Habituation of Brain Activity with Repetition in Color and Picture-Word Stroop Tests

As a widely used mental task for functional near-infrared spectroscopy (fNIRS), the original color-word Stroop task has the advantage of being difficult to habituate, but also the disadvantage of being difficult to understand, especially for children. While the introduction of derived Stroop tasks offers highly promising countermeasures, changes in brain activity during these tests have not been well tested. We investigated the degree of habituation between the original and a derived Stroop task by measuring brain activity to obtain a better fNIRS task design. Fourteen healthy adults participated in the study, and a 10-channel fNIRS device was used. A picture-word Stroop task with lower linguistic conflict than the original was conducted. The original and derived Stroop tests were repeated four times in a 1-week interval. We found that the original Stroop test did not show any significant changes in brain activity with repeated measures; however, brain activity decreased during the derived test. The differences in habituation between the original and derived tests may be due to the differences in the strength of the linguistic conflict. Our findings also highlight the need to consider the effects of habituation when using derived Stroop tasks in repeated measures.

Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review

In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback.

Modeling the interaction between donor-derived regulatory T cells and effector T cells early after allogeneic hematopoietic stem cell transplantation

While allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potential curative therapy against hematological malignancies, modulation of donor T cell alloreactivity is required to enhance the graft-versus-leukemia (GVL) effect and control graft-versus-host-disease (GVHD) after allo-HSCT. Donor-derived regulatory CD4⁺CD25⁺Foxp3⁺ T cells (Tregs) play a central role in establishing of immune tolerance after allo-HSCT. They could be a key target to be modulated for increasing the GVL effect and control of GVHD. We constructed an ordinary differential equation model incorporating bidirectional interactions between Tregs and effector CD4⁺ T cells (Teffs) as a mechanism for control of Treg cell concentration. The goal is to elucidate how the interaction between Tregs and Teffs is modulated in order to get insights into fine tuning of alloreactivity after allo-HSCT. The model was calibrated with respect to published Treg and Teff recovery data after allo-HSCT. The calibrated model exhibits perfect or near-perfect adaptation to stepwise perturbations between Treg and Teff interactions, as seen in Treg cell populations when patients with relapsed malignancy were treated with anti-CTLA-4 (cytotoxic T lymphocyte-associated antigen 4). In addition, the model predicts observed shifts of Tregs and Teffs concentrations after co-stimulatory receptor IL-2R or TNFR2 blockade with allo-HSCT. The present results suggest simultaneous blockades of co-stimulatory and co-inhibitory receptors as a potential treatment for enhancing the GVL effect after allo-HSCT without developing GVHD.

Cell types and synchronous-activity patterns of inspiratory neurons in the preBötzinger complex of mouse medullary slices during early postnatal development

To examine whether and how the inspiratory neuronal network in the preBötzinger complex (preBötC) develops during the early postnatal period, we quantified the composition of the population of inspiratory neurons between postnatal day 1 (p1) and p10 by applying calcium imaging to medullary transverse slices in double-transgenic mice expressing fluorescent marker proteins. We found that putative excitatory and glycinergic neurons formed a majority of the population of inspiratory neurons, and the composition rates of these two inspiratory neurons inverted at p5-6. We also found that the activity patterns of these two types of inspiratory neurons became significantly well-synchronized with the inspiratory rhythmic bursting pattern in the preBötC within the first postnatal week. GABAergic and GABA-glycine cotransmitting inspiratory neurons formed only a small population just after birth, which almost disappeared until p10. In conclusion, the inspiratory neuronal network in the preBötC matures at the level of both neuronal population and neuronal activities during early postnatal development.

A computational model to explore how temporal stimulation patterns affect synapse plasticity

Plasticity-related proteins (PRPs), which are synthesized in a synapse activation-dependent manner, are shared by multiple synapses to a limited spatial extent for a specific period. In addition, stimulated synapses can utilize shared PRPs through synaptic tagging and capture (STC). In particular, the phenomenon by which short-lived early long-term potentiation is transformed into long-lived late long-term potentiation using shared PRPs is called “late-associativity,” which is the underlying principle of “cluster plasticity.” We hypothesized that the competitive capture of PRPs by multiple synapses modulates late-associativity and affects the fate of each synapse in terms of whether it is integrated into a synapse cluster. We tested our hypothesis by developing a computational model to simulate STC, late-associativity, and the competitive capture of PRPs. The experimental results obtained using the model revealed that the number of competing synapses, timing of stimulation to each synapse, and basal PRP level in the dendritic compartment altered the effective temporal window of STC and influenced the conditions under which late-associativity occurs. Furthermore, it is suggested that the competitive capture of PRPs results in the selection of synapses to be integrated into a synapse cluster via late-associativity.

Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2- or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST - mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

Cell Type-Dependent Activation Sequence During Rhythmic Bursting in the PreBötzinger Complex in Respiratory Rhythmic Slices From Mice

Spontaneous respiratory rhythmic burst activity can be preserved in the preBötzinger Complex (preBötC) of rodent medullary transverse slices. It is known, that the activation sequence of inspiratory neurons in the preBötC stochastically varies from cycle to cycle. To test whether the activation timing of an inspiratory neuron depends on its neurotransmitter, we performed calcium imaging of preBötC neurons using double-transgenic mice expressing EGFP in GlyT2⁺ neurons and tdTomato in GAD65⁺ neurons. Five types of inspiratory neurons were identified using the fluorescence protein expression and the maximum cross-correlation coefficient between neuronal calcium fluctuation and field potential. Regarding the activation sequence, irregular type putative excitatory (GlyT2^-/GAD65^-) neurons and irregular type glycinergic (GlyT2⁺/GAD65^-) neurons tended to be activated early, while regular type putative excitatory neurons, regular type glycinergic neurons tended to be activated later. In conclusion, the different cell types define a general framework for the stochastically changing activation sequence of inspiratory neurons in the preBötC.

Night duty and decreased brain activity of medical residents: a wearable optical topography study

BACKGROUND

Overwork, fatigue, and sleep deprivation due to night duty are likely to be detrimental to the performance of medical residents and can consequently affect patient safety.

OBJECTIVE

The aim of this study was to determine the possibility of deterioration of cerebral function of sleep-deprived, fatigued residents using neuroimaging techniques.

DESIGN

Six medical residents were instructed to draw blood from artificial vessels installed on the arm of a normal cooperator. Blood was drawn at a similar time of the day, before and after night duty. To assess sleep conditions during night duty, the participants wore actigraphy units throughout the period of night duty. Changes in cerebral hemodynamics, during the course of drawing blood, were measured using a wearable optical topography system.

RESULTS

The visual analogue scale scores after night duty correlated negatively with sleep efficiency during the night duty (ρ = -0.812, p = 0.050). The right prefrontal cortex activity was significantly decreased in the second trial after night duty compared with the first (p = 0.028). The extent of [oxy-Hb] decrease, indicating decreased activity, in the right dorsolateral prefrontal cortex correlated negatively with the Epworth sleepiness score after night duty (ρ = -0.841, p = 0.036).

CONCLUSIONS

Sleep deprivation and fatigue after night duty, caused a decrease in the activity of the right dorsolateral prefrontal cortex of the residents, even with a relatively easy routine. This result implies that the brain activity of medical residents exposed to stress on night duty, although not substantially sleep-deprived, was impaired after the night duty, even though they apparently performed a simple medical technique appropriately. Reconsideration of the shift assignments of medical residents is strongly advised.

ABBREVIATIONS

DLPFC: Dorsolateral prefrontal cortex; ESS: Epworth sleepiness scale; PSQI: Pittsburgh sleep quality index; ROI: Regions of interest; VAS: Visual analogue scale; WOT: Wearable optical topography.

Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico

We hypothesize that the network topology within the pre-Bötzinger Complex (preBötC), the mammalian respiratory rhythm generating kernel, is not random, but is optimized in the course of ontogeny/phylogeny so that the network produces respiratory rhythm efficiently and robustly. In the present study, we attempted to identify topology of synaptic connections among constituent neurons of the preBötC based on this hypothesis. To do this, we first developed an effective evolutionary algorithm for optimizing network topology of a neuronal network to exhibit a ‘desired characteristic’. Using this evolutionary algorithm, we iteratively evolved an in silico preBötC ‘model’ network with initial random connectivity to a network exhibiting optimized synchronous population bursts. The evolved ‘idealized’ network was then analyzed to gain insight into: (1) optimal network connectivity among different kinds of neurons—excitatory as well as inhibitory pacemakers, non-pacemakers and tonic neurons—within the preBötC, and (2) possible functional roles of inhibitory neurons within the preBötC in rhythm generation. Obtained results indicate that (1) synaptic distribution within excitatory subnetwork of the evolved model network illustrates skewed/heavy-tailed degree distribution, and (2) inhibitory subnetwork influences excitatory subnetwork primarily through non-tonic pacemaker inhibitory neurons. Further, since small-world (SW) network is generally associated with network synchronization phenomena and is suggested as a possible network structure within the preBötC, we compared the performance of SW network with that of the evolved model network. Results show that evolved network is better than SW network at exhibiting synchronous bursts.

Respiratory calcium fluctuations in low-frequency oscillating astrocytes in the pre-Bötzinger complex

Astrocytes have been found to modulate neuronal activity through calcium-dependent signaling in various brain regions. However, whether astrocytes of the pre-Bötzinger complex (preBötC) exhibit respiratory rhythmic fluctuations is still controversial. Here we evaluated calcium-imaging experiments within preBötC in rhythmically active medullary slices from TgN(hGFAP-EGFP) mice using advanced analyses. 13.8% of EGFP-negative cells, putative neurons, showed rhythmic fluorescent changes that were highly correlated to the respiratory rhythmic fluctuation (cross-correlation coefficient>0.5 and dF/F>0.2%). In contrast, a considerable number of astrocyte somata exhibited synchronized low-frequency (<0.03Hz) calcium oscillations. After band-pass filtering, signals that irregularly preceded the calcium signal of EGFP-negative cells were observed in 10.2% of astrocytes, indicating a functional coupling between astrocytes and neurons in preBötC. A model simulation confirmed that such preinspiratory astrocytic signals can arise from coupled neuronal and astrocytic oscillators, supporting a concept that slow oscillatory changes of astrocytic functions modulate neighboring neuronal activity to add variability in respiratory rhythm.

Pixel timing correction in time-lapsed calcium imaging using point scanning microscopy

In point scanning imaging, data are acquired by sequentially scanning each pixel of a predetermined area. This way of scanning leads to time delays between pixels, especially for lower scanning speed or large scanned areas. Therefore, experiments are often performed at lower framerates in order to ensure a sufficient signal-to-noise ratio, even though framerates above 30 frames per second are technically feasible. For these framerates, we suggest that it becomes crucial to correct the time delay between image pixels prior to analyses. In this paper, we apply temporal interpolation (or pixel timing correction) for calcium imaging in two-photon microscopy as an example of fluorescence imaging. We present and compare three interpolation methods (linear, Lanczos and cubic B-spline). We test these methods on a simulated network of coupled bursting neurons at different framerates. In this network, we introduce a time delay to simulate a scanning by point scanning microscopy. We also assess these methods on actual microscopic calcium imaging movies recorded at usual framerates. Our numerical results suggest that point scanning microscopy imaging introduces statistically significant time delays between image pixels at low frequency. However, we demonstrate that pixel timing correction compensates for these time delays, regardless of the used interpolation method.

Relation between parametric change of the workload and prefrontal cortex activity during a modified version of the 'rock, paper, scissors' task

BACKGROUND/AIMS

Modified rock, paper, scissors (RPS) tasks have previously been used in neuroscience to investigate activity of the prefrontal cortex (PFC). In this study, we investigated hemodynamic changes in the PFC using near-infrared spectroscopy (NIRS) during a modified RPS task in which each subject's successful performance rate was equalized; the workload was increased parametrically in order to reveal the resulting pattern of PFC activation.

METHODS

The subjects were 20 healthy adults. During RPS, the player uses hand gestures to represent rock, paper, and scissors. Rock beats scissors, paper beats rock, and scissors beats paper. In the modified RPS task, the player is instructed to lose intentionally against the computer hand; the computer goes first and the player follows. The interstimulus interval (ISI) level was adjusted with 11 steps. If the level rose, the ISI decreased and the workload increased parametrically. The maximal level (maxLv: the shortest ISI and the biggest workload) in which a subject could perform the task correctly was determined for every subject during rehearsal of the task prior to the experiment. Lowering the level from the maxLv made the task easier. Hemodynamic changes were measured by NIRS over 4 task levels (maxLv-3, maxLv-2, maxLv-1 and maxLv).

