probably, the observed electroencephalography changes corresponded to the patterns of brain activation associated with cognitive load and the resulting fatigue.Conclusions. We developed a suitable approach for the psychometric testing before and after working in VR headset, which demonstrated general tolerance and acceptable subjective difficulties to VR load.

Author(s): Ostrowski, Lauren M; Spencer, Elizabeth R; Bird, Lynne M; Thibert, Ronald; Komorowski, Robert W; Kramer, Mark A; Chu, Catherine J | Abstract: ObjectiveAngelman syndrome (AS) is a severe neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A gene in neurons. Promising disease-modifying treatments to reinstate UBE3A expression are under development and an early measure of treatment response is critical to their deployment in clinical trials. Increased delta power in EEG recordings, reflecting abnormal neuronal synchrony, occurs in AS across species and correlates with genotype. Whether delta power provides a reliable biomarker for clinical symptoms remains unknown.MethodsWe analyzed combined EEG recordings and developmental assessments in a large cohort of individuals with AS (Nn=n82 subjects, 133 combined EEG and cognitive assessments, 1.08-28.16nyears; 32F) and evaluated delta power as a biomarker for cognitive function, as measured by the Bayley Cognitive Score. We examined the robustness of this biomarker to varying states of consciousness, recording techniques and analysis procedures.ResultsDelta power predicted the Bayley Scale cognitive score (Pnln10-5 , R2 n=n0.9374) after controlling for age (Pnln10-24 ), genotype:age (Pnln10-11 ), and repeat assessments (Pnln10-8 ), with the excellent fit on cross validation (R2 n=n0.95). There were no differences in model performance across states of consciousness or bipolar versus average montages (ΔAICnln2). Models using raw data excluding frontal channels outperformed other models (ΔAICngn4) and predicted performance in expressive (Pn=n0.0209) and receptive communication (Pnln10-3 ) and fine motor skills (Pnln10-4 ).InterpretationDelta power is a simple, direct measure of neuronal activity that reliably correlates with cognitive function in AS. This electrophysiological biomarker offers an objective, clinically relevant endpoint for treatment response in emerging clinical trials.

Speech is transient. To comprehend entire sentences, segments consisting of multiple words need to be memorized for at least a while. However, it has been noted previously that we struggle to memorize segments longer than approximately 2.7 s. We hypothesized that electrophysiological processing cycles within the delta band (

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation.

Cortical tracking of linguistic structures in speech, such as phrases (, info:eu-repo/semantics/published

Objective Aim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects. Methods Functional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta). Results Comparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced. Conclusions Brain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness. Significance The SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.

Background: Patients with amnesic mild cognitive impairment due to Alzheimer’s disease (ADMCI) typically show a “slowing” of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that “slowing.” Objective: Here we tested the hypothesis that the “slowing” of rsEEG rhythms is related to EEA in ADMCI patients. Methods: Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals. Results: EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (

Delta-frequency network activity is commonly associated with sleep or behavioral disengagement accompanied by a dearth of cortical spiking, but delta in awake behaving animals is not well understood. We show that hippocampal (HC) synchronization in the delta frequency band (1-4 Hz) is related to animals’ locomotor behavior using a detailed analysis of simultaneous head- and body-tracking data. In contrast to running-speed modulation of the theta rhythm (6-10 Hz, a critical mechanism in navigation models), we observed that strong delta synchronization occurred when animals were stationary or moving slowly and while theta and fast gamma (55-120 Hz) were weak. We next combined time-frequency decomposition of the local field potential with hierarchical clustering algorithms to categorize momentary estimations of the power spectral density (PSD) into putative modes of HC activity. Delta and theta power measures from these modes were notably orthogonal, and theta and delta coherences between HC recording sites were monotonically related to theta-delta ratios across modes. Next, we focused on bouts of precisely-defined running and stationary behavior. Extraction of delta and theta power density estimates for each instance of these bout types confirmed the orthogonality between frequency bands seen across modes. We found that delta-band and theta-band coherence within HC, and in a small sample, between HC and medial prefrontal cortex (mPFC), mirrored delta and theta components of the PSD. Delta-band synchronization often developed rapidly when animals paused briefly between runs, as well as appearing throughout longer stationary bouts. Taken together, our findings suggest that delta-dominated network modes (and corresponding mPFC-HC couplings) represent functionally-distinct circuit dynamics that are temporally and behaviorally interspersed amongst theta-dominated modes during navigation. As such these modes of mPFC-HC circuit dynamics could play a fundamental role in coordinating encoding and retrieval mechanisms or decision-making processes at a timescale that segments event sequences within behavioral episodes.

Auditory hallucinations (AHs) are a common symptom of schizophrenia and contribute significantly to disease burden. Research on schizophrenia and AHs is limited and fails to adequately address the effect of AHs on resting EEG in patients with schizophrenia. This study assessed changes in frequency bands (delta, theta, alpha, beta) of resting EEG taken from hallucinating patients (n = 12), nonhallucinating patients (n = 11), and healthy controls (n = 12). Delta and theta activity were unaffected by AHs but differed between patients with schizophrenia and healthy controls. Alpha activity was affected by AHs: nonhallucinators had more alpha activity than hallucinators and healthy controls. Additionally, beta activity was inversely related to trait measures of AHs. These findings contribute to the literature of resting eyes closed EEG recordings of schizophrenia and AHs, and indicate the role of delta, theta, alpha, and beta as markers for schizophrenia and auditory hallucinations.

