Text and image generation from intracranial electroencephalography using an embedding space for text and images.

OBJECTIVE: Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis (ALS). An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions. Approach: In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortexes. A CLIP image vector was inferred from the high-γ power of the iEEG signals recorded while viewing the images. Main results: Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy. Significance: The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication. .

Is Phantom Limb Awareness Necessary for the Treatment of Phantom Limb Pain?

Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Characteristics of Changes in Intrathecal Baclofen Dosage over Time due to Causative Disease.

Intrathecal baclofen (ITB) therapy effectively treats spasticity caused by brain or spinal cord lesions. However, only a few studies compare the course of treatment for different diseases. We investigated the change in daily dose of baclofen per year and its associated adverse events in patients presenting with the three most common etiologies at our institute: hereditary spastic paraplegia, cerebral palsy, and spinal cord injury. The ITB pumps were implanted from July 2007 to August 2019, with a mean follow-up period of 70 months. In patients with hereditary spastic paraplegia, baclofen dosage was reduced after eight years following ITB introduction, and the treatment was terminated in one patient owing to disease progression. In patients with cerebral palsy, the dosage increased gradually, and became constant in the 11th year. Patients with spinal cord injury gradually increased their baclofen dosage throughout the entire observation period. Severity and adverse event rates were higher in patients with cerebral palsy than in others. The degree and progression of spasticity varied depending on the causative disease. Understanding the characteristics and natural history of each disease is important when continuing ITB treatment.

Magnetoencephalographic neurofeedback training decreasesß-low-γphase-amplitude coupling of the motor cortex of healthy adults: a double-blinded randomized crossover feasibility study.

Objective.The coupling between the beta (13-30 Hz) phase and low gamma (50-100 Hz) amplitude in the motor cortex is thought to regulate motor performance. Abnormal phase-amplitude coupling (PAC) of beta-low gamma (ß-low-γPAC) is associated with motor symptoms of Parkinson's disease. However, the causal relationship betweenß-low-γPAC and motor performance in healthy subjects is unknown. We hypothesized that healthy subjects could change the strength of theß-low-γPAC in the resting state by neurofeedback training (NFT) to control theß-low-γPAC, such that the motor performance changes in accordance with the changes inß-low-γPAC in the resting state.Approach.We developed an NFT to control the strength of theß-low-γPAC in the motor cortex, which was evaluated by magnetoencephalography (MEG) using a current source estimation technique. Twenty subjects were enrolled in a double-blind randomized crossover trial to test the feasibility of the MEG NFT. In the NFT for 2 d, the subjects were instructed to reduce the size of a black circle whose radius was proportional (down-training) or inversely proportional (up-training) to the strength of theß-low-γPAC. The reaction times (RTs) to press a button according to some cues were evaluated before and after training. This study was registered at ClinicalTrials.gov (NCT03837548) and UMIN-CTR (UMIN000032937).Main results.Theß-low-γPAC during the resting state was significantly decreased after down-training, although not significantly after up-training. RTs tended to decrease after both trainings, however the differences were not statistically significant. There was no significant correlation between the changes inß-low-γPAC during rest and RTs.Significance.The proposed MEG NFT was demonstrated to change theß-low-γPAC of the motor cortex in healthy subjects. However, a relationship between PAC and RT has not yet been demonstrated.

Clinical Factors Related to Outcomes in Pediatric Epilepsy Surgery: Insight into Predictors of Poor Surgical Outcome.

Successful surgery for drug-resistant pediatric epilepsy can facilitate motor and cognitive development and improve quality of life by resolution or reduction of epileptic seizures. Therefore, surgery should be considered early in the disease course. However, in some cases, the estimated surgical outcomes are not achieved, and additional surgical treatments are considered. In this study, we investigated the clinical factors related with such unsatisfactory outcomes.We reviewed the clinical data of 92 patients who underwent 112 surgical procedures (69 resection and 53 palliation procedures). Surgical outcomes were assessed according to the postoperative disease status, which was classified as good, controlled, and poor. The following clinical factors were analyzed in relation to surgical outcome: sex, age at onset, etiology (malformation of cortical development, tumor, temporal lobe epilepsy, scar, inflammation, and non-lesional epilepsy), presence of genetic cause, and history of developmental epileptic encephalopathy. At a median of 59 (30-81.25) months after the initial surgery, the disease status was good in 38 (41%), controlled in 39 (42%), and poor in 15 (16%) patients. Among the evaluated factors, etiology exhibited the strongest correlation with surgical outcomes. Tumor-induced and temporal lobe epilepsy were correlated with good, whereas malformation of cortical development, early seizure onset, and presence of genetic cause were correlated with poor disease status. Although epilepsy surgery for the patients who present with the latter factors is challenging, these patients demonstrate a greater need for surgical treatment. Hence, development of more effective surgical options is warranted, including palliative procedures.

Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. METHODS: We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N = 55), patients with Alzheimer's disease (AD, N = 101), dementia with Lewy bodies (DLB, N = 75), and idiopathic normal pressure hydrocephalus (iNPH, N = 60) to evaluate the discriminative accuracy of these diseases. RESULTS: High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). CONCLUSION: This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.

Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.

Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.

Tight adhesions after spinal cord stimulation observed during dorsal root entry zone lesioning for pain after spinal root avulsion: illustrative cases.

BACKGROUND: Patients often experience strong shooting pains after spinal root avulsion. The efficacy of spinal cord stimulation (SCS) for this type of pain is inconsistent; however, dorsal root entry zone (DREZ) lesioning (DREZ-lesion) has often proven to be an effective treatment modality. The authors report two cases in which DREZ-lesion was performed to treat pain after spinal root avulsion after implantation of SCS, but the operations were challenging due to strong adhesions. OBSERVATIONS: The authors present two cases of patients with pain after spinal root avulsion in whom SCS implantation was only temporarily effective. Patients complained of persistent and paroxysmal shooting pains in the upper extremities. SCS removal and DREZ-lesion were performed, but adhesions in the epidural and subdural space contacting the leads were strong, making it difficult to expose the DREZ. LESSONS: Although adhesions around the spinal cord can be caused by trauma, the authors believe that in these cases, the adhesions could have been caused by the SCS leads. There are few previous reports confirming the efficacy of SCS in treating pain after spinal root avulsion; therefore, caution is required when considering SCS implantation.

Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.

Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain. Neurofeedback training was performed in a single-blind, randomized, crossover trial using two decoders constructed using motor cortical currents measured during phantom hand movements; the motor cortical currents contralateral or ipsilateral to the phantom hand (contralateral and ipsilateral training) were estimated from magnetoencephalograms. Patients were instructed to control the size of a disk, which was proportional to the decoding results, but to not move their phantom hands or other body parts. The pain assessed by the visual analogue scale was significantly greater after contralateral training than after ipsilateral training. Classification accuracy of phantom hand movements significantly increased only after contralateral training. These results suggested that the proposed neurofeedback training changed phantom hand representation and modulated pain without explicit phantom hand movements or hand-like visual feedback, thus showing the relation between the phantom hand representations and pain. PERSPECTIVE: Our work demonstrates the feasibility of using neurofeedback training to change phantom hand representation and modulate pain perception without explicit phantom hand movements and hand-like visual feedback. The results enhance the mechanistic understanding of certain treatments, such as mirror therapy, that change the sensorimotor cortical representation.

Evaluating the Safety of Simultaneous Intracranial Electroencephalography and Functional Magnetic Resonance Imaging Acquisition Using a 3 Tesla Magnetic Resonance Imaging Scanner.

Background: The unsurpassed sensitivity of intracranial electroencephalography (icEEG) and the growing interest in understanding human brain networks and ongoing activities in health and disease have make the simultaneous icEEG and functional magnetic resonance imaging acquisition (icEEG-fMRI) an attractive investigation tool. However, safety remains a crucial consideration, particularly due to the impact of the specific characteristics of icEEG and MRI technologies that were safe when used separately but may risk health when combined. Using a clinical 3-T scanner with body transmit and head-receive coils, we assessed the safety and feasibility of our icEEG-fMRI protocol. Methods: Using platinum and platinum-iridium grid and depth electrodes implanted in a custom-made acrylic-gel phantom, we assessed safety by focusing on three factors. First, we measured radio frequency (RF)-induced heating of the electrodes during fast spin echo (FSE, as a control) and the three sequences in our icEEG-fMRI protocol. Heating was evaluated with electrodes placed orthogonal or parallel to the static magnetic field. Using the configuration with the greatest heating observed, we then measured the total heating induced in our protocol, which is a continuous 70-min icEEG-fMRI session comprising localizer, echo-planar imaging (EPI), and magnetization-prepared rapid gradient-echo sequences. Second, we measured the gradient switching-induced voltage using configurations mimicking electrode implantation in the frontal and temporal lobes. Third, we assessed the gradient switching-induced electrode movement by direct visual detection and image analyses. Results: On average, RF-induced local heating on the icEEG electrode contacts tested were greater in the orthogonal than parallel configuration, with a maximum increase of 0.2°C during EPI and 1.9°C during FSE. The total local heating was below the 1°C safety limit across all contacts tested during the 70-min icEEG-fMRI session. The induced voltage was within the 100-mV safety limit regardless of the configuration. No gradient switching-induced electrode displacement was observed. Conclusion: We provide evidence that the additional health risks associated with heating, neuronal stimulation, or device movement are low when acquiring fMRI at 3 T in the presence of clinical icEEG electrodes under the conditions reported in this study. High specific absorption ratio sequences such as FSE should be avoided to prevent potential inadvertent tissue heating.

