Esther P. Gardner

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Towards effective and rewarding data sharing.

Recently issued NIH policy statement and implementation guidelines (National Institutes of Health, 2003) promote the sharing of research data. While urging that “all data should be considered for data sharing” and “data should be made as widely and freely available as possible” the current policy requires only high-direct-cost (>US

Facilitation of neuronal activity in somatosensory and posterior parietal cortex during prehension

500,000/yr) grantees to share research data, starting 1 October 2003. Data sharing is central to science, and we agree that data should be made available.

Somatosensory evoked potentials (SEPs) and cortical single unit responses elicited by mechanical tactile stimuli in awake monkeys.

Abstract In order to study prehension in a reproducible manner, we trained monkeys to perform a task in which rectangular, spherical, and cylindrical objects were grasped, lifted, held, and lowered in response to visual cues. The animal’s hand movements were monitored using digital video, together with simultaneously recorded spike trains of neurons in primary somatosensory cortex (S-I) and posterior parietal cortex (PPC). Statistically significant task-related modulation of activity occurred in 78% of neurons tested in the hand area; twice as many cells were facilitated during object acquisition as were depressed. Cortical neurons receiving inputs from tactile receptors in glabrous skin of the fingers and palm, hairy skin of the hand dorsum, or deep receptors in muscles and joints of the hand modulated their firing rates during prehension in consistent and reproducible patterns. Spike trains of individual neurons differed in duration and amplitude of firing, the particular hand behavior(s) monitored, and their sensitivity to the shape of the grasped object. Neurons were classified by statistical analysis into groups whose spike trains were tuned to single task stages, spanned two successive stages, or were multiaction. The classes were not uniformly distributed in specific cytoarchitectonic fields, nor among particular somatosensory modalities. Sequential deformation of parts of the hand as the task progressed was reflected in successive responses of different members of this population. The earliest activity occurred in PPC, where 28% of neurons increased firing prior to hand contact with objects; such neurons may participate in anticipatory motor control programs. Activity shifted rostrally to S-I as the hand contacted the object and manipulated it. The shape of the grasped object had the strongest influence on PPC cells. The results suggest that parietal neurons monitor hand actions during prehension, as well as the physical properties of the grasped object, by shifting activity between populations responsive to hand shaping, grasping, and manipulatory behaviors.

Properties of kinesthetic neurons in somatosensory cortex of awake monkeys.

The origins of surface recorded evoked potentials have been investigated by combining recordings of single unit responses and somatosensory evoked potentials (SEPs) from the postcentral gyrus of 4 alert macaque monkeys. Responses were elicited by mechanical tactile stimuli (airpuffs) which selectively activate rapidly adapting cutaneous mechanoreceptors, and permit patterned stimulation of a restricted area of skin. Epidurally recorded SEPs consisted of an early positive complex, beginning 8-10 msec after airpuff onset, with two prominent positive peaks (P15 and P25), succeeded by a large negative potential (N43) lasting 30 msec, and a late slow positivity (P70). SEPs, while consistent in wave form, varied slightly between monkeys. The amplitude of the early positive complex was enhanced by increasing the number of stimulated points, or by placing the airpuffs in the receptive fields of cortical neurons located beneath the SEP recording electrode. SEP amplitude was depressed when preceded 20-40 msec earlier by a conditioning stimulus to the same skin area. Single unit responses in areas 3b and 1 of primary somatosensory (SI) cortex consisted of a burst of impulses, beginning 11-12 msec after the airpuff onset, and lasting another 15-20 msec. Peak unitary activity occurred at 12-15 msec, corresponding to the P15 wave in the SEP. No peak in SI unit responses occurred in conjunction with the P25 wave. Although SI neurons fired at lower rates during P25, the lack of any peak in SI unit responses suggests that activity in other cortical areas, such as SII cortex, contributes to this wave. Most unit activity in SI cortex ceased by the onset of N43, and was replaced by a period of profound response depression, in which unit responses to additional tactile stimuli were reduced. We propose that the N43 wave reflects IPSPs in cortical neurons previously depolarized and excited by the airpuff stimulus. Late positive potentials (P70) in the SEP had no apparent counterpart in SI unit activity, suggesting generation at other cortical loci.

Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

(1) To study neural mechanisms used to encode kinesthetic information in somatosensory cortex of awake monkeys, we recorded from 227 single neurons responsive to joint movement or specific postures of the forelimb or hand (kinesthetic neurons). Unit responses were characterized quantitatively with respect to: (a) firing patterns; (b) responses to ramp changes in joint position and joint velocity; and (c) responses to sinusoidal joint movements. (2) Kinesthetic neurons were divided into 3 groups. Rapidly-adapting neurons (44%) responded only to joint movement, giving a burst of impulses proportional to velocity. They showed no tonic responses to limb posture. Two populations of tonically active neurons were observed: slowly-adapting neurons (43%) and postural neurons (13%). Both types increased their firing rates with increasing degrees of flexion or extension, showing maximum excitation at the extremes of joint position in the preferred direction. They were distinguished by their sensitivity to the velocity of movement, the size of the angle over which they respond, and the phase relation of their responses to sinusoidal joint movement. (3) The firing rates of kinesthetic neurons in S-I cortex are functions of both joint angle and joint velocity. The importance of each component varies in the 3 classes: velocity of movement is the most important determinant of firing rates of rapidly-adapting and slowly-adapting kinesthetic neurons, and joint angle predominates the responses of postural neurons.

Sensory monitoring of prehension in the parietal lobe: a study using digital video.

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

Neural Representation of Hand Kinematics During Prehension in Posterior Parietal Cortex of the Macaque Monkey

Digital video provides technological tools for monitoring hand kinematics during prehension, and for correlating motor behavior with the simultaneously recorded firing patterns of neurons in parietal cortex of monkeys. The constancy of the hand action in the task allowed us to derive population responses of neurons in both S-I and posterior parietal cortex (PPC) from serial single unit recordings. Activity of PPC neurons preceded that in S-I, and was often shape-selective for particular objects, suggesting that they play an important role in motor planning of prehension. Detailed sensory monitoring of hand-object interactions occurred in S-I, where distinct groups of neurons responded to specific behaviors such as grasping, lifting, holding or releasing objects.

Multiple-joint neurons in somatosensory cortex of awake monkeys

Studies of hand manipulation neurons in posterior parietal cortex of monkeys suggest that their spike trains represent objects by the hand postures needed for grasping or by the underlying patterns of muscle activation. To analyze the role of hand kinematics and object properties in a trained prehension task, we correlated the firing rates of neurons in anterior area 5 with hand behaviors as monkeys grasped and lifted knobs of different shapes and locations in the workspace. Trials were divided into four classes depending on the approach trajectory: forward, lateral, and local approaches, and regrasps. The task factors controlled by the animal-how and when he used the hand-appeared to play the principal roles in modulating firing rates of area 5 neurons. In all, 77% of neurons studied (58/75) showed significant effects of approach style on firing rates; 80% of the population responded at higher rates and for longer durations on forward or lateral approaches that included reaching, wrist rotation, and hand preshaping prior to contact, but only 13% distinguished the direction of reach. The higher firing rates in reach trials reflected not only the arm movements needed to direct the hand to the target before contact, but persisted through the contact, grasp, and lift stages. Moreover, the approach style exerted a stronger effect on firing rates than object features, such as shape and location, which were distinguished by half of the population. Forty-three percent of the neurons signaled both the object properties and the hand actions used to acquire them. However, the spread in firing rates evoked by each knob on reach and no-reach trials was greater than distinctions between different objects grasped with the same approach style. Our data provide clear evidence for synergies between reaching and grasping that may facilitate smooth, coordinated actions of the arm and hand.

Digital video: a tool for correlating neuronal firing patterns with hand motor behavior

(1) In the somatosensory cortex of alert monkeys, 55 neurons were found which receive convergent information from two or more adjacent joints. Most of these multiple-joint neurons were excited by postures of the hand, particularly those involved in grasping. (2) Three basic types of joint interactions were observed. The simplest neurons (occlusion neurons) responded to postures of several different joints, but combination of the preferred postures produced no further increase in firing. The more complex cells showed summated responses to combined postures of adjacent joints, or subliminal facilitation between joints. The responses of both summation neurons and subliminal facilitation neurons were graded with joint angle, and there was an optimum or preferred position for both joints which gave the strongest response. (3) Multiple-joint neurons may provide a neuronal substrate for extracting postural information from several different populations of kinesthetic neurons. They therefore act as feature-detecting neurons, abstracting information about specific body postures.