Longitudinal Differences in Human Hippocampal Connectivity During Episodic Memory Processing.

The question of longitudinal hippocampal functional specialization is critical to human episodic memory because an accurate understanding of this phenomenon would impact theories of mnemonic function and entail practical consequences for the clinical management of patients undergoing temporal lobe surgery. The implementation of the robotically assisted stereo electroencephalography technique for seizure mapping has provided our group with the opportunity to obtain recordings simultaneously from the anterior and posterior human hippocampus, allowing us to create an unparalleled data set of human subjects with simultaneous anterior and posterior hippocampal recordings along with several cortical regions. Using these data, we address several key questions governing functional hippocampal connectivity in human memory. First, we ask whether functional networks during episodic memory encoding and retrieval are significantly different for the anterior versus posterior hippocampus (PH). We also examine how connections differ across the 2-5 Hz versus 4-9 Hz theta frequency ranges, directly addressing the relative contribution of each of these separate bands in hippocampal-cortical interactions. While we report some overlapping connections, we observe evidence of distinct anterior versus posterior hippocampal networks during memory encoding related to frontal and parietal connectivity as well as hemispheric differences in aggregate connectivity. We frame these findings in light of the proposed AT/PM memory systems. We also observe distinct encoding versus retrieval connectivity patterns between anterior and posterior hippocampal networks, we find that overall connectivity is greater for the PH in the right hemisphere, and further that these networks significantly differ in terms of frontal and parietal connectivity. We place these findings in the context of existing theoretical treatments of human memory systems, especially the proposed AT/PM system. During memory retrieval, we observe significant differences between slow-theta (2-5 Hz) and fast-theta (4-9 Hz) connectivity between the cortex and hippocampus. Finally, we test how these distinct theta frequency oscillations propagate within the hippocampus, using phase slope index to estimate the direction slow-theta and fast-theta oscillations travel during encoding and retrieval. We uncover evidence that 2-5 Hz oscillations travel in the posterior-to-anterior direction, while 5-9 Hz oscillations travel from anterior-to-posterior. Taken together, our findings describe mnemonically relevant functional connectivity differences along the longitudinal axis of the human hippocampus that will inform interpretation of models of hippocampal function that seek to integrate rodent and human data.

Electroencephalography-based real-time cortical monitoring system that uses hierarchical Bayesian estimations for the brain-machine interface.

In this study, a real-time cortical activity monitoring system was constructed, which could estimate cortical activities every 125 milliseconds over 2,240 vertexes from 64 channel electroencephalography signals through the Hierarchical Bayesian estimation that uses functional magnetic resonance imaging data as its prior information. Recently, functional magnetic resonance imaging has mostly been used in the neurofeedback field because it allows for high spatial resolution. However, in functional magnetic resonance imaging, the time for the neurofeedback information to reach the patient is delayed several seconds because of its poor temporal resolution. Therefore, a number of problems need to be solved to effectively implement feedback training paradigms in patients. To address this issue, this study used a new cortical activity monitoring system that improved both spatial and temporal resolution by using both functional magnetic resonance imaging data and electroencephalography signals in conjunction with one another. This system is advantageous as it can improve applications in the fields of real-time diagnosis, neurofeedback, and the brain-machine interface.

Intentional signal in prefrontal cortex generalizes across different sensory modalities.

Biofeedback-EEG training to learn the mental control of an external device (e.g., a cursor on the screen) has been an important paradigm to attempt to understand the involvements of various areas of the brain in the volitional control and the modulation of intentional thought processes. Often the areas to adapt and to monitor progress are selected a priori. Less explored, however, has been the notion of automatically emerging activation in a particular area or subregions within that area recruited above and beyond the rest of the brain. Likewise, the notion of evoking such a signal as an amodal, abstract one remaining robust across different sensory modalities could afford some exploration. Here we develop a simple binary control task in the context of brain-computer interface (BCI) and use a Bayesian sparse probit classification algorithm to automatically uncover brain regional activity that maximizes task performance. We trained and tested 19 participants using the visual modality for instructions and feedback. Across training blocks we quantified coupling of the frontoparietal nodes and selective involvement of visual and auditory regions as a function of the real-time sensory feedback. The testing phase under both forms of sensory feedback revealed automatic recruitment of the prefrontal cortex with a parcellation of higher strength levels in Brodmann's areas 9, 10, and 11 significantly above those in other brain areas. We propose that the prefrontal signal may be a neural correlate of externally driven intended direction and discuss our results in the context of various aspects involved in the cognitive control of our thoughts.