RESULTS

The hemodynamic changes in the left lateral PFC and bilateral Brodmann area 6 rose significantly with the increase in workload and presented a linear trend.

CONCLUSION

These results suggest that PFC activation may linearly increase with increased workload during a modified RPS task in which successful performance rates of subjects are equalized.

Basic Study for New Assistive Technology Based on Brain Activity During Car Driving

The purpose of this research is develop assistive robots and apparatuses. There is a pressing need to develop new systems that assist and act for car driving and wheelchairs for the elderly as the population ages. In developing systems, it is thought to be important to examine behaviors spatial recognition. Experiments have therefore been performed to examine human spatial perceptions, especially left- and rightside visual recognition, while cars being driven using near-infrared spectroscopy (NIRS). Previous research found significant differences in the dorsolateral prefrontal cortex in the left cranial hemisphere during virtual driving and actual driving tasks. This paper discusses the measurement of brain activity during car driving. A detailed analysis was performed by segmentalizing brain activity during driving based on the motion of subjects, and we report on the relationship between brain activity and movement perception during driving.

Standardization of size, shape and internal structure of spinal cord images: comparison of three transformation methods

Functional fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular dynamics in the brain and spinal cord. However, it is difficult to integrate spatial information obtained from imaging data in specific regions of interest across multiple samples, due to large variability in the size, shape and internal structure of samples. To solve this problem, we attempted to standardize transversely sectioned spinal cord images focusing on the laminar structure in the gray matter. We employed three standardization methods, the affine transformation (AT), the angle-dependent transformation (ADT) and the combination of these two methods (AT+ADT). The ADT is a novel non-linear transformation method developed in this study to adjust an individual image onto the template image in the polar coordinate system. We next compared the accuracy of these three standardization methods. We evaluated two indices, i.e., the spatial distribution of pixels that are not categorized to any layer and the error ratio by the leave-one-out cross validation method. In this study, we used neuron-specific marker (NeuN)-stained histological images of transversely sectioned cervical spinal cord slices (21 images obtained from 4 rats) to create the standard atlas and also to serve for benchmark tests. We found that the AT+ADT outperformed other two methods, though the accuracy of each method varied depending on the layer. This novel image standardization technique would be applicable to optical recording such as voltage-sensitive dye imaging, and will enable statistical evaluations of neural activation across multiple samples.

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.

Spatio-temporal correlations from fMRI time series based on the NN-ARx model

For the purpose of statistical characterization of the spatio-temporal correlation structure of brain functioning from high-dimensional fMRI time series, we introduce an innovation approach. This is based on whitening the data by the Nearest-Neighbors AutoRegressive model with external inputs (NN-ARx). Correlations between the resulting innovations are an extension of the usual correlations, in which mean-correction is carried out by the dynamic NN-ARx model instead of the static, standard linear model for fMRI time series. Measures of dependencies between regions are defined by summarizing correlations among innovations at several time lags over pairs of voxels. Such summarization does not involve averaging the data over each region, which prevents loss of information in case of non-homogeneous regions. Statistical tests based on these measures are elaborated, which allow for assessing the correlation structure in search of connectivity. Results of application of the NN-ARx approach to fMRI data recorded in visual stimuli experiments are shown. Finally, a number of issues related with its potential and limitations are commented.

Dual oscillator model of the respiratory neuronal network generating quantal slowing of respiratory rhythm

We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382–397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na⁺ current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821–826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000–18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC.