Introduction Lateralized rhythmic delta activity (LRDA) is a rare pattern on the ictal-interictal continuum (IIC) encountered in critically ill patients. Its association with acute seizures is yet to be fully explored. Insular involvement is a common finding in patients with infectious and autoimmune encephalitis. The association between acute insular lesions and the ictal-interictal continuum, particularly LRDA, has not been explored before. Methods A case series of 4 patients with either herpetic or autoimmune encephalitis and prominent insular cortex involvement who had LRDA when monitored on continuous EEG is being presented. Results Two patients had herpetic encephalitis and 2 patients had autoimmune encephalitis. All patients had either clinical or electrographic seizures with 1 patient progressing into new-onset refractory status epilepticus. Conclusion LRDA can be seen in patients with insular cortex acute inflammation. In this group of patients, LRDA may be associated with a higher risk of acute seizures. The presence of this otherwise not clearly epileptiform pattern should raise the clinical suspicion for the development of acute seizures. Patients with LRDA and ipsilateral insular lesions should be carefully monitored for the development of recurrent electrographic or electroclinical seizures and status epilepticus.

Objective Aside from the cognitive impairment, patients with dementia with Lewy bodies (DLB) have a high frequency of visual hallucinations and a number of other vision-related symptoms, whereas auditory hallucinations are less frequent. To better understand the differential dysfunction of the visual network in DLB, we compared auditory and visual event-related potentials and oscillations in patients with DLB. Methods Event-related potentials elicited by visual and auditory oddball tasks were recorded in 23 patients with DLB and 22 healthy controls and analyzed in time and time-frequency domain. Results DLB patients had decreased theta band activity related to both early sensory and later cognitive processing in the visual, but not in the auditory task. Patients had lower delta and higher alpha and beta bands power related to later cognitive processing in both auditory and visual tasks. Conclusions In DLB visual event-related oscillations are characterized by a decrease in theta and lack of inhibition in alpha bands. Significance Decreased theta and a lack of inhibition in alpha band power might be an oscillatory underpinning of some classical DLB symptoms such as fluctuations in attention and high-level visual disturbances and a potential marker of dysfunction of the visual system in DLB.

Decision making relies on the interplay between two distinct learning mechanisms, namely habitual model-free learning and goal-directed model-based learning. Recent literature suggests that this interplay is significantly shaped by the environmental structure as represented by an internal model. We employed a modified two-stage but one-decision Markov decision task to investigate how two internal models differing in the predictability of stage transitions influence the neural correlates of feedback processing. Our results demonstrate that fronto-central theta and the feedback-related negativity (FRN), two correlates of reward prediction errors in the medial frontal cortex, are independent of the internal representations of the environmental structure. In contrast, centro-parietal delta and the P3, two correlates possibly reflecting feedback evaluation in working memory, were highly susceptible to the underlying internal model. Model-based analyses of single-trial activity showed a comparable pattern, indicating that while the computation of unsigned reward prediction errors is represented by theta and the FRN irrespective of the internal models, the P3 adapts to the internal representation of an environment. Our findings further substantiate the assumption that the feedback-locked components under investigation reflect distinct mechanisms of feedback processing and that different internal models selectively influence these mechanisms.

Young adults born at extremely low birth weight (prenatal adversity;Our results suggest that being exposed to pre- and post-natal adversity may have different long-term consequences on brain development. We speculate that these differences might be associated with some of the different functional outcomes known to characterize each type of adverse experience.

The ictal-interictal continuum represents a diagnostic challenge even for expert neurrophysiologists, often requiring an additional multimodal diagnostic workup to understand its clinical significance. Lateralised rhythmic delta activity (LRDA) is an ictal-interictal continuum pattern that has only recently been investigated and recognised as potentially ictogenic or sometimes even ictal. We describe a patient who presented with acute-onset aphasia, initially suspected of having a stroke; advanced brain imaging with CT-perfusion showed features suggesting regional left temporo-parietal hyperperfusion and an EEG revealed LRDA with fluctuations and intermixed sharp waves in the same areas. Treatment with lacosamide caused both clinical and EEG improvement after a few hours, supporting the hypothesis that the EEG pattern represented an ictal/interictal phenomenon. In the literature, a correlation between metabolic/perfusion imaging and ictal-interictal continuum patterns is described regarding lateralised periodic discharges but less studied for LRDA. In this case, we adopted a multimodal approach, integrating advanced imaging, EEG, clinical features, and response to therapy, to consider the overall clinical presentation as focal NCSE.

The objective was to examine the discriminatory ability of electroencephalogram (EEG) delta power in neonates with hypoxic-ischemic encephalopathy (HIE) with well-defined outcomes. Prolonged continuous EEG recordings from term neonates with HIE during therapeutic hypothermia enrolled in a prospective observational study were examined. Adverse outcome was defined as death or severe brain injury by magnetic resonance imaging (MRI); favorable outcome was defined as normal or mild injury by MRI. Neonates were stratified by Sarnat grade of encephalopathy at admission. EEG was partitioned into 10-minute nonoverlapping artifact- and seizure-free epochs. Delta power was calculated and compared between the groups using receiver operating characteristic (ROC) analyses and Wilcoxon rank-sum tests. An area under the ROC curve >0.7 with P

EEGs are known to provide biomarkers for consciousness. Although EEG correlates of loss of consciousness (LOC) are often ascribed to changes in neural synchrony, mounting evidence suggests that some changes result from asynchronous neural activity. By combining EEG recordings of humans undergoing propofol administration with biophysical modelling, we present here a principled decomposition of EEG changes during LOC into synchronous and asynchronous sources. Our results reveal that IPSP decay rate and mean spike rate shape aperiodic EEG features, and that propofol’s effects on these parameters largely explain the changes in EEG spectra following propofol infusion. We further show that traditional spectral EEG analysis likely conflates these effects with changes in rhythmic activity, thereby masking the true dynamics of neural synchrony. We conclude that the well-documented propofol-induced alpha rhythm in fact appears before LOC, and that the moment of LOC is uniquely correlated with the sudden appearance of a delta rhythm.

