Low-frequency stimulation enhances ensemble co-firing and dexterity after stroke.

Electrical stimulation is a promising tool for modulating brain networks. However, it is unclear how stimulation interacts with neural patterns underlying behavior. Specifically, how might external stimulation that is not sensitive to the state of ongoing neural dynamics reliably augment neural processing and improve function? Here, we tested how low-frequency epidural alternating current stimulation (ACS) in non-human primates recovering from stroke interacted with task-related activity in perilesional cortex and affected grasping. We found that ACS increased co-firing within task-related ensembles and improved dexterity. Using a neural network model, we found that simulated ACS drove ensemble co-firing and enhanced propagation of neural activity through parts of the network with impaired connectivity, suggesting a mechanism to link increased co-firing to enhanced dexterity. Together, our results demonstrate that ACS restores neural processing in impaired networks and improves dexterity following stroke. More broadly, these results demonstrate approaches to optimize stimulation to target neural dynamics.

Modularity and robustness of frontal cortical networks.

Neural activity underlying short-term memory is maintained by interconnected networks of brain regions. It remains unknown how brain regions interact to maintain persistent activity while exhibiting robustness to corrupt information in parts of the network. We simultaneously measured activity in large neuronal populations across mouse frontal hemispheres to probe interactions between brain regions. Activity across hemispheres was coordinated to maintain coherent short-term memory. Across mice, we uncovered individual variability in the organization of frontal cortical networks. A modular organization was required for the robustness of persistent activity to perturbations: each hemisphere retained persistent activity during perturbations of the other hemisphere, thus preventing local perturbations from spreading. A dynamic gating mechanism allowed hemispheres to coordinate coherent information while gating out corrupt information. Our results show that robust short-term memory is mediated by redundant modular representations across brain regions. Redundant modular representations naturally emerge in neural network models that learned robust dynamics.

Dynamic fading memory and expectancy effects in the monkey primary visual cortex.

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

Neural heterogeneity controls computations in spiking neural networks.

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain.

Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.

The Neural Dynamics of Face Ensemble and Central Face Processing.

Extensive work has investigated the neural processing of single faces, including the role of shape and surface properties. However, much less is known about the neural basis of face ensemble perception (e.g., simultaneously viewing several faces in a crowd). Importantly, the contribution of shape and surface properties have not been elucidated in face ensemble processing. Furthermore, how single central faces are processed within the context of an ensemble remains unclear. Here, we probe the neural dynamics of ensemble representation using pattern analyses as applied to electrophysiology data in healthy adults (seven males, nine females). Our investigation relies on a unique set of stimuli, depicting different facial identities, which vary parametrically and independently along their shape and surface properties. These stimuli were organized into ensemble displays consisting of six surround faces arranged in a circle around one central face. Overall, our results indicate that both shape and surface properties play a significant role in face ensemble encoding, with the latter demonstrating a more pronounced contribution. Importantly, we find that the neural processing of the center face precedes that of the surround faces in an ensemble. Further, the temporal profile of center face decoding is similar to that of single faces, while those of single faces and face ensembles diverge extensively from each other. Thus, our work capitalizes on a new center-surround paradigm to elucidate the neural dynamics of ensemble processing and the information that underpins it. Critically, our results serve to bridge the study of single and ensemble face perception.

Aberrant effort-based reward dynamics in anhedonia.

Anhedonia is a transdiagnostic symptom and associated with a spectrum of reward deficits among which the motivational dysfunction is poorly understood. Previous studies have established the abnormal cost-benefit trade-off as a contributor to motivational deficits in anhedonia and its relevant psychiatric diseases. However, it remains elusive how the anhedonic neural dynamics underlying reward processing are modulated by effort expenditure. Using an effort-based monetary incentive delay task, the current event-related potential study examined the neural dynamics underlying the effort-reward interplay in anhedonia using a nonclinical sample who scored high or low on an anhedonia questionnaire. We found that effort prospectively decreased reward effect on the contingent variation negativity and the target-P3 but retrospectively enhanced outcome effect on the feedback-P3 following effort expenditure. Compared to the low-anhedonia group, the high-anhedonia group displayed a diminished effort effect on the target-P3 during effort expenditure and an increased effort-enhancement effect for neutral trials during the feedback-P3 period following effort expenditure. Our findings suggest that anhedonia is associated with an inefficient control and motivation allocation along the efforted-based reward dynamics from effort preparation to effort production.

