Visual Mental Imagery and Neural Dynamics of Sensory Substitution in the Blindfolded Subjects.

Although one can recognize the environment by soundscape substituting vision to auditory signal, whether subjects could perceive the soundscape as visual or visual-like sensation has been questioned. In this study, we investigated hierarchical process to elucidate the recruitment mechanism of visual areas by soundscape stimuli in blindfolded subjects. Twenty-two healthy subjects were repeatedly trained to recognize soundscape stimuli converted by visual shape information of letters. An effective connectivity method called dynamic causal modeling (DCM) was employed to reveal how the brain was hierarchically organized to recognize soundscape stimuli. The visual mental imagery model generated cortical source signals of five regions of interest better than auditory bottom-up, cross-modal perception, and mixed models. Spectral couplings between brain areas in the visual mental imagery model were analyzed. While within-frequency coupling is apparent in bottom-up processing where sensory information is transmitted, cross-frequency coupling is prominent in top-down processing, corresponding to the expectation and interpretation of information. Sensory substitution in the brain of blindfolded subjects derived visual mental imagery by combining bottom-up and top-down processing.

Identification of cerebral cortices processing acceleration, velocity, and position during directional reaching movement with deep neural network and explainable AI.

Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.

Cortical maps of somatosensory perception in human.

Tactile and movement-related somatosensory perceptions are crucial for our daily lives and survival. Although the primary somatosensory cortex is thought to be the key structure of somatosensory perception, various cortical downstream areas are also involved in somatosensory perceptual processing. However, little is known about whether cortical networks of these downstream areas can be dissociated depending on each perception, especially in human. We address this issue by combining data from direct cortical stimulation (DCS) for eliciting somatosensation and data from high-gamma band (HG) elicited during tactile stimulation and movement tasks. We found that artificial somatosensory perception is elicited not only from conventional somatosensory-related areas such as the primary and secondary somatosensory cortices but also from a widespread network including superior/inferior parietal lobules and premotor cortex. Interestingly, DCS on the dorsal part of the fronto-parietal area including superior parietal lobule and dorsal premotor cortex often induces movement-related somatosensations, whereas that on the ventral one including inferior parietal lobule and ventral premotor cortex generally elicits tactile sensations. Furthermore, the HG mapping results of the movement and passive tactile stimulation tasks revealed considerable similarity in the spatial distribution between the HG and DCS functional maps. Our findings showed that macroscopic neural processing for tactile and movement-related perceptions could be segregated.

Characterization of brain network supporting episodic memory in the absence of one medial temporal lobe.

How the brain supports normal episodic memory function without medial temporal lobe (MTL) structures has not been well characterized, which could provide clues for new therapeutic targets for people with MTL dysfunction-related memory impairment. To characterize brain network supporting effective episodic memory function in the absence of unilateral MTL, we investigated the whole-brain cortical interactions during functional magnetic resonance imaging memory encoding paradigms of words and figures in patients who showed a normal range of memory capacity following unilateral MTL resection and healthy controls (HC). Compared to the HC, the patients showed less activation in the left inferior frontal areas and right thalamus together with greater activation in the many cortical areas including the medial prefrontal cortex (mPFC). Task-based functional connectivity (FC) analysis revealed that the mPFC showed stronger interactions with widespread brain areas in both patient groups, including the hippocampus contralateral to the resection. Moreover, the strength of the mPFC FC predicts the individual memory capacity of the patients. Our data suggest that hyperconnectivity of distributed brain areas, especially the mPFC, is a neural mechanism for memory function in the absence of one MTL.

Treatment Outcome of Auditory and Frontal Dual-Site rTMS in Tinnitus Patients and Changes in Magnetoencephalographic Functional Connectivity after rTMS: Double-Blind Randomized Controlled Trial.