Interactions among cortical areas of tinnitus brain remained unclear. Weaker alpha and stronger delta activities in tinnitus have been noted over auditory cortices. However, the interplay between a single substrate with whole brain within alpha/delta band remained unknown. Thirty-one patients with chronic tinnitus were recruited. Thirty-four healthy volunteers served as controls. Magnetoencephalographic measurements of spontaneous activities were performed. The strength of alpha/delta activities was analyzed. By dividing cortices into 38 regions of interest (ROIs), measurements of connectivity were performed using amplitude envelope correlation (AEC). Global connectivity was calculated by adding and averaging connectivity of single ROI with every other region. There were no significant differences in mean power of alpha and delta band between groups, despite the trend of stronger alpha and weaker delta band in controls. The global connectivity of alpha wave was significantly stronger in tinnitus for left frontal pole, and of delta wave for bilateral pars orbitalis, bilateral superior temporal, bilateral middle temporal, right pars triangularis, right transverse temporal, right inferior temporal, and right supra-marginal. The global connectivity of alpha/delta waves was enhanced for tinnitus in designated ROIs of frontal/temporal/parietal lobes. The underlying mechanism(s) might be associated with augmentation/modulation of tinnitus perception. Our results corroborated the evolving consensus about neural correlates inside frontal/temporal/parietal lobes as essential elements of hubs for central processing of tinnitus. Further study to explore the resolution of effective connectivity between those ROIs and respective substrates by using AEC will be necessary for the evaluation of pathogenetic scenario for tinnitus.

Neural oscillations are electrical activities of the brain measurable at different frequencies. This paper studies the interaction between the fast and slow processes in the brain. We analyze electrocorticogram (ECoG) signals from the simple Wistar rats with developed tools application: low-pass and band- pass zero-phase filters to separate slow and fast components and envelope extraction to determine gamma-amplitude bursts and attenuations. Then we compute the correlation between the gamma rhythm envelope and a low-frequency ECoG signal. The analysis shows that the low-frequency signal (identified as delta rhythm) modulates the gamma rhythm with an approximately half-second time delay.

Objective Conventional polysomnographic recordings reflect brain dynamics associated with sleep architecture. We hypothesized that noninvasive tools like transcranial alternating current stimulation (tACS) and acoustic stimulation (for generating event related potentials [ERPs]) would help to predict sleep stability and provide a window to actively assess brain activity during sleep. Materials and methods Twelve healthy male volunteers participated in the multiple whole-night polysomnography (PSG) recording protocol. Acoustic tones (100 msec duration) were presented throughout night to evaluate ERP during sleep. Furthermore, 30 sec tACS were presented during nonrapid eye movement (NREM) and rapid eye movement (REM) sleep on subsequent two nights without disturbing the subject's sleep. For ERP analysis, event-locked artifact-free epochs from each sleep stage were averaged separately. For tACS analysis, 30 sec prestimulus and poststimulus artifact-free EEG epochs were subjected to bootstrapping-based comparison of power spectral values. Results Acoustic stimulation generated sleep stage-dependent ERP components (N350, N550, and P900) in all participants. The tACS stimulation during NREM sleep (0.75 Hz) increased parietal delta power but decreased frontocentral theta and increased frontal gamma power when delivered during REM sleep (40Hz). These interventions provide details on sleep stability as larger N550-P900 ERP-complex correlated with lower NREM disruptions (Spearman's rho = -0.553; p = 0.049; n = 10) and tACS-related theta power perturbation with higher REM disruptions (Spearman's rho = 0.734; p = 0.030; n = 7). Conclusions Noninvasive brain stimulation approaches such as sleep ERP and sleep tACS are reliable tools to evaluate sleep stability during NREM and REM sleep, respectively, but more large-sample studies are warranted.

Patients with chronic pain often report being sensitive to pain at night before falling asleep, a time when the synchronization of cortical activity is initiated. However, how cortical activity relates to pain sensitivity is still unclear. Because sleep is characterized by enhanced cortical delta power, we hypothesized that enhanced cortical delta power may be an indicator of intensified pain. To test this hypothesis, we used pain thresholds tests, EEG/electromyogram recordings, c-Fos staining, and chemogenetic and pharmacological techniques in mice. We found that sleep deprivation or pharmacologic enhancement of EEG delta power by reserpine and scopolamine dramatically decreased mechanical pain thresholds, but not thermal withdrawal latency, in a partial sciatic nerve ligation model of neuropathic pain mice. On the contrary, suppression of EEG delta power using a wake-promoting agent modafinil significantly attenuated mechanical allodynia. Moreover, when EEG delta power was enhanced, c-Fos expression decreased in most regions of the cortex, except the anterior cingulate cortex (ACC), where c-Fos was increased in the somatostatin- and parvalbumin-positive GABAergic neurons. Chemogenetic activation of GABAergic neurons in ACC enhanced EEG delta power and lowered mechanical pain thresholds simultaneously in naive mice. However, chemogenetic inhibition of ACC GABAergic neurons could not block mechanical allodynia. These results provided compelling evidence that elevated EEG delta power is accompanied with aggravated neuropathic pain, whereas decreased delta power attenuated it, suggesting that enhanced delta power can be a specific marker of rising chronic neuropathic pain and that wake-promoting compounds could be used as analgesics in the clinic.

Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset™, Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients. We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10–20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision. Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865–0.999) and 0.909 (0.587–0.998), respectively, and for detecting PDs were 0.824 (0.566–0.926) and 0.970 (0.842–0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440–0.897) and 0.970 (0.842–0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5–142). HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.

The right temporoparietal junction (rTPJ) is a multisensory integration hub that is increasingly utilized as a target of stimulation studies exploring its rich functional network roles and potential clinical applications. While transcranial direct current stimulation (tDCS) is frequently employed in such studies, there is still relatively little known regarding its local and network neurophysiological effects, particularly at important nonmotor sites such as the rTPJ. The current study applied either anodal, cathodal, or sham high-definition tDCS to the rTPJ of 53 healthy participants and used offline EEG to assess the impacts of stimulation on resting state (eyes open and eyes closed) band power and coherence. Temporoparietal and central region delta power was increased after anodal stimulation (the latter trend only), whereas cathodal stimulation increased frontal region delta and theta power. Increased coherence between right and left temporoparietal regions was also observed after anodal stimulation. All significant effects occurred in the eyes open condition. These findings are discussed with reference to domain general and mechanistic theories of rTPJ function. Low-frequency oscillatory activity may exert long-range inhibitory network influences that enable switching between and integration of endogenous/exogenous processing streams.NEW & NOTEWORTHY Through the novel use of high-definition transcranial direct current stimulation (tDCS) and EEG, we provide evidence that both anodal and cathodal stimulation of the right temporoparietal junction selectively modulate slow-wave power and coherence in distributed network regions of known relevance to proposed temporoparietal junction functionality. These results also provide direct evidence of the ability of tDCS to modulate oscillatory activity at a long-range network level, which may have explanatory power in terms of both neurophysiological and behavioral effects.

Recent evidence suggests that relative to nonshy children, shy children exhibit a lower overall frontal EEG alpha/delta ratio (ADR) during middle childhood, possibly reflecting relatively less frontal brain maturation at this age. We examined this same ADR measure in relation to the stability of observed shyness and parent-reported child social anxiety measured across two laboratory visits separated by approximately 1 year during late childhood in 51 children (33% female, age range 10-16 years). We found that the overall frontal ADR score was significantly lower among children with high, stable observed shyness and parent-reported child social anxiety compared to children in the low, stable class. Findings provide convergent evidence suggesting that the stability of shyness in late childhood may be linked to relatively less overall frontal brain maturation at this age. We speculate on the adaptive function of delaying frontal brain maturation in the origins and maintenance of children's shyness.

Previous studies indicate that temporal predictability can enhance timing and intensity perception, but it is not known whether it also enhances pitch perception, despite pitch being a fundamental perceptual attribute of sound. Here we investigate this in the context of rhythmic regularity, a form of predictable temporal structure common in sound streams, including music and speech. It is known that neural oscillations in low (delta: 1–3 Hz) and high (beta: 15–25 Hz) frequency bands entrain to rhythms in phase and power, respectively, but it is not clear why both low and high frequency bands entrain to external rhythms, and whether they and their coupling serve different perceptual functions. Participants discriminated near-threshold pitch deviations (targets) embedded in either rhythmic (regular/isochronous) or arrhythmic (irregular/non-isochronous) tone sequences. Psychophysically, we found superior pitch discrimination performance for target tones in rhythmic compared to arrhythmic sequences. Electroencephalography recordings from auditory cortex showed that delta phase, beta power modulation, and delta-beta coupling were all modulated by rhythmic regularity. Importantly, trial-by-trial neural-behavioural correlational analyses showed that, prior to a target, the depth of U-shaped beta power modulation predicted pitch discrimination sensitivity whereas cross-frequency coupling strength predicted reaction time. These novel findings suggest that delta phase might reflect rhythmic temporal expectation, beta power temporal attention, and delta-beta coupling auditory-motor communication. Together, low and high frequency auditory neural oscillations reflect different perceptual functions that work in concert for tracking rhythmic regularity and proactively facilitate pitch perception.

Empirical evidence suggests that, in the auditory cortex (AC), the phase relationship between spikes and local-field potentials (LFPs) plays an important role in the processing of auditory stimuli. Nevertheless, unlike the case of other sensory systems, it remains largely unexplored in the auditory modality whether the properties of the cortical columnar microcircuit shape the dynamics of spike-LFP coherence in a layer-specific manner. In this study, we directly tackle this issue by addressing whether spike-LFP and LFP-stimulus phase synchronization are spatially distributed in the AC during sensory processing, by performing laminar recordings in the cortex of awake short-tailed bats (Carollia perspicillata) while animals listened to conspecific distress vocalizations. We show that, in the AC, spike-LFP and LFP-stimulus synchrony depend significantly on cortical depth, and that sensory stimulation alters the spatial and spectral patterns of spike-LFP phase-locking. We argue that such laminar distribution of coherence could have functional implications for the representation of naturalistic auditory stimuli at a cortical level.

