Scalp Electroencephalography Research Articles

Word-selective EEG/MEG responses in the English language obtained with Fast Periodic Visual Stimulation (FPVS)

Abstract Fast periodic visual stimulation (FPVS) allows the objective measurement of brain responses of human word discrimination (i.e., reproducible word-category-selective responses) with a high signal-to-noise ratio. This approach has been successfully employed over the last decade in a number of scalp electroencephalography (EEG) studies. Three important advances for research on word-selective brain responses were achieved in the present study: (1) we extend previous evidence of robust word-category-selective responses to the English language, (2) report results for combined EEG and MEG signals, and (3) source estimation results. English words were presented periodically (2 Hz) among different types of letter strings (10Hz; consonant strings, non-words, pseudowords) whilst recording simultaneous EEG and MEG in 25 participants who performed a simple non-linguistic color detection task. Data were analyzed in sensor and in source space. With only 4 minutes of stimulation we observed a robust word discrimination response in each condition, even when words were embedded in sequences of word-like pseudowords. This response was larger in nonwords and largest in consonant strings. We observed left-lateralized responses in all conditions in the majority of our participants. Cluster-based permutation tests revealed that these responses were left-lateralized in sensor as well as in source space, with peaks in left posterior regions. Our results demonstrate that the FPVS approach can elicit robust English word-discrimination responses in EEG and MEG within only a few minutes of recording time. Together with source estimation, this can provide novel insights into the neural basis of visual word recognition in healthy and clinical populations.

Self-regulation of slow cortical potential and seizure suppression by scalp electroencephalography: Early prediction of therapeutic efficacy

Self-regulation of slow cortical potential and seizure suppression by scalp electroencephalography: Early prediction of therapeutic efficacy

Beta-gamma phase-amplitude coupling of scalp electroencephalography during walking preparation in Parkinson's disease differs depending on the freezing of gait.

Despite using beta oscillations within the subthalamic nucleus as a biomarker of akinesia or rigidity in Parkinson's disease, a specific biomarker for freezing of gait (FOG) remains unclear. Recently, scalp phase-amplitude coupling (PAC) measured through scalp electroencephalography (EEG) has emerged as a promising tool for analyzing brain function. In this study, we examined whether PAC could be a biomarker for FOG. We enrolled 11 patients with Parkinson's disease and recorded scalp EEG in preparation for and during gait while simultaneously assessing motor function, including FOG. We investigated changes in cortical PAC during walking with and without FOG and examined its correlation with the postural instability and gait difficulty (PIGD) score. Patient characteristics were as follows: mean age 59.1 ± 6.9 years, disease duration 13.9 ± 4.1 years, and seven men. Four trials were excluded from the analysis owing to artifacts. In the trials without FOG (n = 18), beta-gamma PAC in the sensorimotor area decreased during gait preparation (p = 0.011; linear mixed-effects model), which was not the case in trials with FOG (n = 6) (p = 0.64; linear mixed-effects model). Using a support vector machine, machine learning of PAC during preparation for walking predicted the presence of FOG with an accuracy of 71.2%. Conversely, PAC increased during walking in trials with FOG (p = 0.0042; linear mixed-effects model), and PAC 20 s after the start of walking was positively correlated with the PIGD score (correlation coefficient = 0.406, p = 0.032; Pearson's rank correlation). Beta-gamma PAC in the sensorimotor area during preparation for walking differs depending on the emergence of FOG. As gait symptoms worsened, beta-gamma PAC in the sensorimotor area during walking gradually increased. Cortical PAC may be a biomarker for FOG in Parkinson's disease and may lead to the development of strategies to prevent falls in the future.

Neural Dynamics of Creative Movements During the Rehearsal and Performance of “LiveWire"

This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of “LiveWire” - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.

Surgical outcomes of drug-refractory infantile epileptic spasms syndrome and related prognostic factors: a retrospective study