BACKGROUND: Recently, the role of neural modulation in nonauditory cortices via repetitive transcranial magnetic stimulation (rTMS) for tinnitus control has been emphasized. It is now more compelling to consider these nonauditory cortices and the whole "tinnitus network" as targets for tinnitus treatment to achieve a better outcome. OBJECTIVE: We aimed to investigate the effects of active dual-site rTMS treatment in tinnitus reduction using a double-blind randomized controlled trial. METHOD: In study 1, the dual-site rTMS treatment group (n = 17) was treated daily for 4 consecutive days. The sham group (n = 13) also visited the clinic for 4 days; they received sham treatment for the same duration as the dual-site rTMS treatment group. In study 2, the rTMS treatment protocol was exactly the same as in study 1. Magnetoencephalography recordings were performed before and 1 week after the last rTMS treatment. The outcome measure was the Tinnitus Handicap Inventory (THI) score and the visual analog scale score. The effects of treatment were assessed 1, 2, 4, and 8 weeks after rTMS treatment in study 1. Then the mean band power and network changes were compared between pre- and post-treatment values after rTMS in study 2. RESULT: Patients in the dual-site rTMS treatment group exhibited significantly improved THI scores at 2, 4, and 8 weeks after rTMS treatment compared with the pretreatment scores. However, the sham group did not show any significant reduction in THI scores. When the mean band power changes were compared between pre- and post-treatment assessments, an increased oscillation power was observed in the alpha band after rTMS. CONCLUSION: A beneficial effect of rTMS on tinnitus suppression was found in the dual-site active rTMS group, but not in the sham rTMS group.

A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

BACKGROUND: A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. METHODS: The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. RESULTS: The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. CONCLUSIONS: A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portability, and the performance with EEG was comparable to that with MEG.

Blocking of irrelevant memories by posterior alpha activity boosts memory encoding.

In our daily lives, we are confronted with a large amount of information. Because only a small fraction can be encoded in long-term memory, the brain must rely on powerful mechanisms to filter out irrelevant information. To understand the neuronal mechanisms underlying the gating of information into long-term memory, we employed a paradigm where the encoding was directed by a "Remember" or a "No-Remember" cue. We found that posterior alpha activity increased prior to the "No-Remember" stimuli, whereas it decreased prior to the "Remember" stimuli. The sources were localized in the parietal cortex included in the dorsal attention network. Subjects with a larger cue-modulation of the alpha activity had better memory for the to-be-remembered items. Interestingly, alpha activity reflecting successful inhibition following the "No-Remember" cue was observed in the frontal midline structures suggesting preparatory inhibition was mediated by anterior parts of the dorsal attention network. During the presentation of the memory items, there was more gamma activity for the "Remember" compared to the "No-Remember" items in the same regions. Importantly, the anticipatory alpha power during cue predicted the gamma power during item. Our findings suggest that top-down controlled alpha activity reflects attentional inhibition of sensory processing in the dorsal attention network, which then finally gates information to long-term memory. This gating is achieved by inhibiting the processing of visual information reflected by neuronal synchronization in the gamma band. In conclusion, the functional architecture revealed by region-specific changes in the alpha activity reflects attentional modulation which has consequences for long-term memory encoding.

Power spectral aspects of the default mode network in schizophrenia: an MEG study.

BACKGROUND: Symptoms of schizophrenia are related to deficits in self-monitoring function, which may be a consequence of irregularity in aspects of the default mode network (DMN). Schizophrenia can also be characterized by a functional abnormality of the brain activity that is reflected in the resting state. Oscillatory analysis provides an important understanding of resting brain activity. However, conventional methods using electroencephalography are restricted because of low spatial resolution, despite their excellent temporal resolution.The aim of this study was to investigate resting brain oscillation and the default mode network based on a source space in various frequency bands such as theta, alpha, beta, and gamma using magnetoencephalography. In addition, we investigated whether these resting and DMN activities could distinguish schizophrenia patients from normal controls. To do this, the power spectral density of each frequency band at rest was imaged and compared on a spatially normalized brain template in 20 patients and 20 controls. RESULTS: The spatial distribution of DMN activity in the alpha band was similar to that found in previous fMRI studies. The posterior cingulate cortex (PCC) and lateral inferior parietal cortex were activated at rest, while the medial prefrontal cortex (MPFC) was deactivated at rest rather than during the task. Although the MPFC and PCC regions exhibited contrasting activation patterns, these two regions were significantly coherent at rest. The DMN and resting activities of the PCC were increased in schizophrenia patients, predominantly in the theta and alpha bands. CONCLUSIONS: By using MEG to identify the DMN regions, predominantly in the alpha band, we found that both resting and DMN activities were augmented in the posterior cingulate in schizophrenia patients. Furthermore, schizophrenia patients exhibited decreased coherence between the PCC and MPFC in the gamma band at rest.

