Larry Shupe

Increased gamma-range activity in human sensorimotor cortex during performance of visuomotor tasks.

OBJECTIVE We documented changes in spectral power of human electrocorticograms (ECoG) during performance of sensorimotor tasks. METHODS In 6 human subjects, ECoGs were recorded simultaneously from 14 subdural cortical sites in forearm sensorimotor cortex. The subjects performed 3 visuomotor tasks: tracking a moving visual target with a joystick-controlled cursor, threading pieces of tubing, and pinching the fingers sequentially against the thumb. Control conditions consisted of passive resting and active extension of the wrist. For each site the spectral power of the ECoG during these behaviors was computed for 5 10 Hz ranges between 10 and 60 Hz. RESULTS All subjects showed power decreases in the range of 11-20 Hz and power increases in the 31-60 Hz range during performance of the visuomotor tasks, at sites in forearm sensorimotor cortex and adjacent areas. Simple wrist movements often produced little change in power. Three subjects showed episodes of explicit gamma oscillations during the visuomotor tasks. Different sites showed increases in gamma-range power for different tasks, indicating that the spatial distribution of the gamma activity is specific to the tasks. Cross-spectra showed that gamma activity could become synchronized between separate sites during particular tasks. CONCLUSIONS Synchronized gamma-range activity in human sensorimotor cortex increases with performance of manipulative visuomotor tasks, supporting the hypothesis that coherent gamma oscillations may play a role in sensorimotor integration or attention.

No Evidence for Chronic Demyelination in Spared Axons after Spinal Cord Injury in a Mouse

The pattern of remyelination after traumatic spinal cord injury remains elusive, with animal and human studies reporting partial to complete demyelination followed by incomplete remyelination. In the present study, we found that spared rubrospinal tract (RST) axons of passage traced with actively transported dextrans and examined caudally to the lesion 12 weeks after mouse spinal cord contusion injury were fully remyelinated. Spared axons exhibited a marginally reduced myelin thickness and significantly shorter internodes. CASPR (contactin-associated protein) and Kv1.2 channels were used to identify internodes and paranodal protein distribution properties were used as an index of myelin integrity. This is the first time the CNS myelin internode length was measured in a mouse. To better understand the significance of shortened internodes and thinner myelin in spared axons, we modeled conduction properties using McIntyres et al. model of myelinated axons. Mathematical modeling predicted a 21% decrease in the conduction velocity of remyelinated RST axons attributable to shortened internodes. To determine whether demyelination could be present on axons exhibiting a pathological transport system, we used the retroviral reporter system. Virally delivered green fluorescent protein unveiled a small population of dystrophic RST axons that persist chronically with evident demyelination or abnormal remyelination. Collectively, these data show that lasting demyelination in spared axons is rare and that remyelination of axons of passage occurs in the chronically injured mouse spinal cord.

The Neurochip-2: An Autonomous Head-Fixed Computer for Recording and Stimulating in Freely Behaving Monkeys

The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkeys head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two “programmable-system-on-chips” (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments.

Neural networks controlling wrist movements

In monkeys performing a step-tracking task, the discharge patterns of forearm motor units and connected premotoneuronal cells in the cortex and red nucleus (identified by postspike facilitation of EMG) fall into characteristic classes: tonic, phasic-tonic, decrementing, etc. The authors used dynamic neural network models incorporating these discharge patterns to investigate networks that could transform a step input of target position to the observed discharge patterns of flexor and extensor motoneurons. These networks have interconnected hidden units with either excitatory or inhibitory connections to each other and to the motoneurons. The activity of many hidden units resembles discharge patterns that are observed in monkey recordings. The network solutions typically involve preferential connectivity within synergistic groups and often include reciprocal inhibition of antagonists. A network trained on a specific input step level does not necessarily produce a proportional output for other step sizes; however, the networks can be trained to generate motor responses proportional to a target step size. The role of the hidden units can also be investigated by selective lesions or stimulation

Changes in power and coherence of brain activity in human sensorimotor cortex during performance of visuomotor tasks

