No abstract

Objectives Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study is to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique. Design The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning audiogram estimation and 1 repetition of conventional modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz). Results The two threshold estimate methods delivered very similar estimates at standard audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably to those of other threshold audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency. Conclusions The new machine learning audiogram estimation technique produces continuous threshold audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.

GA. Intrafascicular stimulation of monkey arm nerves evokes coordinated grasp and sensory responses.

The ability to use information about the uncertainty of future outcomes is critical for adaptive behavior in an uncertain world. We show that the basal forebrain (BF) contains at least two distinct neural-coding strategies to support this capacity. The dorsal-lateral BF, including the ventral pallidum (VP), contains reward-sensitive neurons, some of which are selectively suppressed by uncertain-reward predictions (U Ϫ ). In contrast, the medial BF (mBF) contains reward-sensitive neurons, some of which are selectively enhanced (U ϩ ) by uncertain-reward predictions. In a two-alternative choice-task, U Ϫ neurons were selectively suppressed while monkeys chose uncertain options over certain options. During the same choice-epoch, U ϩ neurons signaled the subjective reward value of the choice options. Additionally, after the choice was reported, U ϩ neurons signaled reward uncertainty until the choice outcome. We suggest that uncertainty-related suppression of VP may participate in the mediation of uncertainty-seeking actions, whereas uncertainty-related enhancement of the mBF may direct cognitive resources to monitor and learn from uncertain-outcomes.

Recording and stimulation via high-count penetrating microelectrode arrays implanted in peripheral nerves may help restore precise motor and sensory function after nervous system damage or disease. Although previous work has demonstrated safety and relatively successful stimulation for long-term implants of 100-electrode Utah Slanted Electrode Arrays (USEAs) in feline sciatic nerve [1], two major remaining challenges were 1) to maintain viable recordings of nerve action potentials long-term, and 2) to overcome contamination of unit recordings by myoelectric (EMG) activity in awake, moving animals. In conjunction with improvements to USEAs themselves, we have redesigned several aspects of our USEA containment and connector systems. Although further increases in unit yield and long-term stability remain desirable, here we report considerable progress toward meeting both of these goals: We have successfully recorded unit activity from USEAs implanted intrafascicularly in sciatic nerve for periods up to 4 months (the terminal experimental time point), and we have developed a containment system that effectively eliminates or substantially reduces EMG contamination of unit recordings in the moving animal. In addition, we used a 100-channel wireless recording integrated circuit attached to implanted USEAs to transmit broadband or spike-threshold data from nerve. Neural data thusly obtained during imposed limb movements were decoded blindly to drive a virtual prosthetic limb in real time. These results support the possibility of using USEAs in peripheral nerves to provide motor control and cutaneous or proprioceptive sensory feedback in individuals after limb loss or spinal cord injury.

The production of graceful skeletal movements requires coordinated activation of multiple muscles that produce torques around multiple joints. The work described herein is focused on one such movement, stance, that requires coordinated activation of extensor muscles acting around the hip, knee and ankle joints. The forces evoked in these muscles by external stimulation all have a complex dependence on muscle length and shortening velocities, and some of these muscles are bi-articular. In order to recreate sit-to-stand maneuvers in the anesthetized feline, we excited the hind limb musculature using intrafascicular multielectrode stimulation (IFMS) of the muscular branch of the sciatic nerve, the femoral nerve, and the main branch of the sciatic nerve. Stimulation was achieved with either acutely or chronically implanted Utah Slanted Electrode Arrays (USEAs) via subsets of electrodes 1) that activated motor units in the extensor muscles of the hip, knee, and ankle joints, 2) that were able to evoke large extension forces, and 3) that manifested minimal coactivation of the targeted motor units. Three hind limb force-generation strategies were investigated, including sequential activation of independent motor units to increase force, and interleaved or simultaneous IFMS of three sets of six or more USEA electrodes that excited the hip, knee, and ankle extensors. All force-generation strategies evoked stance, but the interleaved IFMS strategy also reduced muscle fatigue produced by repeated sit-to-stand maneuvers compared with fatigue produced by simultaneous activation of different motor neuron pools. These results demonstrate the use of interleaved IFMS as a means to recreate coordinated, fatigue-resistant multi-joint muscle forces in the unilateral hind limb. This muscle activation paradigm could provide a promising neuroprosthetic approach for the restoration of sit-to-stand transitions in individuals who are paralyzed by spinal cord injury, stroke, or disease.

Brain oscillations reflect changes in electrical potentials summated across neuronal populations. Low- and high-frequency rhythms have different modulation patterns. Slower rhythms are spatially broad, while faster rhythms are more local. From this observation, we hypothesized that low- and high-frequency oscillations reflect white- and gray-matter communications, respectively, and synchronization between low-frequency phase with high-frequency amplitude represents a mechanism enabling distributed brain-networks to coordinate local processing. Testing this common understanding, we selectively disrupted white or gray matter connections to human cortex while recording surface field potentials. Counter to our original hypotheses, we found that cortex consists of independent oscillatory-units (IOUs) that maintain their own complex endogenous rhythm structure. IOUs are differentially modulated by white and gray matter connections. White-matter connections maintain topographical anatomic heterogeneity (i.e., separable processing in cortical space) and gray-matter connections segregate cortical synchronization patterns (i.e., separable temporal processing through phase-power coupling). Modulation of distinct oscillatory modules enables the functional diversity necessary for complex processing in the human brain.

The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (< 100 pF) for tuning the coil to resonance, and a coil for power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.