Dual model transfer learning to compensate for individual variability in brain-computer interface.

BACKGROUND AND OBJECTIVE: Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.

Unraveling tactile categorization and decision-making in the subregions of supramarginal gyrus via direct cortical stimulation.

OBJECTIVE: This study aims to investigate the potential of direct cortical stimulation (DCS) to modulate tactile categorization and decision-making, as well as to identify the specific locations where these cognitive functions occur. METHODS: We analyzed behavioral changes in three epilepsy patients with implanted electrodes using electrocorticography (ECoG) and a vibrotactile discrimination task. DCS was applied to investigate its impact on tactile categorization and decision-making processes. We determined the precise location of the electrodes where each cognitive function was modulated. RESULTS: This functional discrimination was related with gamma band activity from ECoG. DCS selectively affected either tactile categorization or decision-making processes. Tactile categorization was modulated by stimulating the rostral part of the supramarginal gyrus, while decision-making was modulated by stimulating the caudal part. CONCLUSIONS: DCS can enhance cognitive processes and map brain regions responsible for tactile categorization and decision-making within the supramarginal gyrus. This study also demonstrates that DCS and the gamma activity of ECoG can concordantly identify the detailed brain mapping in a tactile process compared to other functional neuroimaging. SIGNIFICANCE: The combination of DCS and ECoG gamma activity provides a more nuanced and detailed understanding of brain function than traditional neuroimaging techniques alone.

Discrete tactile feature comparison subprocess in human brain during a decision-making process.

From sensory input to motor action, encoded sensory features flow sequentially along cortical networks for decision-making. Despite numerous studies probing the decision-making process, the subprocess that compares encoded sensory features before making a decision has not been fully elucidated in humans. In this study, we investigated sensory feature comparison by presenting two different tasks (a discrimination task, in which participants made decisions by comparing two sequential tactile stimuli; and a detection task, in which participants responded to the second tactile stimulus in two sequential stimuli) to epilepsy patients while recording electrocorticography (ECoG). By comparing tactile-specific gamma band (30-200 Hz) power between the two tasks, the decision-making process was divided into three subprocesses-categorization, comparison, and decision-consistent with a previous study (Heekeren et al., 2004). These subprocesses occurred sequentially in the dorsolateral prefrontal cortex, premotor cortex, secondary somatosensory cortex, and parietal lobe. Gamma power showed two different patterns of correlation with response time. In the inferior parietal lobule (IPL), there was a negative correlation. This means that as gamma power increased, response time decreased. In the secondary somatosensory cortex (S2), there was a positive correlation. Here, as gamma power increased, response time also increased. These results indicate that the IPL and S2 encode tactile feature comparison differently. Our connectivity analysis showed that the S2 transmitted tactile information to the IPL. Our findings suggest that multiple areas in the parietal lobe encode sensory feature comparison differently before making a decision.

Hippocampal Neuronal Activity Preceding Stimulus Predicts Later Memory Success.

Hippocampal neuronal activity at a time preceding stimulus onset affects episodic memory performance. We hypothesized that neuronal activity preceding an event supports successful memory formation; therefore, we explored whether a characterized encoding-associated brain activity, viz. the neuronal activity preceding a stimulus, predicts subsequent memory formation. To address this issue, we assessed the activity of single neurons recorded from the hippocampus in humans, while participants performed word memory tasks. Human hippocampal single-unit activity elicited by a fixation cue preceding words increased the firing rates (FRs) and predicted whether the words are recalled in a subsequent memory test; this indicated that successful memory formation in humans can be predicted by a preceding stimulus activity during encoding. However, the predictive effect of preceding stimulus activity did not occur during retrieval. These findings suggest that the preparative arrangement of brain activity before stimulus encoding improves subsequent memory performance.

