Pre-hospital stroke monitoring past, present, and future: a perspective.

Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.

Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.

Background: Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation. Methods: Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables. Results: The prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients. Conclusion: Our findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD.

Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

BACKGROUND: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. OBJECTIVE: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. METHODS: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies' ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. RESULTS: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. CONCLUSIONS: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35442.

Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment.

Background: Cognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment. Methods: This study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load. Results: MMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24-27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load. Discussion: This study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention. Trial Registration: NIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Continuous Monitoring of Mental Load During Virtual Simulator Training for Laparoscopic Surgery Reflects Laparoscopic Dexterity: A Comparative Study Using a Novel Wireless Device.

INTRODUCTION: Cognitive Load Theory (CLT) relates to the efficiency with which individuals manipulate the limited capacity of working memory load. Repeated training generally results in individual performance increase and cognitive load decrease, as measured by both behavioral and neuroimaging methods. One of the known biomarkers for cognitive load is frontal theta band, measured by an EEG. Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment. Measuring the cognitive load of young surgeons undergoing such training can help to determine whether they are ready to take part in a real surgery. In this study, we measured the performance of medical students and interns in a surgery simulator, while their brain activity was monitored by a single-channel EEG. METHODS: A total of 38 medical students and interns were divided into three groups and underwent three experiments examining their behavioral performances. The participants were performing a task while being monitored by the Simbionix LAP MENTOR™. Their brain activity was simultaneously measured using a single-channel EEG with novel signal processing (Aurora by Neurosteer®). Each experiment included three trials of a simulator task performed with laparoscopic hands. The time retention between the tasks was different in each experiment, in order to examine changes in performance and cognitive load biomarkers that occurred during the task or as a result of nighttime sleep consolidation. RESULTS: The participants' behavioral performance improved with trial repetition in all three experiments. In Experiments 1 and 2, delta band and the novel VC9 biomarker (previously shown to correlate with cognitive load) exhibited a significant decrease in activity with trial repetition. Additionally, delta, VC9, and, to some extent, theta activity decreased with better individual performance. DISCUSSION: In correspondence with previous research, EEG markers delta, VC9, and theta (partially) decreased with lower cognitive load and higher performance; the novel biomarker, VC9, showed higher sensitivity to lower cognitive load levels. Together, these measurements may be used for the neuroimaging assessment of cognitive load while performing simulator laparoscopic tasks. This can potentially be expanded to evaluate the efficacy of different medical simulations to provide more efficient training to medical staff and measure cognitive and mental loads in real laparoscopic surgeries.

Postural control in karate practitioners: Does practice make perfect?

BACKGROUND: Karate training likely leads to enhanced postural control, however, previous studies did not always include a healthy, physically active comparison group and the findings are inconsistent. RESEARCH QUESTION: Will the postural control of experienced karate practitioners be better than that of experienced swimmers, i.e., athletes with similar characteristics who do not practice under conditions that require upright postural control? METHODS: In this cross-sectional study, 20 experienced, male karate practitioners and 20 experienced, male swimmers, ages 20-50, performed four standing postural control tasks of increasing difficulty: (a) two-legged stance with eyes open; (b) one-legged stance with eyes open; (c) one-legged stance with eyes closed, and (d) a dual-task, one-legged stance with eyes closed and a verbal fluency challenge. The primary outcome measure was a functional, behavioral measure that reflects the loss of balance. Specifically, in tasks that included one-legged stance, every touch of the raised foot to the floor was counted. Center-of-gravity movements were measured using a wearable sensor. RESULTS: Task-related differences were seen in all of the postural control measures. In the OneLegEyesClosed task, the median number of touches was 0.00 in the karate group and 6.50 in the swimming group (p < 0.001). In the OneLegEyesClosedWords task, the median number of touches was 0.00 in the karate group and 5.00 in the swimming group (p < 0.001). Shannon entropy, a measure of the complexity of the sway of the center-of-gravity, was lower in the karate group (p = 0.002), compared to the swimmers. SIGNIFICANCE: Karate training is associated with a higher level of postural control, even when compared to a physically active age-matched comparison group. In addition to supporting the specificity of exercise training principle, these findings raise the intriguing possibility that karate may be useful as a form of pre-habilitation, potentially aiding in the prevention of age-associated declines in balance control.

Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience.

Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF)1. Particularly for stress management, targeting deeply located limbic areas involved in stress processing2 has paved new paths for brain-guided interventions. However, the high cost and immobility of fMRI constitute a challenging drawback for the scalability (accessibility and cost-effectiveness) of the approach, particularly for clinical purposes3. The current study aimed to overcome the limited applicability of rt-fMRI by using an electroencephalography (EEG) model endowed with improved spatial resolution, derived from simultaneous EEG-fMRI, to target amygdala activity (termed amygdala electrical fingerprint (Amyg-EFP))4-6. Healthy individuals (n = 180) undergoing a stressful military training programme were randomly assigned to six Amyg-EFP-NF sessions or one of two controls (control-EEG-NF or NoNF), taking place at the military training base. The results demonstrated specificity of NF learning to the targeted Amyg-EFP signal, which led to reduced alexithymia and faster emotional Stroop, indicating better stress coping following Amyg-EFP-NF relative to controls. Neural target engagement was demonstrated in a follow-up fMRI-NF, showing greater amygdala blood-oxygen-level-dependent downregulation and amygdala-ventromedial prefrontal cortex functional connectivity following Amyg-EFP-NF relative to NoNF. Together, these results demonstrate limbic specificity and efficacy of Amyg-EFP-NF during a stressful period, pointing to a scalable non-pharmacological yet neuroscience-based training to prevent stress-induced psychopathology.

Author Correction: Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience.

The original and corrected Acknowledgements are shown in the accompanying Author Correction.

Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia.

Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplificating effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation. We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6 ±â€¯11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion. REM latency and objective sleep quality index were robustly improved following the treatment course only in the real-neurofeedback group (time × group p < 0.05) and to a greater extent among good modulators (time × sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the neurofeedback training. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the real-neurofeedback group (time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures. Our findings suggest that Amyg-EFP-neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation treatment in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the substantial link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.

Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes.

This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.

Identification of a T cell gene expression clock obtained by exploiting a MZ twin design.

Many studies investigated age-related changes in gene expression of different tissues, with scarce agreement due to the high number of affecting factors. Similarly, no consensus has been reached on which genes change expression as a function of age and not because of environment. In this study we analysed gene expression of T lymphocytes from 27 healthy monozygotic twin couples, with ages ranging over whole adult lifespan (22 to 98 years). This unique experimental design allowed us to identify genes involved in normative aging, which expression changes independently from environmental factors. We obtained a transcriptomic signature with 125 genes, from which chronological age can be estimated. This signature has been tested in two datasets of same cell type hybridized over two different platforms, showing a significantly better performance compared to random signatures. Moreover, the same signature was applied on a dataset from a different cell type (human muscle). A lower performance was obtained, indicating the possibility that the signature is T cell-specific. As a whole our results suggest that this approach can be useful to identify age-modulated genes.

A Real-Time Kinect Signature-Based Patient Home Monitoring System.

Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect ("Kinect") system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints' estimations, and erroneous multiplexing of different subjects' estimations to one. This study addresses these limitations by incorporating a set of features that create a unique "Kinect Signature". The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.

One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation.

The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level-dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level-dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.

Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach.

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

Monitoring cardiac stress using features extracted from S1 heart sounds.

It is known that acoustic heart sounds carry significant information about the mechanical activity of the heart. In this paper, we present a novel type of cardiac monitoring based on heart sound analysis. Specifically, we study two morphological features and their associations with physiological changes from the baseline state. The framework is demonstrated on recordings during laparoscopic surgeries of 15 patients. Insufflation, which is performed during laparoscopic surgery, provides a controlled, externally induced cardiac stress, enabling an analysis of each patient with respect to their own baseline. We demonstrate that the proposed features change during cardiac stress, and the change is more significant for patients with cardiac problems. Furthermore, we show that other well-known ECG morphology features are less sensitive in this specific cardiac stress experiment.

Machine learning fMRI classifier delineates subgroups of schizophrenia patients.