Altered Thalamic Connectivity Due to Focused Ultrasound Thalamotomy in Patients with Essential Tremor.

BACKGROUND: Although stereotactic ablation surgery is known to ameliorate involuntary movement dramatically, little is known regarding alterations in whole-brain networks due to disruption of the deep brain nucleus. To explore changes in the whole-brain network after thalamotomy, we analyzed structural and functional connectivity alterations using resting-state functional magnetic resonance imaging and diffusion tensor imaging in patients with essential tremor who had undergone focused ultrasound (FUS) thalamotomy. METHODS: Seven patients with intractable essential tremors and 7 age-matched healthy controls were enrolled in the study. The tremor score in essential tremor patients was assessed, and resting-state functional magnetic resonance imaging and diffusion tensor imaging were performed before and 3 months after left ventral intermediate nucleus thalamotomy using FUS. RESULTS: There was a significant improvement in the tremor of the right hand after FUS thalamotomy. Seed-based functional connectivity analysis revealed a significant increase in functional connectivity between the left thalamus and the caudal part of the dorsal premotor cortex after FUS thalamotomy. Structural connectivity analysis did not detect statistically significant changes between before and after FUS. There was no correlation between the changes in functional connectivity and tremor score. CONCLUSIONS: Although the number of cases is small, our results show that functional connectivity between the thalamus and the premotor cortex increases after the amelioration of tremors by FUS thalamotomy. The lack of correlation between increased functional connectivity and clinical tremor scores suggests that the observed increase in functional connectivity may be a compensatory change in the secondary sensorimotor changes that occur after thalamotomy.

Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy.

Objective.Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups.Approach.We obtained resting-state MEG and magnetic resonance imaging (MRI) in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and MRI to calculate Power in theδ(1-3 Hz),θ(4-7 Hz),α(8-13 Hz),ß(13-30 Hz), lowγ(35-55 Hz), and highγ(65-90 Hz) bands and FC in theθband. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases ofδ, θ, α, andßand the amplitudes of low and highγ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, FC, and features extracted by deep learning (DL) individually or combined, we tested whether PAC improves discrimination accuracy for the two groups.Main results.The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest forθ/lowγin the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and DL.Significance.Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.

Voluntary control of semantic neural representations by imagery with conflicting visual stimulation.

Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended asymmetrically on the perceived and imagined categories. Shared neural representation between mental imagery and perception was still activated by the imagery under semantically conflicting perceptions depending on the semantic category.

Frequency band coupling with high-frequency activities in tonic-clonic seizures shifts from θ to δ band.

OBJECTIVE: To clarify variations in the relationship between high-frequency activities (HFAs) and low-frequency bands from the tonic to the clonic phase in focal to bilateral tonic-clonic seizures (FBTCS), using phase-amplitude coupling. METHODS: This retrospective study enrolled six patients with drug-resistant focal epilepsy who underwent intracranial electrode placement at Osaka University Hospital (July 2018-July 2019). We recorded 11 FBTCS. The synchronization index (SI) and receiver-operating characteristic (ROC) analysis were used to analyze the coupling between HFA amplitude (80-250 Hz) and lower frequencies phase. RESULTS: In the tonic phase, the θ (4-8 Hz)-HFA coupling peaked, and the HFA power occurred at baseline (0 µV) of θ oscillations. In contrast, in the clonic phase, the δ (2-4 Hz)-HFA coupling peaked, and the HFA power occurred at the trough of δ oscillations. ROC analysis indicated that the δ-HFA SI discriminated well the clonic from the tonic phase. CONCLUSIONS: The main low-frequency band modulating the HFA shifted from the θ band in the tonic phase to the δ band in the clonic phase. SIGNIFICANCE: Neurophysiological key frequency bands were implied to be the θ band and δ band in tonic and clonic seizures, respectively, which improves our understanding of FBTCS.

Magnetoencephalography detects phase-amplitude coupling in Parkinson's disease.