BackgroundIn this study, we aimed to assess the efficacy of surgical treatment in children with drug-refractory infantile epileptic spasms syndrome (IESS) and examine the factors influencing the post-surgical outcomes.MethodsThe clinical data of 30 children (18 males and 12 females) with epileptic spasms (ES) who underwent surgery at the Epilepsy Center of Shenzhen Children’s Hospital between June 2018 and June 2020 were retrospectively analyzed. Post-surgical outcomes were evaluated using the Engel Epilepsy Surgery Outcome Scale. Scalp electroencephalography and developmental quotient were assessed preoperatively and postoperatively. Univariate analysis and exact logistic regression analyses were used to identify the factors affecting the postoperative efficacy.ResultsOf the 30 patients who underwent surgical resection, 22 (73.3%) achieved Engel’s class I–II outcomes. Additionally, motor and cognitive functions improved in 14 patients (46.7%). The development of 12 (40%) patients remained at the preoperative development level. The median number of antiseizure medications taken preoperatively was 5.27 (range 2–10), which decreased to 1.90 (range 0–4) at the last follow-up. Seizure duration, etiology, positive positron emission tomography-magnetic resonance imaging (PET-MRI), surgery type, and lesion location were significantly correlated with the postoperative efficacy (P < 0.05). Positive PET/MRI findings and lesion location predicted independently the postoperative outcomes. Permanent impairments of motor or language function were rare, with only two cases reporting hydrocephalus and one reporting hemiplegia.ConclusionsSurgery is an effective treatment option for children with IESS. Early referral and comprehensive preoperative evaluation are essential for identification of surgically treatable structural lesions. The primary surgically treatable cause is cortical malformation, followed by perinatal brain injury. Hemispheric disconnection is a preferred surgical approach. Positive PET/MRI findings and lesion location predicted the postoperative outcomes.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).

Neurological disorders affecting speech production adversely impact quality of&#xD;life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking&#xD;devices and P300 spellers are slow and unnatural for these patients. An&#xD;alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech&#xD;characteristics, offering a more natural communication mechanism. This research&#xD;explores the feasibility of decoding speech features using non-invasive EEG. Nine&#xD;neurologically intact participants were equipped with a 63-channel EEG system&#xD;with additional sensors to eliminate eye artifacts. Participants read aloud sentences&#xD;displayed on a screen selected for phonetic similarity to the English language. Deep&#xD;learning models, including Convolutional Neural Networks and Recurrent Neural&#xD;Networks with and without attention modules, were optimized with a focus on&#xD;minimizing trainable parameters and utilizing small input window sizes for real-time&#xD;application. These models were employed for discrete and continuous speech decoding&#xD;tasks, achieving statistically significant participant-independent decoding performance&#xD;for discrete classes and continuous characteristics of the produced audio signal. A&#xD;frequency sub-band analysis highlighted the significance of certain frequency bands&#xD;(delta, theta, and gamma) for decoding performance, and a perturbation analysis&#xD;was used to identify crucial channels. Assessed channel selection methods did not&#xD;significantly improve performance, suggesting a distributed representation of speech&#xD;information encoded in the EEG signals. Leave-One-Out training demonstrated&#xD;the feasibility of utilizing common speech neural correlates, reducing data collection&#xD;requirements from individual participants.

Multimodal and quantitative analysis of the epileptogenic zone network in the pre-surgical evaluation of drug-resistant focal epilepsy

Surgical resection for epilepsy often fails due to incomplete Epileptogenic Zone Network (EZN) localization from scalp electroencephalography (EEG), stereo-EEG (SEEG), and Magnetic Resonance Imaging (MRI). Subjective interpretation based on interictal, or ictal recordings limits conventional EZN localization. This study employs multimodal analysis using high-density-EEG (HDEEG), Magnetoencephalography (MEG), functional-MRI (fMRI), and SEEG to overcome these limitations in a patient with drug-resistant MRI-negative focal epilepsy. A 17-year-old with drug-resistant epilepsy underwent evaluation. HDEEG, MEG, fMRI, and SEEG were used, with a novel HDEEG-cap facilitating simultaneous EEG-MEG and EEG-fMRI recordings. Electrical and magnetic source imaging were performed, and fMRI data were analysed for homogenous regions. SEEG analysis involved spike detection, spike timing analysis, ictal fast activity quantification, and Granger-based connectivity analysis. Non-invasive sessions revealed consistent interictal source imaging results identifying the EZN in the right anterior cingulate cortex. EEG-fMRI highlighted broader activation in the right cingulate cortex. SEEG analysis localized spikes and fast activity in the right anterior and posterior cingulate gyri. Multi-modal analysis suggested the EZN in the right frontal lobe, primarily involving the anterior and mid-cingulate cortices. Multi-modal non-invasive analyses can optimise SEEG implantation and surgical decision-making. Invasive analyses corroborated non-invasive findings, emphasising the importance of individual-case quantitative analysis across modalities in complex epilepsy cases.

