Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.

One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.

A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power.

Inter-areal coherence between field potentials is a widespread phenomenon in cortex. Coherence has been hypothesized to reflect phase-synchronization between oscillators and flexibly gate communication according to behavioral and cognitive demands. We reveal an alternative mechanism where coherence is not the cause but the consequence of communication and naturally emerges because spiking activity in a sending area causes post-synaptic potentials both in the same and in other areas. Consequently, coherence depends in a lawful manner on power and phase-locking in the sender and connectivity. Changes in oscillatory power explained prominent changes in fronto-parietal and LGN-V1 coherence across behavioral conditions. Optogenetic experiments and excitatory-inhibitory network simulations identified afferent synaptic inputs rather than spiking entrainment as the principal determinant of coherence. These findings suggest that unique spectral profiles of different brain areas automatically give rise to large-scale coherence patterns that follow anatomical connectivity and continuously reconfigure as a function of behavior and cognition.

A Turntable Setup for Testing Visual and Tactile Grasping Movements in Non-human Primates.

Grasping movements are some of the most common movements primates do every day. They are important for social interactions as well as picking up objects or food. Usually, these grasping movements are guided by vision but proprioceptive and haptic inputs contribute greatly. Since grasping behaviors are common and easy to motivate, they represent an ideal task for understanding the role of different brain areas during planning and execution of complex voluntary movements in primates. For experimental purposes, a stable and repeatable presentation of the same object as well as the variation of objects is important in order to understand the neural control of movement generation. This is even more the case when investigating the role of different senses for movement planning, where objects need to be presented in specific sensory modalities. We developed a turntable setup for non-human primates (macaque monkeys) to investigate visually and tactually guided grasping movements with an option to easily exchange objects. The setup consists of a turntable that can fit six different objects and can be exchanged easily during the experiment to increase the number of presented objects. The object turntable is connected to a stepper motor through a belt system to automate rotation and hence object presentation. By increasing the distance between the turntable and the stepper motor, metallic components of the stepper motor are kept at a distance to the actual recording setup, which allows using a magnetic-based data glove to track hand kinematics. During task execution, the animal sits in the dark and is instructed to grasp the object in front of it. Options to turn on a light above the object allow for visual presentation of the objects, while the object can also remain in the dark for exclusive tactile exploration. A red LED is projected onto the object by a one-way mirror that serves as a grasp cue instruction for the animal to start grasping the object. By comparing kinematic data from the magnetic-based data glove with simultaneously recorded neural signals, this setup enables the systematic investigation of neural population activity involved in the neural control of hand grasping movements.

Neural coding of intended and executed grasp force in macaque areas AIP, F5, and M1.

Considerable progress has been made over the last decades in characterizing the neural coding of hand shape, but grasp force has been largely ignored. We trained two macaque monkeys (Macaca mulatta) on a delayed grasping task where grip type and grip force were instructed. Neural population activity was recorded from areas relevant for grasp planning and execution: the anterior intraparietal area (AIP), F5 of the ventral premotor cortex, and the hand area of the primary motor cortex (M1). Grasp force was strongly encoded by neural populations of all three areas, thereby demonstrating for the first time the coding of grasp force in single- and multi-units of AIP. Neural coding of intended grasp force was most strongly represented in area F5. In addition to tuning analysis, a dimensionality reduction method revealed low-dimensional responses to grip type and grip force. Additionally, this method revealed a high correlation between latent variables of the neural population representing grasp force and the corresponding latent variables of electromyographic forearm muscle activity. Our results therefore suggest an important role of the cortical areas AIP, F5, and M1 in coding grasp force during movement execution as well as of F5 for coding intended grasp force.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data.

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Neural Dynamics of Variable Grasp-Movement Preparation in the Macaque Frontoparietal Network.