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.

Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.

Propofol anesthesia-induced spatiotemporal changes in cortical activity with loss of external and internal awareness: An electrocorticography study.

OBJECTIVE: To understand the underlying mechanism of consciousness, investigating spatiotemporal changes in the cortical activity during the induction phase of unconsciousness is important. Loss of consciousness induced by general anesthesia is not necessarily accompanied by a uniform inhibition of all cortical activities. We hypothesized that cortical regions involved in internal awareness would be suppressed after disruption of cortical regions involved in external awareness. Thus, we investigated temporal changes in cortex during induction of unconsciousness. METHODS: We recorded electrocorticography data of 16 epilepsy patients and investigated power spectral changes during induction phase from awake state to unconsciousness. Temporal changes were assessed at 1) the start point and 2) the interval of normalized time between start and end of power change (Δ tnormalized). RESULTS: We found that the power increased at frequencies < 46 Hz, and decreased in range of 62-150 Hz, in global channels. In temporal changes of power change, superior parietal lobule and dorsolateral prefrontal cortex started to change early, but the changes were completed over a prolonged interval, whereas angular gyrus and associative visual cortex showed a delayed change and rapid completion. CONCLUSIONS: Loss of consciousness induced by general anesthesia results first from disrupted communication between self and external world, followed by disrupted communication within self, with decreased activities of superior parietal lobule and dorsolateral prefrontal cortex, and later, attenuated activities of angular gyrus. SIGNIFICANCE: Our findings provided neurophysiological evidence for the temporal changes in consciousness components induced by general anesthesia.

Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.

Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.

Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms.

Brain-computer interfaces (BCIs) can restore impaired cognitive functions in people with neurological disorders such as stroke. Musical ability is a cognitive function that is correlated with non-musical cognitive functions, and restoring it can enhance other cognitive functions. Pitch sense is the most relevant function to musical ability according to previous studies of amusia, and thus decoding pitch information is crucial for BCIs to be able to restore musical ability. This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4-B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (< 13 Hz) and high-frequency ( 13 Hz) bands, and frontal and parietal areas. We classified two EEG feature sets, IC and DC, into seven pitch classes using five types of classifiers. The best classification performance for seven pitches was obtained using IC and multiclass Support Vector Machine with an average accuracy of 35.68±7.47% (max. 50%) and an information transfer rate (ITR) of 0.37±0.22 bits/sec. When grouping the pitches to vary the number of classes (K = 2-6), the ITR was similar across K and feature sets, suggesting the efficiency of DC. This study demonstrates for the first time the feasibility of decoding imagined musical pitch directly from human EEG.

Modulation of somatosensory evoked magnetic fields by intensity of interfering stimuli in human somatosensory cortex: an MEG study.

Somatosensory evoked responses are known to be modulated by previous interfering stimuli. Here, we first investigated the modulatory effects of interfering stimuli with different intensities on somatosensory evoked magnetic field in human primary (S1) and secondary (S2) somatosensory cortices. In the control condition of the study, test stimulus, set to strong intensity, was delivered to the left median nerve. Interfering stimuli with three different levels of intensity from weak (WI) through moderate (MI) and finally to strong (SI) were interspersed to the left median nerve between the test stimuli in each interfering condition. The cortical responses to the test stimulus were modeled with equivalent current dipoles in the contralateral S1 and bilateral S2 cortices from 17 subjects. The amplitude of the N20m deflection from the S1 was not changed by any interfering stimuli, whereas the amplitude of later P35m deflection was reduced by MI stimulus. The amplitude of P60m deflection was reduced by MI and SI stimuli. The extent of amplitude reduction of the bilateral S2 response was markedly increased as intensity of interfering stimuli increased from weak to moderate, but further reduction by the SI stimuli compared to MI stimuli was not observed. Those results indicated that somatosensory cortical activation in the S1 (P35m and P60m) and S2 were modulated by intensity of interfering stimuli. Our findings of a greater gating effect on the bilateral S2 compared to the contralateral S1 indicate that S2 may play an important role in temporal integration of different intensity levels of somatosensory inputs.