A New Epileptic Seizure Prediction Framework Based on Electroencephalography Signals

This research seeks to evaluate how effectively seizures can be predicted and managed in epilepsy using a specialized deep learning model based on Long Short-Term Memory (LSTM) neural networks. The model leverages non-invasive scalp electroencephalography (EEG) recordings for predicting seizures. To develop and assess the proposed LSTM neural network model, a comprehensive dataset was gathered. The model emphasizes achieving high sensitivity and reducing false alarms to improve its real-time applicability. The evaluation involved various metrics to measure accuracy, sensitivity, and rates of false positives and false negatives. The effectiveness of the proposed LSTM neural network model was outstanding, with accuracy rates ranging from 99.07% to 99.95%. Notably, the sensitivity score of 1 confirmed precise prediction for all seizure cases. The model demonstrated minimal false positive and false negative rates, highlighting its reliability in predicting seizures. This study emphasizes the promising potential of the proposed LSTM neural network model in providing advanced warning for seizures. The high accuracy and sensitivity rates suggest its usefulness in enabling timely preventive measures for patients, ultimately reducing the occurrence of seizures. This innovative approach holds significance in enhancing the overall management and quality of life for individuals dealing with epilepsy.

Electroencephalographic source imaging of spikes with concurrent high-frequency oscillations is concordant with the clinical ground truth.

Epilepsy raises critical challenges to accurately localize the epileptogenic zone (EZ) to guide presurgical planning. Previous research has suggested that interictal spikes overlapping with high-frequency oscillations, referred to here as pSpikes, serve as a reliable biomarker for EZ estimation, but there remains a question as to whether and to how pSpikes perform as compared to other types of epileptic spikes. This study aims to address this question by investigating the source imaging capabilities of pSpikes alongside other spike types. A total of 2819 interictal spikes from 76-channel scalp electroencephalography (EEG) were analyzed in a cohort of 24 drug-resistant focal epilepsy patients. All patients received surgical resection, and 16 were declared seizure-free based on at least 1 year of postoperative follow-up. A recently developed electrophysiological source imaging algorithm-fast spatiotemporal iteratively reweighted edge sparsity (FAST-IRES)-was used for source imaging of the detected interictal spikes. The performance of 217 pSpikes was compared with 772 nSpikes (spikes with irregular high-frequency activations), 1830 rSpikes (spikes with no high-frequency activity), and all 2819 aSpikes (all interictal spikes). The localization and extent estimation using pSpikes are concordant with the clinical ground truth; using pSpikes yields the best performance compared with nSpikes, rSpikes, and conventional spike imaging (aSpikes). For multiple spike type seizure-free patients, the mean localization error for pSpike imaging was 6.8 mm, compared with 15.0 mm for aSpikes. The sensitivity, precision, and specificity were .41, .67, and .93 for pSpikes compared with .32, .48, and .93 for aSpikes. These results demonstrate the merits of noninvasive EEG source localization, and that (1) pSpike is a superior biomarker, outperforming conventional spike imaging for the localization of epileptic sources, and especially those with multiple irritative zones; and (2) FAST-IRES provides accurate source estimation that is highly concordant with clinical ground truth, even insituations of single spike analysis with low signal-to-noise ratio.

Ictal and interictal epileptic networks of 34 patients with Hypothalamic Hamartoma on scalp electroencephalography.

To investigate ictal and interictal cortical involvement in epilepsy associated with hypothalamic hamartoma. We conducted a retrospective study of 34 patients with epilepsy and hypothalamic hamartoma, using data from long-term video-EEG-monitoring. We analyzed onset and propagation of ictal and interictal scalp EEG and visualized the resulting networks of cortical involvement. According to clinical and EEG criteria we grouped patients in: (1) focal disease, (2) focal advanced disease, (3) extended disease. We compared networks between these groups and different seizure types. Eight patients underwent several video-EEGs, and we analyzed all to investigate epilepsy progression. Epileptic activity mainly involved frontal and temporal cortex regions. Involvement of frontal regions was more common in advanced stages of the disease, and strong fronto-temporal connections were observed in the ictal networks of patients in intermediate stages (25.0% (left) and 35.7% (right) of seizures with EEG correlate). Occurrence and timing of EEG-correlate significantly depended on the seizure type (Chi-2-test, p<<0.001). In patients with several EEGs, seizure activity increased by +0.67 seizures/day/month (mean). There were significant differences between patients with normal and impaired cognitive function, with the latter showing pronounced ictal involvement of fronto-temporal cortex areas (p<0.001). Overall, in epilepsy due to hypothalamic hamartoma, cortical involvement focuses on frontal and temporal regions and varies systematically with the stage of the disease, different seizure types and presence of impaired cognitive function. We propose that these data may help improve our general understanding of epileptogenesis and potentially provide insights for the surgical therapy of hypothalamic hamartomas.

