Stephanie Westendorff

Implementation of spatial transformation rules for goal-directed reaching via gain modulation in monkey parietal and premotor cortex.

Planning goal-directed movements requires the combination of visuospatial with abstract contextual information. Our sensory environment constrains possible movements to a certain extent. However, contextual information guides proper choice of action in a given situation and allows flexible mapping of sensory instruction cues onto different motor actions. We used anti-reach tasks to test the hypothesis that spatial motor-goal representations in cortical sensorimotor areas are gain modulated by the behavioral context to achieve flexible remapping of spatial cue information onto arbitrary motor goals. We found that gain modulation of neuronal reach goal representations is commonly induced by the behavioral context in individual neurons of both, the parietal reach region (PRR) and the dorsal premotor cortex (PMd). In addition, PRR showed stronger directional selectivity during the planning of a reach toward a directly cued goal (pro-reach) compared with an inferred target (anti-reach). PMd, however, showed stronger overall activity during reaches toward inferred targets compared with directly cued targets. Based on our experimental evidence, we suggest that gain modulation is the computational mechanism underlying the integration of spatial and contextual information for flexible, rule-driven stimulus–response mapping, and thereby forms an important basis of goal-directed behavior. Complementary contextual effects in PRR versus PMd are consistent with the idea that posterior parietal cortex preferentially represents sensory-driven, “automatic” motor goals, whereas frontal sensorimotor areas are stronger engaged in the representation of rule-based, “inferred” motor goals.

Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition

Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset error-locked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.

Fully Implantable Multi-Channel Measurement System for Acquisition of Muscle Activity

This paper presents intramuscular electromyogram (EMG) signals obtained with a fully implantable measurement system that were recorded during goal directed arm movements. In a first implantation thin film electrodes were epimysially implanted on the deltoideus of a rhesus macaque and the encapsulation process was monitored by impedance measurements. Increase of impedance reached a constant level after four weeks indicating a complete encapsulation of electrodes. EMG recorded with these electrodes yielded a signal-to-noise ratio of about 80 dB at 200 Hz. The EMG recorded during goal-directed arm movements showed a high similarity to movements in the same direction and at the same time presented clear differences between different movement directions in time domain. Six classifiers and seven time and frequency domain features were investigated with the aim of discriminating the direction of arm movement from EMG signals. Reliable recognition of arm movements was achieved for a subset of the movements under investigation only. A second implantation of the whole measurement system for nine weeks demonstrated simple handling during surgery and good biotolerance in the animals.

Interareal Spike-Train Correlations of Anterior Cingulate and Dorsal Prefrontal Cortex during Attention Shifts

The anterior cingulate cortex (ACC) and prefrontal cortex (PFC) are believed to coactivate during goal-directed behavior to identify, select, and monitor relevant sensory information. Here, we tested whether coactivation of neurons across macaque ACC and PFC would be evident at the level of pairwise neuronal correlations during stimulus selection in a spatial attention task. We found that firing correlations emerged shortly after an attention cue, were evident for 50–200 ms time windows, were strongest for neuron pairs in area 24 (ACC) and areas 8 and 9 (dorsal PFC), and were independent of overall firing rate modulations. For a subset of cell pairs from ACC and dorsal PFC, the observed functional spike-train connectivity carried information about the direction of the attention shift. Reliable firing correlations were evident across area boundaries for neurons with broad spike waveforms (putative excitatory neurons) as well as for pairs of putative excitatory neurons and neurons with narrow spike waveforms (putative interneurons). These findings reveal that stimulus selection is accompanied by slow time scale firing correlations across those ACC/PFC subfields implicated to control and monitor attention. This functional coupling was informative about which stimulus was selected and thus indexed possibly the exchange of task-relevant information. We speculate that interareal, transient firing correlations reflect the transient coordination of larger, reciprocally interacting brain networks at a characteristic 50–200 ms time scale. SIGNIFICANCE STATEMENT Our manuscript identifies interareal spike-train correlations between primate anterior cingulate and dorsal prefrontal cortex during a period where attentional stimulus selection is likely controlled by these very same circuits. Interareal correlations emerged during the covert attention shift to one of two peripheral stimuli, proceeded on a slow 50–200 ms time scale, and occurred between putative pyramidal and putative interneurons. Spike-train correlations emerged particularly for cell pairs tuned to similar contralateral target locations, thus indexing the interareal coordination of attention-relevant information. These findings characterize a possible way by which prefrontal and anterior cingulate cortex circuits implement their control functions through coordinated firing when macaque monkeys select and monitor relevant stimuli for goal-directed behaviors.