Consistently increased dorsolateral prefrontal cortex activity during the exposure to acute stressors.

Stress has a major impact on our mental health. Nonetheless, it is still not fully understood how the human brain responds to ongoing stressful events. Here, we aimed to determine the cortical dynamics during the exposure to ecologically valid, standardized stressors. To this end, we conducted 3 experiments in which healthy participants underwent the Trier Social Stress Test (experiments 1 and 2) and the Socially Evaluated Cold Pressor Test (experiment 3) or a respective control manipulation, while we measured their cortical activity using functional near-infrared spectroscopy. Increases in salivary cortisol and subjective stress levels confirmed the successful stress induction in all experiments. Results of experiment 1 showed significantly increased cortical activity, in particular in the dorsolateral prefrontal cortex, during the exposure to the Trier Social Stress Test. Experiment 2 replicated this finding and showed further that this stress-related increase in dorsolateral prefrontal cortex activity was transient and limited to the period of the Trier Social Stress Test. Experiment 3 demonstrated the increased dorsolateral prefrontal cortex activity during the Socially Evaluated Cold Pressor Test, suggesting that this increase is generalizable and not specific to the Trier Social Stress Test. Together, these data show consistently that dorsolateral prefrontal cortex activity is not reduced, as commonly assumed, but increased under stress, which may promote coping with the ongoing stressor.

Energy-efficient network activity from disparate circuit parameters.

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

Dynamical differential covariance recovers directional network structure in multiscale neural systems.

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.

Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation.

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.

A generic neural factor linking resting-state neural dynamics and the brain's response to unexpectedness in multilevel cognition.

The brain's response to change is fundamental to learning and adaptation; this implies the presence of a universal neural mechanism under various contexts. We hypothesized that this mechanism manifests in neural activity patterns across low and high levels of cognition during task processing as well as in resting-state neural dynamics, because both these elements are different facets of the same dynamical system. We tested our hypothesis by (i) characterizing (a) the neural response to changes in low-level continuous information stream and unexpectedness at different cognitive levels and (b) the spontaneous neural dynamics in resting state, and (ii) examining the associations among the dynamics according to cross-individual variability (n = 200). Our results showed that the brain's response magnitude was monotonically correlated with the magnitude of information fluctuation in a low-level task, forming a simple psychophysical function; moreover, this effect was found to be associated with the brain's response to unexpectedness in high-level cognitive tasks (including language processing). These coherent multilevel neural effects in task processing were also shown to be strongly associated with resting-state neural dynamics characterized by the waxing and waning of Alpha oscillation. Taken together, our results revealed large-scale consistency between the neural dynamic system and multilevel cognition.

Physical effort paradox during reward evaluation and links to perceived control.

Effort expenditure not only prospectively discounts the reward associated with it but also retrospectively adds the subjective value of reward, which is known as the effort paradox. This study aimed to resolve the effort paradox during reward evaluation through the critical lens of neural dynamics and its potential moderators. In total, 40 participants completed an effort-reward task wherein they exerted varying physical efforts to obtain an opportunity to win monetary rewards by active or passive decision-making. We found that after-effects of physical effort expenditure during reward evaluation unfolded as an effort paradox over time, manifesting itself as an effort discounting effect during the reward positivity (RewP) period but as an effort enhancement effect during the late positive potential (LPP) interval. Then, we found a dynamic balance between the discounting effect and the enhancement effect such that the more effort discounted the RewP at the early stage, the more effort increased the LPP at the late stage. Moreover, we observed that the effort-reward relationship was modulated by perceived control such that it increased the reward sensitivity effect and decreased the effort discounting effect. Together, our findings provide new insights into the neural mechanisms underlying the after-effects of physical effort exertion during reward evaluation.

Spectral Fingerprints of Cortical Neuromodulation.

Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation, which has profound effects on neuronal processing, cognition, and behavior. However, little is known about the cortical control and effects of pupil-linked neuromodulation. Here, we show that pupil dynamics are tightly coupled to temporally, spectrally, and spatially specific modulations of local and large-scale cortical population activity in the human brain. We quantified the dynamics of band-limited cortical population activity in resting human subjects using magnetoencephalography and investigated how neural dynamics were linked to simultaneously recorded pupil dynamics. Our results show that pupil-linked neuromodulation does not merely affect cortical population activity in a stereotypical fashion. Instead, we identified three frontal, precentral, and occipitoparietal networks, in which local population activity with distinct spectral profiles in the theta, beta, and alpha bands temporally preceded and followed changes in pupil size. Furthermore, we found that amplitude coupling at ∼16 Hz in a large-scale frontoparietal network predicted pupil dynamics. Our results unravel network-specific spectral fingerprints of cortical neuromodulation in the human brain that likely reflect both the causes and effects of neuromodulation.SIGNIFICANCE STATEMENT Brain function is constantly affected by modulatory neurotransmitters. Pupil size has been established as a versatile marker of noradrenergic and cholinergic neuromodulation. However, because the cortical correlates of pupil dynamics are largely unknown, fundamental questions remain unresolved. Which cortical networks control pupil-linked neuromodulation? Does neuromodulation affect cortical activity in a stereotypical or region-specific fashion? To address this, we quantified the dynamics of cortical population activity in human subjects using magnetoencephalography. We found that pupil dynamics are coupled to highly specific modulations of local and large-scale cortical activity in the human brain. We identified four cortical networks with distinct spectral profiles that temporally predicted and followed pupil size dynamics. These effects likely reflect both the cortical control and effect of neuromodulation.

Distributed and Localized Dynamics Emerge in the Mouse Neocortex during Reach-to-Grasp Behavior.

A long-standing question in systems neuroscience is to what extent task-relevant features of neocortical processing are localized or distributed. Coordinated activity across the neocortex has been recently shown to drive complex behavior in the mouse, while activity in selected areas is canonically associated with specific functions (e.g., movements in the case of the motor cortex). Reach-to-grasp (RtG) movements are known to be dependent on motor circuits of the neocortex; however, the global activity of the neocortex during these movements has been largely unexplored in the mouse. Here, we characterized, using wide-field calcium imaging, these neocortex-wide dynamics in mice of either sex engaging in an RtG task. We demonstrate that, beyond motor regions, several areas, such as the visual and the retrosplenial cortices, also increase their activity levels during successful RtGs, and homologous regions across the ipsilateral hemisphere are also involved. Functional connectivity among neocortical areas increases transiently around movement onset and decreases during movement. Despite this global phenomenon, neural activity levels correlate with kinematics measures of successful RtGs in sensorimotor areas only. Our findings establish that distributed and localized neocortical dynamics co-orchestrate efficient control of complex movements.SIGNIFICANCE STATEMENT Mammals rely on reaching and grasping movements for fine-scale interactions with the physical world. In the mouse, the motor cortex is critical for the execution of such behavior, yet little is known about the activity patterns across neocortical areas. Using the mesoscale-level networks as a model of cortical processing, we investigated the hypothesis that areas beyond the motor regions could participate in RtG planning and execution, and indeed a large network of areas is involved while performing RtGs. Movement kinematics correlates mostly with neural activity in sensorimotor areas. By demonstrating that distributed and localized neocortical dynamics for the execution of fine movements coexist in the mouse neocortex during RtG, we offer an unprecedented view on the neocortical correlates of mammalian motor control.

Dissociable neural after-effects of cognitive and physical effort expenditure during reward evaluation.

The reward after-effect of effort expenditure refers to the phenomenon that previous effort investment changes the subjective value of rewards when obtained. However, the neural mechanisms underlying the after-effects of effort exertion are still not fully understood. We investigated the modulation of reward after-effects by effort type (cognitive vs. physical) through the lens of neural dynamics. Thirty-two participants performed a physically or cognitively demanding task during an effort phase and then played a simple gambling game during a subsequent reward phase to earn monetary rewards while their electroencephalogram (EEG) was recorded. We found that previous effort expenditure decreased electrocortical activity during feedback evaluation. Importantly, this effort effect occurred in a domain-general manner during the early stage (as indexed by the reward positivity) but in a domain-specific manner during the later and more elaborative stage (as indexed by the P3 and delta oscillation) of reward evaluation. Additionally, effort expenditure enhanced P3 sensitivity to feedback valence regardless of effort type. Our findings suggest that cognitive and physical effort, although bearing some surface resemblance to each other, may have dissociable neural influences on the reward after-effects.