Insights on neural signal analysis with Higuchi fractal dimension

Abstract Neurophysiological signal analysis is crucial for understanding the complex dynamics of brain function and its deviations in various pathological conditions. Traditional linear methods, while insightful, often fail to capture the full spectrum of inherently non-linear brain dynamics. This review explores the efficacy and applicability of the Higuchi fractal dimension (HFD) in interpreting neurophysiological signals such as scalp electroencephalography (EEG) and stereotactic intracranial encephalography (sEEG). We focus on three case studies: i) distinguishing between Alzheimer’s disease (AD) and healthy controls; ii) classifying neurodynamics across diverse brain parcels looking for a signature of that cortical parcel; and iii) differentiating states of consciousness. Our study highlights the potential of non-linear analysis for deeper insights into brain dynamics and its potential for improving clinical diagnostics.

Neural Activity Differentiates Novel and Learned Event Boundaries.

People parse continuous experiences at natural breakpoints called event boundaries, which is important for understanding an environment's causal structure and for responding to uncertainty within it. However, it remains unclear how different forms of uncertainty affect the parsing of continuous experiences and how such uncertainty influences the brain's processing of ongoing events. We exposed human participants of both sexes (N = 34) to a continuous sequence of semantically meaningless images. We generated sequences from random walks through a graph that grouped images into temporal communities. After learning, we asked participants to segment another sequence at natural breakpoints (event boundaries). Participants segmented the sequence at learned transitions between communities, as well as at novel transitions, suggesting that people can segment temporally extended experiences into events based on learned structure as well as prediction error. Greater segmentation at novel boundaries was associated with enhanced parietal scalp electroencephalography (EEG) activity between 250 and 450 ms after the stimulus onset. Multivariate classification of EEG activity showed that novel and learned boundaries evoked distinct patterns of neural activity, particularly theta band power in posterior electrodes. Learning also led to distinct neural representations for stimuli within the temporal communities, while neural activity at learned boundary nodes showed predictive evidence for the adjacent community. The data show that people segment experiences at both learned and novel boundaries and suggest that learned event boundaries trigger retrieval of information about the upcoming community that could underlie anticipation of the next event in a sequence.

A comparison of EEG encoding models using audiovisual stimuli and their unimodal counterparts.

Communication in the real world is inherently multimodal. When having a conversation, typically sighted and hearing people use both auditory and visual cues to understand one another. For example, objects may make sounds as they move in space, or we may use the movement of a person's mouth to better understand what they are saying in a noisy environment. Still, many neuroscience experiments rely on unimodal stimuli to understand encoding of sensory features in the brain. The extent to which visual information may influence encoding of auditory information and vice versa in natural environments is thus unclear. Here, we addressed this question by recording scalp electroencephalography (EEG) in 11 subjects as they listened to and watched movie trailers in audiovisual (AV), visual (V) only, and audio (A) only conditions. We then fit linear encoding models that described the relationship between the brain responses and the acoustic, phonetic, and visual information in the stimuli. We also compared whether auditory and visual feature tuning was the same when stimuli were presented in the original AV format versus when visual or auditory information was removed. In these stimuli, visual and auditory information was relatively uncorrelated, and included spoken narration over a scene as well as animated or live-action characters talking with and without their face visible. For this stimulus, we found that auditory feature tuning was similar in the AV and A-only conditions, and similarly, tuning for visual information was similar when stimuli were presented with the audio present (AV) and when the audio was removed (V only). In a cross prediction analysis, we investigated whether models trained on AV data predicted responses to A or V only test data similarly to models trained on unimodal data. Overall, prediction performance using AV training and V test sets was similar to using V training and V test sets, suggesting that the auditory information has a relatively smaller effect on EEG. In contrast, prediction performance using AV training and A only test set was slightly worse than using matching A only training and A only test sets. This suggests the visual information has a stronger influence on EEG, though this makes no qualitative difference in the derived feature tuning. In effect, our results show that researchers may benefit from the richness of multimodal datasets, which can then be used to answer more than one research question.

Current progress on characteristics of intracranial electrophysiology related to prolonged disorders of consciousness

Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

Mnemonic brain state engagement is diminished in healthy aging.