BACKGROUND: The search for a validated neuroimaging-based brain marker in psychiatry has thus far been fraught with both clinical and methodological difficulties. The present study aimed to apply a novel data-driven machine-learning approach to functional Magnetic Resonance Imaging (fMRI) data obtained during a cognitive task in order to delineate the neural mechanisms involved in two schizophrenia subgroups: schizophrenia patients with and without Obsessive-Compulsive Disorder (OCD). METHODS: 16 schizophrenia patients with OCD ("schizo-obsessive"), 17 pure schizophrenia patients, and 20 healthy controls underwent fMRI while performing a working memory task. A whole brain search for activation clusters of cognitive load was performed using a recently developed data-driven multi-voxel pattern analysis (MVPA) approach, termed Searchlight Based Feature Extraction (SBFE), and which yields a robust fMRI-based classifier. RESULTS: The SBFE successfully classified the two schizophrenia groups with 91% accuracy based on activations in the right intraparietal sulcus (r-IPS), which further correlated with reduced symptom severity among schizo-obsessive patients. CONCLUSIONS: The results indicate that this novel SBFE approach can successfully delineate between symptom dimensions in the context of complex psychiatric morbidity.

Systems biology and brain activity in neuronal pathways by smart device and advanced signal processing.

Contemporary biomedicine is producing large amount of data, especially within the fields of "omic" sciences. Nevertheless, other fields, such as neuroscience, are producing similar amount of data by using non-invasive techniques such as imaging, functional magnetic resonance and electroencephalography. Nowadays a big challenge and a new research horizon for Systems Biology is to develop methods to integrate and model this data in an unifying framework capable to disentangle this amazing complexity. In this paper we show how methods from genomic data analysis can be applied to brain data. In particular the concept of pathways, networks and multiplex are discussed. These methods can lead to a clear distinction of various regimes of brain activity. Moreover, this method could be the basis for a Systems Biology analysis of brain data and for the integration of these data in a multivariate and multidimensional framework. The feasibility of this integration is strongly dependent from the feature extraction method used. In our case we used an "alphabet" derived from a multi-resolution analysis that is capable to capture the most relevant information from these complex signals.

Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG neurofeedback.

The transition from being fully awake to pre-sleep occurs daily just before falling asleep; thus its disturbance might be detrimental. Yet, the neuronal correlates of the transition remain unclear, mainly due to the difficulty in capturing its inherent dynamics. We used an EEG theta/alpha neurofeedback to rapidly induce the transition into pre-sleep and simultaneous fMRI to reveal state-dependent neural activity. The relaxed mental state was verified by the corresponding enhancement in the parasympathetic response. Neurofeedback sessions were categorized as successful or unsuccessful, based on the known EEG signature of theta power increases over alpha, temporally marked as a distinct "crossover" point. The fMRI activation was considered before and after this point. During successful transition into pre-sleep the period before the crossover was signified by alpha modulation that corresponded to decreased fMRI activity mainly in sensory gating related regions (e.g. medial thalamus). In parallel, although not sufficient for the transition, theta modulation corresponded with increased activity in limbic and autonomic control regions (e.g. hippocampus, cerebellum vermis, respectively). The post-crossover period was designated by alpha modulation further corresponding to reduced fMRI activity within the anterior salience network (e.g. anterior cingulate cortex, anterior insula), and in contrast theta modulation corresponded to the increased variance in the posterior salience network (e.g. posterior insula, posterior cingulate cortex). Our findings portray multi-level neural dynamics underlying the mental transition from awake to pre-sleep. To initiate the transition, decreased activity was required in external monitoring regions, and to sustain the transition, opposition between the anterior and posterior parts of the salience network was needed, reflecting shifting from extra- to intrapersonal based processing, respectively.

An EEG Finger-Print of fMRI deep regional activation.

This work introduces a general framework for producing an EEG Finger-Print (EFP) which can be used to predict specific brain activity as measured by fMRI at a given deep region. This new approach allows for improved EEG spatial resolution based on simultaneous fMRI activity measurements. Advanced signal processing and machine learning methods were applied on EEG data acquired simultaneously with fMRI during relaxation training guided by on-line continuous feedback on changing alpha/theta EEG measure. We focused on demonstrating improved EEG prediction of activation in sub-cortical regions such as the amygdala. Our analysis shows that a ridge regression model that is based on time/frequency representation of EEG data from a single electrode, can predict the amygdala related activity significantly better than a traditional theta/alpha activity sampled from the best electrode and about 1/3 of the times, significantly better than a linear combination of frequencies with a pre-defined delay. The far-reaching goal of our approach is to be able to reduce the need for fMRI scanning for probing specific sub-cortical regions such as the amygdala as the basis for brain-training procedures. On the other hand, activity in those regions can be characterized with higher temporal resolution than is obtained by fMRI alone thus revealing additional information about their processing mode.