Estimate of causality between independent cortical spatial patterns during movement volition in spinal cord injured patients

Static hemodynamic or neuroelectric images of brain regions activated during particular tasks do not convey the information of how these regions communicate to each other. Cortical connectivity estimation aims at describing these interactions as connectivity patterns which hold the direction and strength of the information flow between cortical areas. In this study, we attempted to estimate the causality between distributed cortical systems during a movement volition task in preparation for execution of simple movements by a group of normal healthy subjects and by a group of Spinal Cord Injured (SCI) patients. To estimate the causality between the spatial distributed patterns of cortical activity in the frequency domain, we applied a series of processing steps on the recorded EEG data. From the high-resolution EEG recordings we estimated the cortical waveforms for the regions of interest (ROIs), each representing a selected sensor group population. The solutions of the linear inverse problem returned a series of cortical waveforms for each ROI considered and for each trial analyzed. For each subject, the cortical waveforms were then subjected to Independent Component Analysis (ICA) pre-processing. The independent components obtained by the application of the ThinICA algorithm were further processed by a Partial Directed Coherence algorithm, in order to extract the causality between spatial cortical patterns of the estimated data. The source-target cortical dependencies found in the group of normal subjects were relatively similar in all frequency bands analyzed. For the normal subjects we observed a common source pattern in an ensemble of cortical areas including the right parietal and right lip primary motor areas and bilaterally the primary foot and posterior SMA areas. The target of this cortical network, in the Granger-sense of causality, was shown to be a smaller network composed mostly by the primary foot motor areas and the posterior SMA bilaterally. In the case of the SCI population, both the source and the target cortical patterns had larger sizes than in the normal population. The source cortical areas included always the primary foot and lip motor areas, often bilaterally. In addition, the right parietal area and the bilateral premotor area 6 were also involved. Again, the patterns remained substantially stable across the different frequency bands analyzed. The target cortical patterns observed in the SCI population had larger extensions when compared to the normal ones, since in most cases they involved the bilateral activation of the primary foot movement areas as well as the SMA, the primary lip areas and the parietal cortical areas.

Removal of ocular artifacts for high resolution EEG studies: a simulation study

Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance of the removal of EOG artifact due to the application of the independent component analysis (ICA) methodology. The precise quantification of the effects of artifact removal by ICA is possible by using a simulation setup, with a realistic head model, that it is able to mimic the occurrence of an eye blink. The electrical activity generated by the simulated eyeblink were propagated through the realistic head model and superimposed to a clean segment of EEG. Then, artifact removal was performed by using the ICA approach. Ocular artifact removal was evaluated in different operative conditions, characterized by different signal to noise ratio and number of electrodes. The error measures used were the relative error and the correlation coefficient between the clear, original EEG segment and those obtained after the application of the ICA procedure.

High frequency activities in the human orbitofrontal cortex in sleep–wake cycle

We recorded human orbitofrontal electrocorticogram during wakefulness and sleep in epileptic patients using subdural electrodes. During wakefulness and rapid eye movement (REM) sleep, we observed beta activity in the raw orbitofrontal signals. Power spectral analysis demonstrated beta enhancement during wakefulness and REM sleep when compared to slow wave sleep (SWS). During the phasic REM periods, the beta power was significantly lower than during the tonic REM periods. Gamma enhancement manifested itself in four out of six subjects during the phasic periods. This study is the first that has focused on electrical activity in the human orbitofrontal cortex. Although the role of the orbitofrontal cortex during sleep still remains unclear, high frequency activities give us important suggestions in elucidating the human sleep mechanism.

Theta oscillation in the human anterior cingulate cortex during all-night sleep: an electrocorticographic study

Ten epileptic patients each with subdural electrodes surgically attached to the anterior cingulate cortex (ACC; two cases), the orbitofrontal cortex (OFC; seven cases), or both (one case) were included in this study. We recorded each patient's ACC or OFC electrocorticogram (ECoG) during the time period that the patient was awake and naturally asleep. We performed a Fast Fourier Transformation (FFT) power spectral analysis on each ECoG to examine its frequency component. We found that the ACC showed regular and continuous theta oscillation (5-7Hz) during wakefulness and rapid eye movement (REM) sleep, but not during slow wave sleep. Theta waves observed in REM sleep were not as distinct as those found in wakefulness. We also discovered that the orbitofrontal signals represented spectral peaks in the theta band only during wakefulness. This suggests the coexistence of theta oscillation in the ACC. Considering our previous observations of gamma and beta oscillations in the human hippocampus, we hypothesize that the human limbic system manifests two oscillatory activities. The results obtained in this study suggest that electrophysiological activity in the ACC could be related to particular psychological functions in wakefulness and in REM sleep. These results are useful in elucidating the human brain mechanism.

