Summary:Purpose: The anterior nucleus of the thalamus (ANT) modulates temporal lobe and hypothalamic activities, and relays information to the cingulate gyrus and entorhinal cortex. Deep brain stimulation (DBS) of the ANT has been reported to decrease seizure activity in a limited number of human subjects. However, long-term effect of chronic ANT stimulation on such patients remains unknown. We report long-term follow-up results in four patients receiving ANT stimulation for intractable epilepsy.Methods: Four patients underwent stereotactic implantation of quadripolar stimulating electrodes in the bilateral ANT, guided by single-unit microelectrode recording. Electrode location was confirmed by postoperative magnetic resonance imaging (MRI). The stimulator was activated 2-4 weeks following electrode insertion; initial stimulation parameters were 4-5 V, 90-110 Hz, and 60-90 µs. Seizure frequency was monitored and compared with preimplantation baseline frequency. Intelligence quotient (IQ) test and auditory P300 response were performed before and after implantation of electrodes.Results: Four patients (one man with generalized seizures, and three women with partial seizures and secondary generalization) aged 18-45 years old were studied with mean followup period of 43.8 months. The four patients demonstrated a sustained effect of 49% (range, 35-76%) seizure reduction to ANT stimulation. Simple insertion of DBS electrodes (Sham period, no stimulation) produced a mean reduction in seizures of 67% (range, 44-94%). One patient was seizure-free for 15 months with anticonvulsant medications. One patient had a small frontal hemorrhage and a second patient had extension erosion over scalp; no resultant major or permanent neurological deficit was observed. Preoperative IQ index and auditory P300 were not significantly different with those after electrodes implantation.Conclusions: Implantation of electrodes in the ANT and subsequent stimulation is associated with a significant reduction in seizure frequency. However, our study could not differentiate whether the implantation itself, the subsequent stimulation or postimplantation drug manipulation had the greatest impact. These experimental results prompt further controlled study in a large patient population.

An emerging issue in neuroscience is how to identify baseline state(s) and accompanying networks termed "resting state networks" (RSNs). Although independent component analysis (ICA) in fMRI studies has elucidated synchronous spatiotemporal patterns during cognitive tasks, less is known about the changes in EEG functional connectivity between eyes closed (EC) and eyes open (EO) states, two traditionally used baseline indices. Here we investigated healthy subjects (n = 27) in EC and EO employing a four-step analytic approach to the EEG: (1) group ICA to extract independent components (ICs), (2) standardized low-resolution tomography analysis (sLORETA) for cortical source localization of IC network nodes, followed by (3) graph theory for functional connectivity estimation of epochwise IC band-power, and (4) circumscribing IC similarity measures via hierarchical cluster analysis and multidimensional scaling (MDS). Our proof-of-concept results on alpha-band power demonstrate five statistically clustered groups with frontal, central, parietal, occipitotemporal, and occipital sources. Importantly, during EO compared with EC, graph analyses revealed two salient functional networks with frontoparietal connectivity: a more medial network with nodes in the mPFC/precuneus which overlaps with the "default-mode network" (DMN), and a more lateralized network comprising the middle frontal gyrus and inferior parietal lobule, coinciding with the "dorsal attention network" (DAN). Furthermore, a separate MDS analysis of ICs supported the emergence of a pattern of increased proximity (shared information) between frontal and parietal clusters specifically for the EO state. We propose that the disclosed component groups and their source-derived EEG functional connectivity maps may be a valuable method for elucidating direct neuronal (electrophysiological) RSNs in healthy people and those suffering from brain disorders.

Capturing hand motions for hand function evaluations is essential in the medical field. Various data gloves have been developed for rehabilitation and manual dexterity assessments. This study proposed a modular data glove with 9-axis inertial measurement units (IMUs) to obtain static and dynamic parameters during hand function evaluation. A sensor fusion algorithm is used to calculate the range of motion of joints. The data glove is designed to have low cost, easy wearability, and high reliability. Owing to the modular design, the IMU board is independent and extensible and can be used with various microcontrollers to realize more medical applications. This design greatly enhances the stability and maintainability of the glove.