Reconstructing for joint angles on the shoulder and elbow from non-invasive electroencephalographic signals through electromyography.

In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient (CC) of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles.

Electroencephalography (EEG)-based neurofeedback training for brain-computer interface (BCI).

Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain-computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent.

Control of a vehicle with EEG signals in real-time and system evaluation.

To construct and evaluate a novel wheelchair system that can be freely controlled via electroencephalogram signals in order to allow people paralyzed from the neck down to interact with society more freely. A brain-machine interface (BMI) wheelchair control system was constructed by effective signal processing methods, and subjects were trained by a feedback method to decrease the training time and improve accuracy. The implemented system was evaluated through experiments on controlling bars and avoiding obstacles using three subjects. Furthermore, the effectiveness of the feedback training method was evaluated by comparison with an imaginary movement experiment without any visual feedback for two additional subjects. In the bar-controlling experiment, two subjects achieved a 95.00% success rate, and the third had a 91.66% success rate. In the obstacle avoidance experiment, all three achieved success rate over 90% success rate, and required almost the same amount of time to reach as that when driving with a joystick. In the experiment on imaginary movement without visual feedback, the two additional subjects adapted to the experiment far slower than they did with visual feedback. In this study, the feedback training method allowed subjects to easily and rapidly gain accurate control over the implemented wheelchair system. These results show the importance of the feedback training method using neuroplasticity in BMI systems.

Detecting the number of clusters in n-way probabilistic clustering.

Recently, there has been a growing interest in multiway probabilistic clustering. Some efficient algorithms have been developed for this problem. However, not much attention has been paid on how to detect the number of clusters for the general n-way clustering (n ≥ 2). To fill this gap, this problem is investigated based on n-way algebraic theory in this paper. A simple, yet efficient, detection method is proposed by eigenvalue decomposition (EVD), which is easy to implement. We justify this method. In addition, its effectiveness is demonstrated by the experiments on both simulated and real-world data sets.

Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture.

In our previous study [Koike, Y., Hirose, H., Sakurai, Y., Iijima T., (2006). Prediction of arm trajectory from a small number of neuron activities in the primary motor cortex. Neuroscience Research, 55, 146-153], we succeeded in reconstructing muscle activities from the offline combination of single neuron activities recorded in a serial manner in the primary motor cortex of a monkey and in reconstructing the joint angles from the reconstructed muscle activities during a movement condition using an artificial neural network. However, the joint angles during a static condition were not reconstructed. The difficulties of reconstruction under both static and movement conditions mainly arise due to muscle properties such as the velocity-tension relationship and the length-tension relationship. In this study, in order to overcome the limitations due to these muscle properties, we divided an artificial neural network into two networks: one for movement control and the other for posture control. We also trained the gating network to switch between the two neural networks. As a result, the gating network switched the modules properly, and the accuracy of the estimated angles improved compared to the case of using only one artificial neural network.

Prediction of four degrees of freedom arm movement using EMG signal.

In this paper, we predicted four degrees of freedom movement of a monkey's arm by using a neural network model based on the EMG signal. Through the monkey's reaching task, we measured the electromyograms(EMG) signal from the seven muscles of the arm and simultaneously three dimensional movement trajectory of it. The neural network model used in this study is composed of three layers: the input layer with seven values from the EMG signal of the seven muscles, the middle layer consisted of ten and the output layer of four outputs. The movement predicted by this model was almost the same as the real movement. Besides we could implement no delay interface using the EMG signal that is a fundamental signal from the brain that makes it possible to induce the body's movement. Moreover we can predict not only the external movement of the monkey's arm but also the force of it, which is impossible to be sensed by the external movement sensing devices.