Closed-Loop Interruption of Hippocampal Ripples through Fornix Stimulation in the Non-Human Primate

AbstractBackgroundHippocampal sharp-wave ripples (SWRs) arising from synchronous bursting in CA3 pyramidal cells and propagating to CA1 are thought to facilitate memory consolidation. Stimulation of the CA3 axon collaterals comprising the hippocampal commissure in rats interrupts sharp-wave ripples and leads to memory impairment. In primates, however, these commissural collaterals are limited. Other hippocampal fiber pathways, like the fornix, may be potential targets for modulating ongoing hippocampal activity, with the short latencies necessary to interrupt ripples.ObjectiveThe aim of this study is to determine the efficacy of closed-loop stimulation adjacent to the fornix for interrupting hippocampal ripples.MethodStimulating electrodes were implanted bilaterally alongside the fornix in the macaque, together with microelectrodes targeting the hippocampus for recording SWRs. We first verified that fornix stimulation reliably and selectively evoked a response in the hippocampus. We then implemented online detection and stimulation as hippocampal ripples occurred.ResultsThe closed-loop interruption method was effective in interrupting ripples as well as the associated hippocampal multi-unit activity, demonstrating the feasibility of ripple interruption using fornix stimulation in primates.ConclusionAnalogous to murine research, such an approach will likely be useful in understanding the role of SWRs in memory formation in macaques and other primates sharing these pathways, such as humans. More generally, closed-loop stimulation of the fornix may prove effective in interrogating hippocampal-dependent memory processes. Finally, this rapid, contingent-DBS approach may be a means for modifying pathological high-frequency events within the hippocampus, and potentially throughout the extended hippocampal circuit

Lateral hypothalamic activity indicates hunger and satiety states in humans

Lateral hypothalamic area (LHA) local field potentials (LFPs) were recorded in a Prader–Willi patient undergoing deep brain stimulation (DBS) for obesity. During hunger, exposure to food-related cues induced an increase in beta/ low-gamma activity. In contrast, recordings during satiety were marked by prominent alpha rhythms. Based on these findings, we have delivered alphafrequency DBS prior to and during food intake. Despite reporting an early sensation of fullness, the patient continued to crave food. This suggests that the pattern of activity in LHA may indicate hunger/satiety states in humans but attest to the complexity of conducting neuromodulation studies in obesity

Electrophysiological Recording of the Brain, Visualization, Prediction, and Interconnectivity

The human brain is a complex network of interconnected neurons. The aim of neuroscience and neuroengineering is to decode the neural activity, visualize it and try to better understand how neurons communicate with each other. This dissertation comprises four contributions to the area. These four topics are discussing how functional relationship between brain activity and movement can be found and whether common features found in different regions are correlated (phase-locked). First, the method of chirplet decomposition offers a new way to visualize the time-frequency content of non-stationary signals with higher resolution than previously possible. The use of Wigner-Ville distribution together with chirplet decomposition allows a clearer visualization in terms of both the temporal and frequency details with detail higher than previously achieved using other methods including Choi-Williams and spectrogram. Second, an improved method of averaging of neural signals over repeated trials is introduced whereby slight variations in the alignment of the neural signal over time is corrected through the use of nonlinear shifts. In earlier studies, time alignment has been performed using linear shift (e.g. alignment with movement onset), but this process alone is not sufficient when the signal timing changes differently over time. To overcome this issue, nonlinear transformations were found to remove any temporal variabilities in the way the task was performed. Third, a multilinear model is demonstrated showing how limb velocity in a reach task can be predicted from neuroelectrical activity. The model, after fitting, suggested that high frequency oscillations have sufficient information for both detection of movement onset and reconstruction of its movement. The use of a linear model reduces the overall computational requirements and simplifies the reconstruction of movement kinematics. Finally, a fourth method involving the measurement of coherence over time reveals how circuits in the basal ganglia communicate with the cortical layers during voluntary movements. This allows investigating the inter-coupling between the sensorimotor cortex and basal ganglia and how cortico-basal ganglia coupling changes with respect to the movement. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the cortical region of the brain is required for the execution of voluntary movements. The coherent activity suggests that similar information can be found in two brain regions.Ph.D.2016-05-18 00:00:0

1 Implementing a Speech Recognition System on a Graphics Processor Unit (GPU) using CUDA

A speech recognition system can be classified based on two factors: (1) whether the system is speaker-dependent or speaker-independent, and (2) whether the system works for continuous speech or isolated words. Ideally, it should be abl

Changes in spectral density of ECoG contact located over primary motor cortex derived from activity of Contact 1.

<p>Changes shown in dB with accompanying EMG responses. (A) Response of Participant 1 shows ERS in gamma band (center frequency 158 Hz) and slow oscillations (0–2 Hz) as well as ERD in alpha-beta band (10–30 Hz). Time required for participants to reach target and to return to initial position was approximately 2 seconds. Vertical black lines indicate frequency bands where ERS/ERD was reduced by 50% of its peak value (3 dB drop). (B) Results for Participant 2 and (C) Participant 3.</p

Data of Participant 3.

<p>(A) Gamma power (70–90 Hz) during 50 consecutive trials. EMG onsets are aligned at t = 0. Each trial contains both reaching and retrieval movements. (B) Time course of muscle activity and detected movement onsets. Four typical movement cycles are shown. The solid line shows power as calculated from biceps muscle activity (i.e. the square of the EMG signal) and red vertical dashed lines represent detected movement onsets.</p

Arm velocity predicted by the multiple linear regression model.

<p>Prediction (solid line) and actual velocity (dotted line) over two different trials of reaching right (x-axis component) for Participant 3.</p

Effect of bandwidth used to calculate the band power for Participant 3.

<p>Ratio between movement (0-1sec) and pre-movement (-1 to 0sec) gamma power plotted as a function of bandwidth. Both mean and MSE are shown (solid and dotted lines respectively). The ratio declines monotonically and approaches 1 indicating poor discrimination between movement and rest when using large bandwidths.</p

Example of recordings.

<p>A) Traces showing raw ECoG signals and EMG recorded during a reaching task for Participant 3. B) Arm velocity in three-dimensions during the task.</p