Localization value of magnetoencephalography interictal spikes in adult nonlesional neocortical epilepsy.

Few studies have included magnetoencephalography (MEG) when assessing the diagnostic value of presurgical modalities in a nonlesional epilepsy population. Here, we compare single photon emission computed tomography (SPECT), positron emission tomography (PET), video-EEG (VEEG), and MEG, with intracranial EEG (iEEG) to determine the value of individual modalities to surgical decisions. We analyzed 23 adult epilepsy patients with no abnormal MRI findings who had undergone surgical resection. Localization of individual presurgical tests was determined for hemispheric and lobar locations based on visual analysis. Each localization result was compared with the ictal onset zone (IOZ) defined by using iEEG. The highest to the lowest hemispheric concordance rates were MEG (83%) > ictal VEEG (78%) > PET (70%) > ictal SPECT (57%). The highest to lowest lobar concordance rates were ictal VEEG = MEG (65%) > PET (57%) > ictal SPECT (52%). Statistical analysis showed MEG to have a higher hemispheric concordance than that of ictal SPECT (P = 0.031). We analyzed the effects of MEG clustered-area resection on surgical outcome. Patients who had resection of MEG clusters showed a better surgical outcome than those without such resection (P = 0.038). It is suggested that MEG-based localization had the highest concordance with the iEEG-defined IOZ. Furthermore, MEG cluster resection has prognostic significance in predicting surgical outcome.

Magnetoencephalography in pediatric lesional epilepsy surgery.

This study was performed to assess the usefulness of magnetoencephalography (MEG) as a presurgical evaluation modality in Korean pediatric patients with lesional localization-related epilepsy. The medical records and MEG findings of 13 pediatric patients (6 boys and 7 girls) with localization-related epilepsy, who underwent epilepsy surgery at Seoul National University Children's Hospital, were retrospectively reviewed. The hemispheric concordance rate was 100% (13/13 patients). The lobar or regional concordance rate was 77% (10/13 patients). In most cases, the MEG spike sources were clustered in the proximity of the lesion, either at one side of the margin (nine patients) or around the lesion (one patient); clustered spike sources were distant from the lesion in one patient. Among the patients with clustered spike sources near the lesion, further extensions (three patients) and distal scatters (three patients) were also observed. MEG spike sources were well lateralized and localized even in two patients without focal epileptiform discharges in the interictal scalp electroencephalography. Ten patients (77%) achieved Engel class I postsurgical seizure outcome. It is suggested that MEG is a safe and useful presurgical evaluation modality in pediatric patients with lesion localization-related epilepsy.

Parietal and medial temporal lobe interactions in working memory goal-directed behavior.

Working memory is essential for the organization of goal-directed behavior, which involves multiple brain networks. The frontoparietal network has been proposed as a central node for the maintenance and manipulation of information. However, the exact contribution of the frontal and parietal lobes is still unclear as is that of the medial temporal lobe (MTL). Here, we investigated how the frontoparietal network and the MTL coordinate cognitive functions to control working memory in 12 participants, including five men, with medically intractable epilepsy. Participants performed a modified Sternberg working memory task during intracranial electroencephalography recording. The present working memory task was designed to test the different neural states of working memory subprocesses during memory maintenance and operation. First, we observed increased and sustained low-frequency (2-7 Hz) power in the frontal lobe and MTL, relative to baseline activity during the entire working memory task. Parietal alpha (8-13 Hz) power exhibited peak activity during memory operation. Finally, we found a positive correlation in the alpha band between the MTL and the parietal lobe during memory operation. These results indicate that as task demands become specific and goal-directed, the correlation between the MTL and the parietal lobe increases. This finding provides novel insight into the contribution of the MTL-parietal lobe network to voluntary control of working memory.