Influence of White and Gray Matter Connections on Endogenous Human Cortical Oscillations.

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.

Multiple Mechanisms for Processing Reward Uncertainty in the Primate Basal Forebrain.

UNLABELLED: 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(-)). In contrast, the medial BF (mBF) contains reward-sensitive neurons, some of which are selectively enhanced (U(+)) by uncertain-reward predictions. In a two-alternative choice-task, U(-) neurons were selectively suppressed while monkeys chose uncertain options over certain options. During the same choice-epoch, U(+) neurons signaled the subjective reward value of the choice options. Additionally, after the choice was reported, U(+) 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. SIGNIFICANCE STATEMENT: To survive in an uncertain world, we must approach uncertainty and learn from it. Here we provide evidence for two mostly distinct mechanisms for processing uncertainty about rewards within different subregions of the primate basal forebrain (BF). We found that uncertainty suppressed the representation of certain (or safe) reward values by some neurons in the dorsal-lateral BF, in regions occupied by the ventral pallidum. This uncertainty-related suppression was evident as monkeys made risky choices. We also found that uncertainty-enhanced the activity of many medial BF neurons, most prominently after the monkeys' choices were completed (as they awaited uncertain outcomes). Based on these findings, we propose that different subregions of the BF could support action and learning under uncertainty in distinct but complimentary manners.

Fast, Continuous Audiogram Estimation Using Machine Learning.

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 was 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 one 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 with 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.

Spike-timing computation properties of a feed-forward neural network model.

Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g., serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape these transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity (STDP) rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS) in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

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

High-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss. In this study, we implanted Utah Slanted Electrode Arrays (USEAs) intrafascicularly at the elbow or shoulder in arm nerves of rhesus monkeys (n = 4) under isoflurane anesthesia. Input-output curves indicated that pulse-width-modulated single-electrode stimulation in each arm nerve could recruit single muscles with little or no recruitment of other muscles. Stimulus trains evoked specific, natural, hand movements, which could be combined via multielectrode stimulation to elicit coordinated power or pinch grasp. Stimulation also elicited short-latency evoked potentials (EPs) in primary somatosensory cortex, which might be used to provide sensory feedback from a prosthetic limb. These results demonstrate a high-resolution, high-channel-count interface to the peripheral nervous system for restoring hand function after neural injury or disruption or for examining nerve structure.

Modeling of topology-dependent neural network plasticity induced by activity-dependent electrical stimulation.

Activity-dependent electrical stimulation can induce cerebrocortical reorganization in vivo by activating brain areas using stimulation derived from the statistics of neural or muscular activity. Due to the nature of synaptic plasticity, network topology is likely to influence the effectiveness of this type of neuromodulation, yet its effect under different network topologies is unclear. To address this issue, we simulated small-scale three-neuron networks to explore topology-dependent network plasticity. The induced neuroplastic changes were evaluated by network coherence and unit-pair mutual information measures. We demonstrated that involvement of monosynaptic feedforward and reciprocal connections is more likely to lead to persistent decreased network coherence and increased network mutual information independent of the global network topology. On the contrary, disynaptic feedforward connections exhibit heterogeneous coherence and unit-pair mutual information sensitivity that depends strongly upon the network context.

Recording sensory and motor information from peripheral nerves with Utah Slanted Electrode Arrays.

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.