Acquisition of myoelectric signals to control a hand prosthesis with implantable epimysial electrodes

The acquisition of myoelectric signals from the Musculus deltoideus of a rhesus monkey is described. Such signals are aimed to be used as control signal for an active myoelectric hand prosthesis. For recording, implantable flexible, polyimide-based multi-site microelectrodes were placed epimysially on the muscle. EMG signals were recorded during voluntary goal-directed movements of the arm, and analyzed with respect to signal amplitude and frequency.

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque.

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.

Subnetwork selection in deep cortical layers is mediated by beta-oscillation dependent firing

Even the simplest tasks in our everyday life depend on the activity in multiple brain areas that are coordinated in large-scale brain networks (Sporns, 2011). These networks restructure the information flow in the brain on fast time scales whenever we re-focus our attention on novel tasks or initiate novel movements to interact with our environment. This restructuring of information flow is implemented in cortical circuits by functionally changing the identity and composition of cells in the output layers that connect to other long-distant network nodes. Recent anatomical evidence has begun to show that these output cells form highly specific, segregated subnetworks (Krook-Magnuson et al., 2012). Cells within a subnetwork more likely interconnect with each other and share distant projection targets, avoiding interactions with other cells that project elsewhere. It remains unknown, however, how cells of the same deep layer subnetwork are selected when task demands change (Douglas and Martin, 2004). Results from a recent study by Canolty et al. (2012) suggest an interesting possibility to resolve this context-dependent output selection by showing that the composition of cells that fire together and in phase in the beta cycle can be inferred from the strength of local beta rhythmic modulation. Canolty and colleagues recorded the firing of cells in deep layer motor cortex of primates engaged in either of two tasks that required moving a cursor between visual stimuli manually (with their hands), or through brain activity (brain control). During these tasks, the majority of cells in motor cortex fire systematically at particular phases of a beta oscillation that reduces its amplitudes when actual or brain controlled movements are planned and executed. Canolty and colleagues show that beta synchronous firing rates of individual cells increase or decrease in close correspondence to increases or decreases in the amplitude of beta oscillations. This cell-specific mapping between firing rates and beta amplitudes was highly stable for single cells across multiple recording sessions, but it varied for a large subset of cells between different tasks in a reversible and reliable manner. These findings have potentially wide implications for our understanding of the mechanisms of rapid sub-network selection. Figure ​Figure1A1A illustrates the cell-specific mapping between firing rates and beta amplitudes and how a particular beta rhythmic state in a local circuit could signify which cells fire and are therefore selected into the currently active subnetwork. When the beta amplitude of the local field potentials surrounding the cell changes over time, for example when it is reduced during movement initiation, the subnetwork of cells that fired during high beta states dissipates and the circuit switches on cells that prefer firing at low beta amplitudes (Figure ​(Figure1A).1A). Furthermore, Canolty et al. showed that for one third of beta-modulated cells, the rank ordering of firing rates varied systematically during different tasks. As shown in Figure ​Figure1B,1B, this task specificity of reliable beta-to-rate mapping could underlie the flexible and fast selection of task-specific subnetworks. Figure 1 Proposed scheme of subnetwork participation of deep layer cells by amplitude variations of beta oscillations. (A) Canolty et al. (2012) have shown that in deep cortical layers the firing rate of single cells show a highly robust sigmoidal relation to ... The task- and cell- specific mapping between beta amplitude and firing rate reveals a statistically robust relationship of two, in principle, independent signals. As suggested by Canolty and colleagues, this relationship would allow activating brain circuits by changing the beta rhythmic temporal structure—independently from targeting the firing rate of neurons explicitly. Such a mechanism assumes that the beta-rhythm is causal of the firing rate changes. Specifically, as indicated in Figure ​Figure1C1C (left panel), external beta rhythmic input may act as the trigger to entrain synaptic activity of cells in target areas that are resonant to beta rhythmic fluctuations of input. Stronger rhythmicity of beta rhythmic input will thereby select cells according to their beta-specific sensitivity and possibly independent of their overall level of excitation (Akam and Kullmann, 2010). According to this scenario, a change of beta rhythmic activity would serve as a true switch of a local network by causally modulating firing rates. Future studies, using stimulation techniques, will be necessary to test the prediction of beta rhythmicity being causally involved in task-specific subnetwork selection. An alternative possibility is that varying beta amplitudes within a local circuit may not cause the switch of subnetworks, but may rather reflect the consequence of the switch itself (Figure ​(Figure1C,1C, right panel). According to this assumption, beta amplitudes may derive from the intrinsic properties of the cells that are selected, including e.g., beta rhythmic, intrinsic burst firing. Thus, the actual switch of active cells would follow from mechanisms that only indirectly relate to the rhythm generating mechanisms. For example, in computational firing rate models of randomly connected networks, such switches of subnetworks can be achieved by locally biasing the balance of excitation and inhibition, such that selected cells will be released from inhibition and will maintain their selectively activated state via the di-synaptic inhibition of non-selected cells (Vogels and Abbott, 2009). Whatever the actual mechanism that causes a local circuit to switch the subnetworks of cells in deep cortical layers, the finding of a reliable mapping between beta amplitude and firing rate in the majority of deep layer cells in motor cortex critically extends our perspective of the working principles of brain activity. The results by Canolty et al. show that brain signals (1) at the local scale of cell (firing), (2) at the meso-scale of circuits (beta entrainment), and (3) at macro-scales comprising long-distant networks (inter-areal beta-coherence, not discussed here), combine together in systematic ways to subserve the larger goal to establish functional networks that can flexibly switch according to rapidly varying task demands. This “cross-level” relation of activity is only recently moving into the focus of scientific scrutiny. The discussed study and its broad analysis of available neuronal signals from all three levels of neuronal dynamics points the right way into this direction, and promises to critically advance our understanding of how external factors like specific reach movements, or the shifting of attention are implemented by the dynamics of local circuits and their dynamic interplay with larger functional brain networks.

