Jacob Reimer

Congruent Activity during Action and Action Observation in Motor Cortex

A variety of studies have shown that motor cortical areas can be activated by observation of familiar actions. Here, we describe single-neuron responses in monkey primary motor (MI) and dorsal premotor (PMd) cortices during passive observation and execution of a familiar task. We show that the spiking modulation, preferred directions, and encoded information of cells in MI and PMd remain consistent during both observation and movement. Furthermore, we find that the presence of a visual target is necessary to elicit this congruent neural activity during observation. These findings along with results from our analysis of the oscillatory power in the beta frequency of the local field potential are consistent with previous imaging and EEG studies that have suggested that congruence between observation and action is a general feature of the motor system, even outside of canonical “mirror” areas. Such congruent activity has proposed relevance to motor learning, mimicry, and communication and has practical applications for the development of motor-cortical neuroprostheses in paralyzed patients.

Fast and Slow Oscillations in Human Primary Motor Cortex Predict Oncoming Behaviorally Relevant Cues

Beta oscillations (12-30 Hz) in local field potentials are prevalent in the motor system, yet their functional role within the context of planning a movement is still debated. In this study, a human participant implanted with a multielectrode array in the hand area of primary motor cortex (MI) was instructed to plan a movement using either the second or fourth of five sequentially presented instruction cues. The beta amplitude increased from the start of the trial until the informative (second or fourth) cue, and was diminished afterwards. Moreover, the beta amplitude peaked just prior to each instruction cue and the delta frequency (0.5-1.5 Hz) entrained to the interval between the cues-but only until the informative cue. This result suggests that the beta amplitude and delta phase in MI reflect the subjects engagement with the rhythmically presented cues and work together to enhance sensitivity to predictable and task-relevant visual cues.

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.

Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex.

Rapid variations in cortical state during wakefulness have a strong influence on neural and behavioural responses and are tightly coupled to changes in pupil size across species. However, the physiological processes linking cortical state and pupil variations are largely unknown. Here we demonstrate that these rapid variations, during both quiet waking and locomotion, are highly correlated with fluctuations in the activity of corticopetal noradrenergic and cholinergic projections. Rapid dilations of the pupil are tightly associated with phasic activity in noradrenergic axons, whereas longer-lasting dilations of the pupil, such as during locomotion, are accompanied by sustained activity in cholinergic axons. Thus, the pupil can be used to sensitively track the activity in multiple neuromodulatory transmitter systems as they control the state of the waking brain.

In vivo three-photon imaging of activity of GCaMP6-labeled neurons deep in intact mouse brain

High-resolution optical imaging is critical to understanding brain function. We demonstrate that three-photon microscopy at 1,300-nm excitation enables functional imaging of GCaMP6s-labeled neurons beyond the depth limit of two-photon microscopy. We record spontaneous activity from up to 150 neurons in the hippocampal stratum pyramidale at ∼1-mm depth within an intact mouse brain. Our method creates opportunities for noninvasive recording of neuronal activity with high spatial and temporal resolution deep within scattering brain tissues.

Statistical assessment of the stability of neural movement representations

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

Periodicity and Evoked Responses in Motor Cortex

Spiking in primary motor cortex (MI) exhibits a characteristic beta frequency periodicity, but the functional relevance of this rhythmic firing is controversial. We simultaneously recorded multiple single units and local field potentials in MI in two monkeys (Macaca mulatta) during continuous, self-paced movements to serially presented targets. We find that the appearance of each new target evokes precisely timed spiking in MI at a characteristic latency but that the exact timing of this response varies depending on its relationship to the phase of the ongoing beta range oscillation. As a result of this interaction between evoked spiking and endogenous beta periodicity, we find that the amount of information about target location encoded in the spiking of MI neurons is not simply a function of elapsed time but depends also on oscillatory phase. Our results suggest that periodicity may be an important feature of the early stages of sensorimotor processing in the cortical motor system.

Benchmarking Spike Rate Inference in Population Calcium Imaging

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

Patterned photostimulation via visible-wavelength photonic probes for deep brain optogenetics

Abstract. Optogenetic methods developed over the past decade enable unprecedented optical activation and silencing of specific neuronal cell types. However, light scattering in neural tissue precludes illuminating areas deep within the brain via free-space optics; this has impeded employing optogenetics universally. Here, we report an approach surmounting this significant limitation. We realize implantable, ultranarrow, silicon-based photonic probes enabling the delivery of complex illumination patterns deep within brain tissue. Our approach combines methods from integrated nanophotonics and microelectromechanical systems, to yield photonic probes that are robust, scalable, and readily producible en masse. Their minute cross sections minimize tissue displacement upon probe implantation. We functionally validate one probe design in vivo with mice expressing channelrhodopsin-2. Highly local optogenetic neural activation is demonstrated by recording the induced response—both by extracellular electrical recordings in the hippocampus and by two-photon functional imaging in the cortex of mice coexpressing GCaMP6.