Concurrent EEG/fMRI analysis by multiway Partial Least Squares

Data may now be recorded concurrently from EEG and functional MRI, using the Simultaneous Imaging for Tomographic Electrophysiology (SITE) method. As yet, there is no established means to integrate the analysis of the combined data set. Recognizing that the hemodynamically convolved time-varying EEG spectrum, S, is intrinsically multidimensional in space, frequency, and time motivated us to use multiway Partial Least-Squares (N-PLS) analysis to decompose EEG (independent variable) and fMRI (dependent variable) data uniquely as a sum of "atoms". Each EEG atom is the outer product of spatial, spectral, and temporal signatures and each fMRI atom the product of spatial and temporal signatures. The decomposition was constrained to maximize the covariance between corresponding temporal signatures of the EEG and fMRI. On all data sets, three components whose spectral peaks were in the theta, alpha, and gamma bands appeared; only the alpha atom had a significant temporal correlation with the fMRI signal. The spatial distribution of the alpha-band atom included parieto-occipital cortex, thalamus, and insula, and corresponded closely to that reported by Goldman et al. [NeuroReport 13(18) (2002) 2487] using a more conventional analysis. The source reconstruction from EEG spatial signature showed only the parieto-occipital sources. We interpret these results to indicate that some electrical sources may be intrinsically invisible to scalp EEG, yet may be revealed through conjoint analysis of EEG and fMRI data. These results may also expose brain regions that participate in the control of brain rhythms but may not themselves be generators. As of yet, no single neuroimaging method offers the optimal combination of spatial and temporal resolution; fusing fMRI and EEG meaningfully extends the spatio-temporal resolution and sensitivity of each method.

Impulse response function based on multivariate AR model can differentiate focal hemisphere in temporal lobe epilepsy

The purpose of this study is to propose and investigate a new approach for discriminating between focal and non-focal hemispheres in intractable temporal lobe epilepsy, based on applying multivariate time series analysis to the discharge-free background brain activity observed in nocturnal electrocorticogram (ECoG) time series. Five unilateral focal patients and one bilateral focal patient were studied. In order to detect the location of epileptic foci, linear multivariate autoregressive (MAR) models were fitted to the ECoG data; as a new approach for the purpose of summarizing these models in a single relevant parameter, the behavior of the corresponding impulse response functions was studied and described by an attenuation coefficient. In the majority of unilateral focal patients, the averaged attenuation coefficient was found to be almost always significantly larger in the focal hemisphere, as compared to the non-focal hemisphere. Also the amplitude of the fluctuations of the attenuation coefficient was significantly larger in the focal hemisphere. Moreover, in one patient showing a typical regular sleep cycle, the attenuation coefficient in the focal hemisphere tended to be larger during REM sleep and smaller during Non-REM sleep. In the bilateral focal patient, no statistically significant distinction between the hemispheres was found. This study provides encouraging results for new investigations of brain dynamics by multivariate parametric modeling. It opens up the possibility of relating diseases like epilepsy to the properties of inconspicuous background brain dynamics, without the need to record and analyze epileptic seizures or other evidently pathological waveforms.

Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

The relationship between the visually evoked P300 event-related potential and gamma band oscillation in the human medial and basal temporal lobes: an electrocorticographic study

We have recorded electrocorticographic activities (ECoG) from subdural electrodes on the human medial temporal lobe (MTL) and basal temporal lobe (BTL) in epileptic patients during cognitive visual tasks designed to evoke the P300 event related potential (ERP). From those recordings we examined the event related gamma band oscillation (ERGBO) and P300 ERP. While P300 was predominantly observed in the MTL, ERGBO was observed in both MTL and BTL. Resembling to P300, ERGBO responses were more often observed following rare stimuli than frequent stimuli. In average responses the ERGBO to rare stimuli followed P300, beginning at 440.5 ms and continuing for about 100 ms. Past studies suggest P300 ERP component reflects a role in cognitive function. Since ERGBO in the present study appeared in different regions and at a different latency from P300, ERGBO may reflect a different physiological role in the cognitive process.

A comparison of non-linear non-parametric models for epilepsy data

EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.