Our voluntary grasping actions lie on a continuum between immediate action and waiting for the right moment, depending on the context. Therefore, studying grasping requires an investigation into how preparation time affects this process. Two macaque monkeys (Macaca mulatta; one male, one female) performed a grasping task with a short instruction followed by an immediate or delayed go cue (0-1300 ms) while we recorded in parallel from neurons in the grasp preparation relevant area F5 that is part of the ventral premotor cortex, and the anterior intraparietal area (AIP). Initial population dynamics followed a fixed trajectory in the neural state space unique to each grip type, reflecting unavoidable movement selection, then diverged depending on the delay, reaching unique states not achieved for immediately cued movements. Population activity in the AIP was less dynamic, whereas F5 activity continued to evolve throughout the delay. Interestingly, neuronal populations from both areas allowed for a readout tracking subjective anticipation of the go cue that predicted single-trial reaction time. However, the prediction of reaction time was better from F5 activity. Intriguingly, activity during movement initiation clustered into two trajectory groups, corresponding to movements that were either "as fast as possible" or withheld movements, demonstrating a widespread state shift in the frontoparietal grasping network when movements must be withheld. Our results reveal how dissociation between immediate and delay-specific preparatory activity, as well as differentiation between cortical areas, is possible through population-level analysis.SIGNIFICANCE STATEMENT Sometimes when we move, we consciously plan our movements. At other times, we move instantly, seemingly with no planning at all. Yet, it's unclear how preparation for movements along this spectrum of planned and seemingly unplanned movement differs in the brain. Two macaque monkeys made reach-to-grasp movements after varying amounts of preparation time while we recorded from the premotor and parietal cortex. We found that the initial response to a grasp instruction was specific to the required movement, but not to the preparation time, reflecting required movement selection. However, when more preparation time was given, neural activity achieved unique states that likely related to withholding movements and anticipation of movement, shedding light on the roles of the premotor and parietal cortex in grasp planning.

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

Predicting Reaction Time from the Neural State Space of the Premotor and Parietal Grasping Network.

Neural networks of the brain involved in the planning and execution of grasping movements are not fully understood. The network formed by macaque anterior intraparietal area (AIP) and hand area (F5) of the ventral premotor cortex is implicated strongly in the generation of grasping movements. However, the differential role of each area in this frontoparietal network is unclear. We recorded spiking activity from many electrodes in parallel in AIP and F5 while three macaque monkeys (Macaca mulatta) performed a delayed grasping task. By analyzing neural population activity during action preparation, we found that state space analysis of simultaneously recorded units is significantly more predictive of subsequent reaction times (RTs) than traditional methods. Furthermore, because we observed a wide variety of individual unit characteristics, we developed the sign-corrected average rate (SCAR) method of neural population averaging. The SCAR method was able to explain at least as much variance in RT overall as state space methods. Overall, F5 activity predicted RT (18% variance explained) significantly better than AIP (6%). The SCAR methods provides a straightforward interpretation of population activity, although other state space methods could provide richer descriptions of population dynamics. Together, these results lend support to the differential role of the parietal and frontal cortices in preparation for grasping, suggesting that variability in preparatory activity in F5 has a more potent effect on trial-to-trial RT variability than AIP. SIGNIFICANCE STATEMENT: Grasping movements are planned before they are executed, but how is the preparatory activity in a population of neurons related to the subsequent reaction time (RT)? A population analysis of the activity of many neurons recorded in parallel in macaque premotor (F5) and parietal (AIP) cortices during a delayed grasping task revealed that preparatory activity in F5 could explain a threefold larger fraction of variability in trial-to-trial RT than AIP. These striking differences lend additional support to a differential role of the parietal and premotor cortices in grasp movement preparation, suggesting that F5 has a more direct influence on trial-to-trial variability and movement timing, whereas AIP might be more closely linked to overall movement intentions.

Normalization of voltage-sensitive dye signal with functional activity measures.

In general, signal amplitude in optical imaging is normalized using the well-established DeltaF/F method, where functional activity is divided by the total fluorescent light flux. This measure is used both directly, as a measure of population activity, and indirectly, to quantify spatial and spatiotemporal activity patterns. Despite its ubiquitous use, the stability and accuracy of this measure has not been validated for voltage-sensitive dye imaging of mammalian neocortex in vivo. In this report, we find that this normalization can introduce dynamic biases. In particular, the DeltaF/F is influenced by dye staining quality, and the ratio is also unstable over the course of experiments. As methods to record and analyze optical imaging signals become more precise, such biases can have an increasingly pernicious impact on the accuracy of findings, especially in the comparison of cytoarchitechtonic areas, in area-of-activation measurements, and in plasticity or developmental experiments. These dynamic biases of the DeltaF/F method may, to an extent, be mitigated by a novel method of normalization, DeltaF/DeltaF(epileptiform). This normalization uses as a reference the measured activity of epileptiform spikes elicited by global disinhibition with bicuculline methiodide. Since this normalization is based on a functional measure, i.e. the signal amplitude of "hypersynchronized" bursts of activity in the cortical network, it is less influenced by staining of non-functional elements. We demonstrate that such a functional measure can better represent the amplitude of population mass action, and discuss alternative functional normalizations based on the amplitude of synchronized spontaneous sleep-like activity. These findings demonstrate that the traditional DeltaF/F normalization of voltage-sensitive dye signals can introduce pernicious inaccuracies in the quantification of neural population activity. They further suggest that normalization-independent metrics such as waveform propagation patterns, oscillations in single detectors, and phase relationships between detector pairs may better capture the biological information which is obtained by high-sensitivity imaging.