Healthy older adults typically show impaired episodic memory - memory for when and where an event oc-curred - but intact semantic memory - knowledge for general information and facts. As older adults also have difficulty inhibiting the retrieval of prior knowledge from memory, their selective decline in episodic memory may be due to a tendency to over engage the retrieval state, a brain state in which attention is focused internally in an attempt to access prior knowledge. The retrieval state trades off with the encoding state, a brain state which supports the formation of new memories. Therefore, episodic memory declines in older adults may be the result of differential engagement in mnemonic brain states. Our hypothesis is that older adults are biased toward a retrieval state. We recorded scalp electroencephalography while young, middle-aged and older adults performed a memory task in which they were explicitly directed to either encode the currently presented object stimulus or retrieve a previously presented, categorically-related object stimulus. We used multivariate pattern analysis of spectral activity to decode engagement in the retrieval vs. encoding state. We find that all three age groups can follow top-down instructions to selectively engage in encoding or retrieval and that we can decode mnemonic states for all age groups. However, we find that mnemonic brain state engagement is diminished for older adults relative to middle-aged adults. Our interpretation is that a combination of executive control deficits and a modest bias to retrieve modulates older adults' mnemonic state engagement. Together, these findings suggest that dif-ferential mnemonic state engagement may underlie age-related memory changes.

Transcranial focused ultrasound modulates visual thalamus in a nonhuman primate model.

The thalamus plays a pivotal role as a neural hub, integrating and distributing visual information to cortical regions responsible for visual processing. Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive brain stimulation technology, enabling modulation of neural circuits with high spatial precision. This study investigates the tFUS neuromodulation at visual thalamus and characterizes the resultant effects on interconnected visual areas in a nonhuman primate model. Experiments were conducted on a rhesus macaque trained in a visual fixation task, combining tFUS stimulation with simultaneous scalp electroencephalography (EEG) and intracranial recordings from area V4, a region closely linked to the thalamus. Ultrasound was delivered through a 128-element random array ultrasound transducer operating at 700 kHz, with the focus steered onto the pulvinar of the thalamus based on neuroanatomical atlas and individual brain model. EEG source imaging revealed localized tFUS-induced activities in the thalamus, midbrain, and visual cortical regions. Critically, tFUS stimulation of the pulvinar can elicit robust neural responses in V4 without visual input, manifested as significant modulations in local field potentials, elevated alpha and gamma power, corroborating the functional thalamocortical connectivity. Furthermore, the tFUS neuromodulatory effects on visually-evoked V4 activities were region-specific within the thalamus and dependent on ultrasound pulse repetition frequency. This work provides direct electrophysiological evidence demonstrating the capability of tFUS in modulating the visual thalamus and its functional impact on interconnected cortical regions in a large mammalian model, paving the way for potential investigations for tFUS treating visual, sensory, and cognitive impairments.

Successful retrieval is its own reward.

The ability to successfully remember past events is critical to our daily lives, yet the neural mechanisms underlying the motivation to retrieve is unclear. Although reward-system activity and feedback-related signals have been observed during memory retrieval, whether this signal reflects intrinsic reward or goal-attainment is unknown. Adjudicating between these two alternatives is crucial for understanding how individuals are motivated to engage in retrieval and how retrieval supports later remembering. To test these two accounts, we conducted between-subjects recognition memory tasks on human participants undergoing scalp electroencephalography and varied test-phase goals (recognize old vs. detect new items). We used an independently validated feedback classifier to measure positive feedback evidence. We find positive feedback following successful retrieval regardless of task goals. Together, these results suggest that successful retrieval is intrinsically rewarding. Such a feedback signal may promote future retrieval attempts as well as bolster later memory for the successfully retrieved events.

Individualized music induces theta-gamma phase-amplitude coupling in patients with disorders of consciousness.