Speech is central to communication among humans. Meaning is largely conveyed by the selection of linguistic units such as words, phrases and sentences. However, prosody, that is the variation of acoustic cues that tie linguistic segments together, adds another layer of meaning. There are various features underlying prosody, one of the most important being pitch and how it is modulated. Recent fMRI and ECoG studies have suggested that there are cortical regions for pitch which respond primarily to resolved harmonics and that high-gamma cortical activity encodes intonation as represented by relative pitch. Importantly, this latter result was shown to be independent of the cortical tracking of the acoustic energy of speech, a commonly used measure. Here, we investigate whether we can isolate low-frequency EEG indices of pitch processing of continuous narrative speech from those reflecting the tracking of other acoustic and phonetic features. Harmonic resolvability was found to contain unique predictive power in delta and theta phase, but it was highly correlated with the envelope and tracked even when stimuli were pitch-impoverished. As such, we are circumspect about whether its contribution is truly pitch-specific. Crucially however, we found a unique contribution of relative pitch to EEG delta-phase prediction, and this tracking was absent when subjects listened to pitch-impoverished stimuli. This finding suggests the possibility of a separate processing stream for prosody that might operate in parallel to acoustic-linguistic processing. Furthermore, it provides a novel neural index that could be useful for testing prosodic encoding in populations with speech processing deficits and for improving cognitively controlled hearing aids.

Studies that use measures of cerebro-acoustic coherence have shown that theta oscillations (3–10 Hz) entrain to syllable-size modulations in the energy envelope of speech. This entrainment creates sensory windows in processing acoustic cues. Recent reports submit that delta oscillations (

Borderline personality disorder (BPD) is a complex and debilitating psychiatric illness. Prior research in adults has shown that neurophysiological deficits in feedback processing and learning from rewards may be central to the development of BPD; however, little research has examined these markers in adolescents and young adults with BPD. The present study used event-related potentials and time-frequency decomposition analysis to probe neural responses to wins and losses in a guessing task among 68 females (13 to 23 years old) either with BPD (n = 35) or no history of mental disorders (healthy control [HC]; n = 33). Participants completed a guessing task wherein they won and lost money at equal frequencies while electroencephalogram (EEG) data were acquired. Adolescents and young adults with BPD showed a smaller differentiation between wins and losses in the reward positivity (RewP) relative to HCs. Using time-frequency decomposition, we isolated distinct frequency bands sensitive to wins (delta = < 3Hz) and losses (theta = 4 Hz to 7 Hz). Compared with BPD participants, HCs showed significantly larger delta power to wins, specifically. The groups did not differ in delta power to losses, nor theta power to wins or losses. Collectively, findings implicate altered reward processing in the pathophysiology of BPD and may inform early identification and targeted intervention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γl‐band) and frontal (δ‐ and γl‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

Key points Sleep spindle frequency positively, duration negatively correlates with brain temperature. Local heating of the thalamus produces similar effects in the heated area. Thalamic network model corroborates temperature dependence of sleep spindle frequency. Brain temperature shows spontaneous microfluctuations during both anesthesia and natural sleep. Larger fluctuations are associated with epochs of REM sleep. Smaller fluctuations correspond to the alteration of spindling and delta epochs of infra-slow oscillation. Abstract Every form of neural activity depends on temperature, yet its relationship to brain rhythms is poorly understood. In this work we examined how sleep spindles are influenced by changing brain temperatures and how brain temperature is influenced by sleep oscillations. We employed a novel thermoelectrode designed for measuring temperature while recording neural activity. We found that spindle frequency is positively correlated and duration negatively correlated with brain temperature. Local heating of the thalamus replicated the temperature dependence of spindle parameters in the heated area only, suggesting biophysical rather than global modulatory mechanisms, a finding also supported by a thalamic network model. Finally, we show that switches between oscillatory states also influence brain temperature on a shorter and smaller scale. Epochs of paradoxical sleep as well as the infra-slow oscillation were associated with brain temperature fluctuations below 0.2°C. Our results highlight that brain temperature is massively intertwined with sleep oscillations on various time scales.

Neurosurgeons are vulnerable to additional noise in their natural operating environment. Noise exposure is associated with reduced cognitive function, inability to concentrate, and nervousness. Mediation music provides an opportunity to create a calmer environment which may reduce stress during surgery. A pilot study was performed to find a suitable task, meditation music of surgeon’s choice, and operation noise and to reach a certain level of training. For the main experiment, two neurosurgeons with different microsurgical experience used real operation noise and meditation music with delta waves as mediating music. Each surgeon performed 10 training bypasses (five with noise and five with music) with 16 stitches in each bypass. The total time to complete 16 stitches, a number of unachieved movements (N.U.Ms), length of thread consumed, and distribution of the stitches were quantified from the recorded videos and compared in both groups. A N.U.Ms were significantly reduced from 109 ± 38 with operation room (OR) noise to 38 ± 13 (p