Acquisition of muscle activity with a fully implantable multi-channel measurement system

This work presents intramuscular measurements of the electromyogram (EMG) during goal directed arm movements. Thin film electrode arrays were epimysially implanted on the deltoideus of a rhesus macaque and the encapsulation process was monitored by impedance measurements. Increase of impedance plateaued after four weeks indicating a complete incorporation of electrodes. EMG recorded with these electrodes yielded a signal to noise ratio of about 80 dB at 200 Hz. The EMG recorded during goal directed arm movements showed high similarity amongst movements in the same direction while presenting clear differences between different movement directions. A second implantation of the whole measurement system for nine weeks proved good handling and biotolerance.

What is ‘anti’ about anti-reaches? Reference frames selectively affect reaction times and endpoint variability

Reach movement planning involves the representation of spatial target information in different reference frames. Neurons at parietal and premotor stages of the cortical sensorimotor system represent target information in eye- or hand-centered reference frames, respectively. How the different neuronal representations affect behavioral parameters of motor planning and control, i.e. which stage of neural representation is relevant for which aspect of behavior, is not obvious from the physiology. Here, we test with a behavioral experiment if different kinematic movement parameters are affected to a different degree by either an eye- or hand-reference frame. We used a generalized anti-reach task to test the influence of stimulus-response compatibility (SRC) in eye- and hand-reference frames on reach reaction times, movement times, and endpoint variability. While in a standard anti-reach task, the SRC is identical in the eye- and hand-reference frames, we could separate SRC for the two reference frames. We found that reaction times were influenced by the SRC in eye- and hand-reference frame. In contrast, movement times were only influenced by the SRC in hand-reference frame, and endpoint variability was only influenced by the SRC in eye-reference frame. Since movement time and endpoint variability are the result of planning and control processes, while reaction times are consequences of only the planning process, we suggest that SRC effects on reaction times are highly suited to investigate reference frames of movement planning, and that eye- and hand-reference frames have distinct effects on different phases of motor action and different kinematic movement parameters.

Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Prediction errors signal unexpected outcomes indicating that expectations need to be adjusted. For adjusting expectations efficiently prediction errors need to be associated with the precise features that gave rise to the unexpected outcome. For many visual tasks this credit assignment proceeds in a multidimensional feature space that makes it ambiguous which object defining features are relevant. Here, we report of a potential solution by showing that neurons in all areas of the medial and lateral fronto-striatal networks encode prediction errors that are specific to separate features of attended multidimensional stimuli, with the most ubiquitous prediction error occurring for the reward relevant features. These feature specific prediction error signals (1) are different from a non-specific prediction error signal, (2) arise earliest in the anterior cingulate cortex and later in lateral prefrontal cortex, caudate and ventral striatum, and (3) contribute to feature-based stimulus selection after learning. These findings provide strong evidence for a widely-distributed feature-based eligibility trace that can be used to update synaptic weights for improved feature-based attention. Highlights Neural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network. Feature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex. Ventral striatum neurons encode feature specific surprise strongest for the goal-relevant feature. Neurons encoding feature-specific prediction errors contribute to attentional selection after learning.