This study aimed to determine whether patients with disorders of consciousness (DoC) could experience neural entrainment to individualized music, which explored the cross-modal influences of music on patients with DoC through phase-amplitude coupling (PAC). Furthermore, the study assessed the efficacy of individualized music or preferred music (PM) versus relaxing music (RM) in impacting patient outcomes, and examined the role of cross-modal influences in determining these outcomes. Thirty-two patients with DoC [17 with vegetative state/unresponsive wakefulness syndrome (VS/UWS) and 15 with minimally conscious state (MCS)], alongside 16 healthy controls (HCs), were recruited for this study. Neural activities in the frontal-parietal network were recorded using scalp electroencephalography (EEG) during baseline (BL), RM and PM. Cerebral-acoustic coherence (CACoh) was explored to investigate participants' abilitiy to track music, meanwhile, the phase-amplitude coupling (PAC) was utilized to evaluate the cross-modal influences of music. Three months post-intervention, the outcomes of patients with DoC were followed up using the Coma Recovery Scale-Revised (CRS-R). HCs and patients with MCS showed higher CACoh compared to VS/UWS patients within musical pulse frequency (p = 0.016, p = 0.045; p < 0.001, p = 0.048, for RM and PM, respectively, following Bonferroni correction). Only theta-gamma PAC demonstrated a significant interaction effect between groups and music conditions (F (2,44) = 2.685, p = 0.036). For HCs, the theta-gamma PAC in the frontal-parietal network was stronger in the PM condition compared to the RM (p = 0.016) and BL condition (p < 0.001). For patients with MCS, the theta-gamma PAC was stronger in the PM than in the BL (p = 0.040), while no difference was observed among the three music conditions in patients with VS/UWS. Additionally, we found that MCS patients who showed improved outcomes after 3 months exhibited evident neural responses to preferred music (p = 0.019). Furthermore, the ratio of theta-gamma coupling changes in PM relative to BL could predict clinical outcomes in MCS patients (r = 0.992, p < 0.001). Individualized music may serve as a potential therapeutic method for patients with DoC through cross-modal influences, which rely on enhanced theta-gamma PAC within the consciousness-related network.

Top-Down Task Goals Induce the Retrieval State.

Engaging the retrieval state (Tulving, 1983) impacts processing and behavior (Long and Kuhl, 2019, 2021; Smith et al., 2022), but the extent to which top-down factors-explicit instructions and goals-versus bottom-up factors-stimulus properties such as repetition and similarity-jointly or independently induce the retrieval state is unclear. Identifying the impact of bottom-up and top-down factors on retrieval state engagement is critical for understanding how control of task-relevant versus task-irrelevant brain states influence cognition. We conducted between-subjects recognition memory tasks on male and female human participants in which we varied test phase goals. We recorded scalp electroencephalography and used an independently validated mnemonic state classifier (Long, 2023) to measure retrieval state engagement as a function of top-down task goals (recognize old vs detect new items) and bottom-up stimulus repetition (hits vs correct rejections (CRs)). We find that whereas the retrieval state is engaged for hits regardless of top-down goals, the retrieval state is only engaged during CRs when the top-down goal is to recognize old items. Furthermore, retrieval state engagement is greater for low compared to high confidence hits when the task goal is to recognize old items. Together, these results suggest that top-down demands to recognize old items induce the retrieval state independent from bottom-up factors, potentially reflecting the recruitment of internal attention to enable access of a stored representation.

Comparison of Continuous Intracortical and Scalp Electroencephalography in Comatose Patients with Acute Brain Injury.

Depth electroencephalography (dEEG) is a recent invasive monitoring technique used in patients with acute brain injury. This study aimed to describe in detail the clinical manifestations of nonconvulsive seizures (NCSzs) with and without a surface EEG correlate, analyze their long-standing effects, and provide data that contribute to understanding the significance of certain scalp EEG patterns observed in critically ill patients. We prospectively enrolled a cohort of 33 adults with severe acute brain injury admitted to the neurological intensive care unit. All of them underwent multimodal invasive monitoring, including dEEG. All patients were scanned on a 3T magnetic resonance imaging scanner at 6months after hospital discharge, and mesial temporal atrophy (MTA) was calculated using a visual scale. In 21 (65.6%) of 32 study participants, highly epileptiform intracortical patterns were observed. A total of 11 (34.3%) patients had electrographic or electroclinical seizures in the dEEG, of whom 8 had both spontaneous and stimulus-induced (SI) seizures, and 3 patients had only spontaneous intracortical seizures. An unequivocal ictal scalp correlate was observed in only 3 (27.2%) of the 11 study participants. SI-NCSzs occurred during nursing care, medical procedures, and family visits. Subtle clinical manifestations, such as restlessness, purposeless stereotyped movements of the upper limbs, ventilation disturbances, jerks, head movements, hyperextension posturing, chewing, and oroalimentary automatisms, occurred during intracortical electroclinical seizures. MTA was detected in 18 (81.8%) of the 22 patients. There were no statistically significant differences between patients with MTA with and without seizures or status epilepticus. Most NCSzs in critically ill comatose patients remain undetectable on scalp EEG. SI-NCSzs frequently occur during nursing care, medical procedures, and family visits. Semiology of NCSzs included ictal minor signs and subtle symptoms, such as breathing pattern changes manifested as patient-ventilator dyssynchrony.