Frequency-dependent brightness enhancement, a perceptual illusion in which a flickering light can appear twice as bright as a constant light, has historically been reported to produce maximum effects at a flicker rate within the alpha (8-12 Hz) band (Bartley in J Exp Psychol 23(3):313-319, 1938). Our recent examinations of this phenomenon using brightness discrimination between two flickering stimuli, however, have instead revealed the brightest percepts from theta-band (4-7 Hz) flicker (Bertrand et al. in Sci Rep 8(1):6152, 2018). Two primary questions arise from these seemingly contradictory findings: first, could task differences between these studies have caused recruitment of discrete oscillatory processes? Second, could the reported theta-band flicker enhancement be the result of an aliased alpha rhythm, sequentially sampling two stimulus locations, resulting in an ~ 5 Hz half-alpha rhythm? Here, we investigated these questions with two experiments: one replicating Bartley's (1938) adjustment paradigm, and one containing both Bartley's adjustment task and Bertrand's (2018) discrimination task, but presenting stimuli only sequentially (rather than concurrently). Examination of a range of frequencies (2-12 Hz) revealed the greatest brightness enhancement arising from flicker in the delta- and theta-band across all conditions, regardless of the spatial or temporal configuration of the stimuli. We speculate that these slower rhythms play an integral role in complex visual operations (e.g., a discrimination decision) where the entrainment of the endogenous neural rhythm to matched exogenous rhythmic stimulation promotes more efficient processing of visual information and thus produces perceptual biases as seen in frequency-dependent brightness enhancement.

Background Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by either disruptions of the gene UBE3A or deletion of chromosome 15 at 15q11-q13, which encompasses UBE3A and several other genes, including GABRB3, GABRA5, GABRG3, encoding gamma-aminobutyric acid type A receptor subunits ( β 3, α 5, γ 3). Individuals with deletions are generally more impaired than those with other genotypes, but the underlying pathophysiology remains largely unknown. Here, we used electroencephalography (EEG) to test the hypothesis that genes other than UBE3A located on 15q11-q13 cause differences in pathophysiology between AS genotypes. Methods We compared spectral power of clinical EEG recordings from children (1–18 years of age) with a deletion genotype (n = 37) or a nondeletion genotype (n = 21) and typically developing children without Angelman syndrome (n = 48). Results We found elevated theta power (peak frequency: 5.3 Hz) and diminished beta power (peak frequency: 23 Hz) in the deletion genotype compared with the nondeletion genotype as well as excess broadband EEG power (1–32 Hz) peaking in the delta frequency range (peak frequency: 2.8 Hz), shared by both genotypes but stronger for the deletion genotype at younger ages. Conclusions Our results provide strong evidence for the contribution of non-UBE3A neuronal pathophysiology in deletion AS and suggest that hemizygosity of the GABRB3-GABRA5-GABRG3 gene cluster causes abnormal theta and beta EEG oscillations that may underlie the more severe clinical phenotype. Our work improves the understanding of AS pathophysiology and has direct implications for the development of AS treatments and biomarkers.

The increased consumption of tobacco products in recent years has been linked, among other factors, to the presence of added flavors. Although flavors are important in explaining consumption, their effects in the brain have until now been unexplored. In the present study, we investigated how electrophysiology can serve to dissociate the effects of nicotine and flavor. Participants attended 4 sessions (2-by-2 factorial design, with flavor and nicotine as within-subject factors), in each session an oddball task was performed before and after smokeless tobacco consumption. We explored the dissociation of neural responses to flavor and nicotine. While event-related potentials did not show modulation due to flavors, time-frequency showed a flavor-nicotine dissociation. Low-frequency activity (delta, theta and alpha) showed only effects of nicotine, and high-frequency activity (beta1, beta2 and gamma) showed effects only susceptible to flavor. Flavors in smokeless tobacco not only made the product more desirable but also triggered the allocation of cognitive resources. This long-lasting effect of flavor may enhance the addictive potential of the tobacco product. Further research is being developed to determine the precise role of flavors in contributing to addiction. This is the first study investigating the neural effects of flavor (specifically wintergreen) in smokeless tobacco products. By understanding the effects of flavors in the brain we can explain the precipitants of tobacco consumption behaviors, and the addictive potential of flavors. Regulators will be able to determine if and in which amount flavors should be allowed in tobacco products.

Delirium is common during sepsis, although under-recognized. We aimed to assess the value of continuous electroencephalography (cEEG) to aid in the diagnosis of delirium in septic patients. We prospectively evaluated 102 consecutive patients in a medical intensive care unit (ICU), who had sepsis or septic shock, without evidence of acute primary central nervous system disease. We initiated cEEG recording immediately after identification. The median cEEG time per patient was 44 h (interquartile range 21–99 h). A total of 6723 h of cEEG recordings were examined. The Confusion Assessment Method for the ICU (CAM-ICU) was administered six times daily to identify delirium. We analyzed the correlation between cEEG and delirium using 1252 two-minute EEG sequences recorded simultaneously with the CAM-ICU scorings. Of the 102 included patients, 66 (65%) had at least one delirium episode during their ICU stay, 30 (29%) remained delirium-free, and 6 (6%) were not assessable due to deep sedation or coma. The absence of delirium was independently associated with preserved high-frequency beta activity (> 13 Hz) (P