Structure and dynamics that specialize neurons for high-frequency coincidence detection in the barn owl nucleus laminaris.

A principal cue for sound source localization is the difference in arrival times of sounds at an animal's two ears (interaural time difference, ITD). Neurons that process ITDs are specialized to compare the timing of inputs with submillisecond precision. In the barn owl, ITD processing begins in the nucleus laminaris (NL) region of the auditory brain stem. Remarkably, NL neurons are sensitive to ITDs in high-frequency sounds (kilohertz-range). This contrasts with ITD-based sound localization in analogous regions in mammals where ITD sensitivity is typically restricted to lower-frequency sounds. Guided by previous experiments and modeling studies of tone-evoked responses of NL neurons, we propose NL neurons achieve high-frequency ITD sensitivity if they respond selectively to the small-amplitude, high-frequency oscillations in their inputs, and remain relatively non-responsive to mean input level. We use a biophysically based model to study the effects of soma-axon coupling on dynamics and function in NL neurons. First, we show that electrical separation of the soma from the axon region in the neuron enhances high-frequency ITD sensitivity. This soma-axon coupling configuration promotes linear subthreshold dynamics and rapid spike initiation, making the model more responsive to input oscillations, rather than mean input level. Second, we provide new evidence for the essential role of phasic dynamics for high-frequency neural coincidence detection. Transforming our model to the phasic firing mode further tunes the model to respond selectively to the oscillating inputs that carry ITD information. Similar structural and dynamical mechanisms specialize mammalian auditory brain stem neurons for ITD sensitivity, and thus, our work identifies common principles of ITD processing and neural coincidence detection across species and for sounds at widely different frequencies.

Neural representation of others during action observation in posterior medial prefrontal cortex.

Making decisions based on the actions of others is critical to daily interpersonal interactions. We investigated the representations of other's actions at single neural level in posterior medial prefrontal cortex (pmPFC) in two monkeys during the observation of actions of another agent, in a social interaction task. Each monkey separately interacted with a human partner. The monkey and the human alternated turns as actor and observer. The actor was required to reach one of two visual targets, avoiding the previously chosen target, while the observer monitored that action. pmPFC neurons decoupled in most cases self from others during both the execution and the observation of explicit actions. pmPFC neurons showed selective directional tuning specific for the agent who was executing the task. Moreover, we assessed the relationship of the response coding between the periods immediately before and after the action, by using a cross-modal decoding analysis. We found neural network stability from the action anticipation period to the observation of other's actions, suggesting a strong relationship between the anticipation and the execution of an action. When the monkey was the actor, the population coding appeared dynamic, possibly reflecting a goal-action transformation unique to the monkey's own action execution.

Wireless, battery-free subdermally implantable photometry systems for chronic recording of neural dynamics.

Recording cell-specific neuronal activity while monitoring behaviors of freely moving subjects can provide some of the most significant insights into brain function. Current means for monitoring calcium dynamics in genetically targeted populations of neurons rely on delivery of light and recording of fluorescent signals through optical fibers that can reduce subject mobility, induce motion artifacts, and limit experimental paradigms to isolated subjects in open, two-dimensional (2D) spaces. Wireless alternatives eliminate constraints associated with optical fibers, but their use of head stages with batteries adds bulk and weight that can affect behaviors, with limited operational lifetimes. The systems introduced here avoid drawbacks of both types of technologies, by combining highly miniaturized electronics and energy harvesters with injectable photometric modules in a class of fully wireless, battery-free photometer that is fully implantable subdermally to allow for the interrogation of neural dynamics in freely behaving subjects, without limitations set by fiber optic tethers or operational lifetimes constrained by traditional power supplies. The unique capabilities of these systems, their compatibility with magnetic resonant imaging and computed tomography and the ability to manufacture them with techniques in widespread use for consumer electronics, suggest a potential for broad adoption in neuroscience research.