To characterize Parkinson's disease, abnormal phase-amplitude coupling is assessed in the cortico-basal circuit using invasive recordings. It is unknown whether the same phenomenon might be found in regions other than the cortico-basal ganglia circuit. We hypothesized that using magnetoencephalography to assess phase-amplitude coupling in the whole brain can characterize Parkinson's disease. We recorded resting-state magnetoencephalographic signals in patients with Parkinson's disease and in healthy age- and sex-matched participants. We compared whole-brain signals from the two groups, evaluating the power spectra of 3 frequency bands (alpha, 8-12 Hz; beta, 13-25 Hz; gamma, 50-100 Hz) and the coupling between gamma amplitude and alpha or beta phases. Patients with Parkinson's disease showed significant beta-gamma phase-amplitude coupling that was widely distributed in the sensorimotor, occipital, and temporal cortices; healthy participants showed such coupling only in parts of the somatosensory and temporal cortices. Moreover, beta- and gamma-band power differed significantly between participants in the two groups (P < 0.05). Finally, beta-gamma phase-amplitude coupling in the sensorimotor cortices correlated significantly with motor symptoms of Parkinson's disease (P < 0.05); beta- and gamma-band power did not. We thus demonstrated that beta-gamma phase-amplitude coupling in the resting state characterizes Parkinson's disease.

Difference in Analgesic Effects of Repetitive Transcranial Magnetic Stimulation According to the Site of Pain.

High-frequency repetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex for neuropathic pain has been shown to be effective, according to systematic reviews and therapeutic guidelines. However, our large, rigorous, investigator-initiated, registration-directed clinical trial failed to show a positive primary outcome, and its subgroup analysis suggested that the analgesic effect varied according to the site of pain. The aim of this study was to investigate the differences in analgesic effects of rTMS for neuropathic pain between different pain sites by reviewing our previous clinical trials. We included three clinical trials in this mini meta-analysis: a multicenter randomized controlled trial at seven hospitals (N = 64), an investigator-initiated registration-directed clinical trial at three hospitals (N = 142), and an exploratory clinical trial examining different stimulation parameters (N = 22). The primary efficacy endpoint (change in pain scale) was extracted for each patient group with pain in the face, upper limb, or lower limb, and a meta-analysis of the efficacy of active rTMS against sham stimulation was performed. Standardized mean difference (SMD) with 95% confidence interval (CI) was calculated for pain change using a random-effects model. The analgesic effect of rTMS for upper limb pain was favorable (SMD = -0.45, 95% CI: -0.77 to -0.13). In contrast, rTMS did not produce significant pain relief on lower limb pain (SMD = 0.04, 95% CI: -0.33 to 0.41) or face (SMD = -0.24, 95% CI: -1.59 to 1.12). In conclusion, these findings suggest that rTMS provides analgesic effects in patients with neuropathic pain in the upper limb, but not in the lower limb or face, under the conditions of previous clinical trials. Owing to the main limitation of small number of studies included, many aspects should be clarified by further research and high-quality studies in these patients.

Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states.

Infraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states.

Data-driven electrophysiological feature based on deep learning to detect epileptic seizures.

Objective. To identify a new electrophysiological feature characterising the epileptic seizures, which is commonly observed in different types of epilepsy.Methods. We recorded the intracranial electroencephalogram (iEEG) of 21 patients (12 women and 9 men) with multiple types of refractory epilepsy. The raw iEEG signals of the early phase of epileptic seizures and interictal states were classified by a convolutional neural network (Epi-Net). For comparison, the same signals were classified by a support vector machine (SVM) using the spectral power and phase-amplitude coupling. The features learned by Epi-Net were derived by a modified integrated gradients method. We considered the product of powers multiplied by the relative contribution of each frequency amplitude as a data-driven epileptogenicity index (d-EI). We compared the d-EI and other conventional features in terms of accuracy to detect the epileptic seizures. Finally, we compared the d-EI among the electrodes to evaluate its relationship with the resected area and the Engel classification.Results. Epi-Net successfully identified the epileptic seizures, with an area under the receiver operating characteristic curve of 0.944 ± 0.067, which was significantly larger than that of the SVM (0.808 ± 0.253,n =21;p =0.025). The learned iEEG signals were characterised by increased powers of 17-92 Hz and >180 Hz in addition to decreased powers of other frequencies. The proposed d-EI detected them with better accuracy than the other iEEG features. Moreover, the surgical resection of areas with a larger increase in d-EI was observed for all nine patients with Engel class ⩽1, but not for the 4 of 12 patients with Engel class >1, demonstrating the significant association with seizure outcomes.Significance.We derived an iEEG feature from the trained Epi-Net, which identified the epileptic seizures with improved accuracy and might contribute to identification of the epileptogenic zone.