Background Correlated activity of slow-wave (e.g. delta) and fast-wave (e.g. beta) frontal brain oscillations is thought to be an electrophysiological correlate of individual differences in neuroendocrine activity and anxiety in adult samples. We know, however, relatively little about the physiological and functional correlates of delta-beta coupling in children. Method We examined whether longitudinal patterns of children's basal salivary cortisol and social anxiety across two visits separated by 1 year were associated with frontal brain delta-beta correlation in children (Mage = 7.59 years, SD = 1.70). At Time 1 (T1), resting baseline electroencephalogram (EEG) recordings were collected from the children and delta and beta power was measured, and at both T1 and Time 2 (T2), basal salivary cortisol was measured, and parents reported on children's symptoms of social anxiety. Results Using latent class growth curve analysis, we found that children's salivary cortisol across visits was characterized by a high, stable class (53%), and a low, unstable class (47%), and children's social anxiety was characterized by a high, stable class (50%) and a low, stable class (50%). Using Fisher's r-to-z transformation, we found that frontal EEG delta-beta correlation was significantly stronger among children with high, stable salivary cortisol levels (compared to the low, unstable class; z = 2.11, p = .02), and among children with high, stable social anxiety levels (compared to the low, stable class; z = 1.72, p = .04). Conclusions These findings demonstrate that longitudinal patterns of neuroendocrine stress activity and social anxiety may be associated with the correlation of EEG power in slow and fast frontal brain oscillations as early as childhood.

Although the EEG slow wave of sleep is typically considered to be a hallmark of nonrapid eye movement (NREM) sleep, recent work in mice has shown that slow waves can also occur in REM sleep. Here, we investigated the presence and cortical distribution of negative delta (1–4 Hz) waves in human REM sleep by analyzing high-density EEG sleep recordings obtained in 28 healthy subjects. We identified two clusters of delta waves with distinctive properties: (1) a frontal-central cluster characterized by ∼2.5–3.0 Hz, relatively large, notched delta waves (so-called “sawtooth waves”) that tended to occur in bursts, were associated with increased gamma activity and rapid eye movements (EMs), and upon source modeling displayed an occipital-temporal and a frontal-central component and (2) a medial-occipital cluster characterized by more isolated, slower (SIGNIFICANCE STATEMENTThe EEG slow wave is typically considered a hallmark of nonrapid eye movement (NREM) sleep, but recent work in mice has shown that it can also occur in REM sleep. By analyzing high-density EEG recordings collected in healthy adult individuals, we show that REM sleep is characterized by prominent delta waves also in humans. In particular, we identified two distinctive clusters of delta waves with different properties: a frontal-central cluster characterized by faster, activating “sawtooth waves” that share many characteristics with ponto-geniculo-occipital waves described in animals and a medial-occipital cluster containing slow waves that are more similar to NREM sleep slow waves. These findings indicate that REM sleep is a spatially and temporally heterogeneous state and may contribute to explaining its known functional and phenomenological properties.

Cognitive flexibility is critical for humans living in complex societies with ever-growing multitasking demands. Yet the low-frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill-defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether a proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.

Objective To evaluate EEG abnormalities, particularly development of temporal intermittent rhythmic delta activity (TIRDA) after laser interstitial thermal therapy (LITT) and assess the role of further surgery after LITT. Methods We retrospectively identified consecutive cases of LITT for the prevalence of post-operative TIRDA. We assessed baseline demographics, clinical variables including age of seizure onset, age at surgery, pre-operative and post-operative EEG changes. Results 40 patients underwent LITT for drug-resistant temporal lobe epilepsy (TLE), 29 met inclusion criteria. Median duration of follow-up was 15 months. Ten patients had post-LITT ipsilateral TIRDA, another two demonstrated post-operative TIRDA but they occurred contralateral to the side of ablation. None of the patients with TIRDA on their post-LITT EEG became seizure-free. Six out of 29 patients (21%) eventually required anterior temporal lobectomy (ATL), and of those 6 patients 4 (66%) had evidence of TIRDA on their post-LITT follow up EEG. The sensitivity and specificity of post-LITT TIRDA in predicting surgical failure was 57.14% and 100% respectively. Conclusions Post-LITT TIRDA may serve as a biomarker to predict unsuccessful seizure outcome following LITT and be an early indicator for ATL. Significance The presence of TIRDA following LITT should prompt early consideration for reoperation.

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

When unexpected events occur during goal-directed behavior, they automatically trigger an orienting-related cascade of psychological and neural processes through which they influence behavior and cognition. If the unexpected event was caused by an action error, additional error-specific, strategic-related processes have been proposed to follow the initial orienting period. Little is known about the neural interactions between action errors and unexpected perceptual events, two instantiations of unexpected events, in these two putative stages of post-error processing. Here, we aimed to address this by investigating the electrophysiological dynamics associated with action errors and unexpected perceptual events using scalp EEG with a focus on the frontal midline (FM) delta-to-theta oscillations (1-8 Hz) indicative of the performance-monitoring system. Specifically, we examined how the timing of unexpected sounds would influence behavior and neural oscillations after action errors, depending on the length of the intertrial interval (ITI). Our data showed that unexpected sounds aggravated post-error decreases in accuracy when they occurred (1) immediately after errors (i.e., post-error orienting period), regardless of ITI and (2) immediately after the post-error stimulus (i.e., post-error strategic period), at short ITIs. Meanwhile, action errors and unexpected sounds independently produced increased FM delta-to-theta power during the post-error orienting period, regardless of ITIs. However, when unexpected sounds occurred during the post-error strategic period, action errors produced lower FM delta-to-theta power than correct responses, at short ITIs. These differential effects of unexpected events on behavior and FM delta-to-theta dynamics support the notion of the two post-error periods during which different processes are implemented.

Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders., This study developed a novel resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging method, 3D‐MEGNET, using deep learning. The optimized 3D‐MEGNET method combining rs‐MEG data from all frequency bands distinguished individuals with combat‐related mild traumatic brain injury (cmTBI) from combat‐deployed healthy controls with high sensitivity, specificity, and diagnostic accuracy. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI.

ObjectiveTo determine the best of commonly used methods for computing the rate of decline in non-rapid eye movement (NREM) sleep EEG delta power overnight (Delta Decline) in terms of vulnerability to missing data and to evaluate whether this rate is slower in insomnia patients than healthy controls (HC).MethodsFifty-one insomnia patients and 53 HC underwent 6 nights of polysomnography. Four methods for estimating Delta Decline were compared (exponential and linear best-fit functions using NREM (1) episode mean, (2) peak, and (3) total delta power and (4) delta power for all available NREM epochs). The best method was applied to compare groups on linear and exponential rates of Delta Decline.ResultsBest-fit models using all available NREM epochs were significantly less vulnerable to deviation due to missing data than other methods. Insomnia patients displayed significantly slower linear and exponential Delta Decline than HC.ConclusionsComputing Delta Decline using all available NREM epochs was the best of the methods studied for minimizing the effects of missing data. Insomnia patients display slower Delta Decline, which is not explained by differences in total sleep time or wake after sleep onset.SignificanceThis study supports using all available NREM epochs in Delta Decline computation and suggests a slower rate in insomnia.

We present a general machine learning (ML) framework for diagnosing brain disorders using EEG recordings. It could detect any systematic deviations from the baseline (BL) EEG recordings. We test the approach using a rodent model of an acquired mesial temporal lobe epilepsy (mTLE) via electrical perforant pathway stimulation (PPS) [1] followed by usually a latent period of several weeks of epileptogenesis (EPG) progression without any seizures. Identifying ongoing EPG during this latent period prior to the first spontaneous seizure might enable early intervention. Previous supervised approaches have proven that deep neural networks can distinguish intracranial EEG signals between the BL period prior to PPS and the EPG period after PPS [2]. Here, we formulate the problem as an unsupervised anomaly detection task, which allows learning the statistics of the input data without the need for any labels as in supervised approaches. Specifically, we first train an autoencoder (AE) to learn to encode EEG data from the BL period. We then apply the trained AE on data from the EPG period. We expect that activity patterns systematically deviating from those observed during BL should be characterized by higher reconstruction errors (REs). Indeed, our results show that REs averaged over one hour gradually increase as a function of time after the PPS (Fig. 1). We also find that the distribution of REs from the BL and the EPG phases are significantly different (Fig. 1B). EEG segments with particularly high REs exhibit spikes and high frequency oscillations (Fig. 1B, right inset). Interestingly, there are an increased large number of segments with unusually low REs (Fig. 1B, left inset), which exhibits EEG slowing and delta rhythm (1.5–3 Hz). In conclusion, we showed that unsupervised ML with AEs allows the automatic detection of systematic drifts in brain activity, which may facilitate rapid EEG recordings for early diagnosis of brain disorders and open the door for early interventions. References Costard, L.S., Neubert, V., Veno, M.T., Su, J., Kjems, J., Connolly, N.M., Prehn, J.H., Schratt, G., Henshall, D.C., Rosenow, F. and Bauer, S., 2019. Electrical stimulation of the ventral hippocampal commissure delays experimental epilepsy and is associated with altered microRNA expression. Brain Stimulation, 12(6), pp. 1390-1401. Lu, D., Bauer, S., Neubert, V., Costard, L.S., Rosenow, F. and Triesch, J., 2020, September. Towards Early Diagnosis of Epilepsy from EEG Data. In Machine Learning for Healthcare Conference (pp. 80-96). PMLR. Fig. 1. A. The reconstruction error evolution throughout the whole recording period of rat #1B. The distribution of the reconstruction errors from the BL and the EPG phases from the recordings of rat #1. We also show some typical EPG segments whose reconstruction errors are below the 10th percentile and over the 90th percentile, highlighted with a solid and a dashed black box, respectively. Other stimulated rats show a similar trend.

An explosion of recent findings firmly demonstrated that brain activity and cognitive function in rodents and humans are modulated synchronously with nasal respiration. Rhythmic respiratory (RR) coupling of wide-spread forebrain activity was confirmed using advanced techniques, including current source density analysis, single unit firing, and phase modulation of local gamma activity, creating solid premise for investigating how higher networks use this mechanism in their communication. Here we show essential differences in the way prefrontal cortex (PFC) and hippocampus (HC) process the RR signal from the olfactory bulb (OB) allowing dynamic PFC-HC coupling utilizing this input. We used inter-regional coherences and their correlations in rats, breathing at low rate (~ 2 Hz) at rest, outside of the short sniffing bouts. We found strong and stable OB-PFC coherence, contrasting OB-HC coherence which was low but highly variable. PFC-HC coupling, however, primarily correlated with the latter, indicating that HC access to the PFC output is dynamically regulated by the responsiveness of HC to the common rhythmic drive. This pattern was present in both theta and non-theta states of waking, whereas PFC-HC communication appeared protected from RR synchronization in sleep states. The findings help to understand the mechanism of rhythmic modulation of non-olfactory cognitive processes by the on-going regular respiration, reported in rodents as well as humans. These mechanisms may also be important to understand how OB-pathology may lead to neurological consequences, similar to known olfactory disturbances in COVID-19.

Objectives: Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer’s Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD. Methods: We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores. Results: In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores. Conclusions: Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.