Electrocorticograms (ECoG) were recorded using subdural grid electrodes in forearm sensorimotor cortex of six human subjects. The subjects performed three visuomotor tasks, tracking a moving visual target with a joystick-controlled cursor; threading pieces of tubing; and pinching the fingers sequentially against the thumb. Control conditions were resting and active wrist extension. ECoGs were recorded at 14 sites in hand- and arm-sensorimotor area, functionally identified with electrical stimulation. For each behavior we computed spectral power of ECoG in each site and coherence in all pair-wise sites. In three out of six subjects, gamma-oscillations were observed when the subjects started the tasks. All subjects showed widespread power decrease in the range of 11-20 Hz and power increase in the 31-60 Hz ranges during performance of the visuomotor tasks. The changes in gamma-range power were more vigorous during the tracking and threading tasks compared with the wrist extension. Coherence analysis also showed similar task-related changes in coherence estimates. In contrast to the power changes, coherence estimates increased not only in gamma-range but also at lower frequencies during the manipulative visuomotor tasks. Paired sites with significant increases in coherence estimates were located within and between sensory and motor areas. These results support the hypothesis that coherent cortical activity may play a role in sensorimotor integration or attention.

Axonal Thinning and Extensive Remyelination without Chronic Demyelination in Spinal Injured Rats

Remyelination following spinal cord injury (SCI) is thought to be incomplete; demyelination is reported to persist chronically and is proposed as a compelling therapeutic target. Yet most reports do not distinguish between the myelin status of intact axons and injury-severed axons whose proximal stumps persist but provide no meaningful function. We previously found full remyelination of spared, intact rubrospinal axons caudal to the lesion in chronic mouse SCI. However, the clinical concept of chronically demyelinated spared axons remains controversial. Since mouse models may have limitations in clinical translation, we asked whether the capacity for full remyelination is conserved in clinically relevant chronic rat SCI. We determined myelin status by examining paranodal protein distribution on anterogradely labeled, intact corticospinal and rubrospinal axons throughout the extent of the lesion. Demyelination was evident on proximal stumps of severed axons, but not on intact axons. For the first time, we demonstrate that a majority of intact axons exhibit remyelination (at least one abnormally short internode, <100 μm). Remarkably, shortened internodes were significantly concentrated at the lesion epicenter and individual axons were thinned by 23% compared with their rostral and caudal zones. Mathematical modeling predicted a 25% decrease in conduction velocity at the lesion epicenter due to short internodes and axonal thinning. In conclusion, we do not find a large chronically demyelinated population to target with remyelination therapies. Interventions may be better focused on correcting structural or molecular abnormalities of regenerated myelin.

Case Study of Ecstatic Meditation: fMRI and EEG Evidence of Self-Stimulating a Reward System

We report the first neural recording during ecstatic meditations called jhanas and test whether a brain reward system plays a role in the joy reported. Jhanas are Altered States of Consciousness (ASC) that imply major brain changes based on subjective reports: (1) external awareness dims, (2) internal verbalizations fade, (3) the sense of personal boundaries is altered, (4) attention is highly focused on the object of meditation, and (5) joy increases to high levels. The fMRI and EEG results from an experienced meditator show changes in brain activity in 11 regions shown to be associated with the subjective reports, and these changes occur promptly after jhana is entered. In particular, the extreme joy is associated not only with activation of cortical processes but also with activation of the nucleus accumbens (NAc) in the dopamine/opioid reward system. We test three mechanisms by which the subject might stimulate his own reward system by external means and reject all three. Taken together, these results demonstrate an apparently novel method of self-stimulating a brain reward system using only internal mental processes in a highly trained subject.

Dynamic Neural Network Models of the Premotoneuronal Circuitry Controlling Wrist Movements in Primates

Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations.The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

NEURAL NETWORK MODELS OF THE PRIMATE MOTOR SYSTEM

Dynamic neural network models that incorporate time-varying activity and allow unrestricted connectivity were trained by back-propagation to generate discharge patterns of cells previously observed in behaving monkeys. Neuronal recordings in monkeys performing a simple alternating step-tracking task have shown that forearm motor units and connected premotoneuronal cells fire with characteristic patterns: phasic-tonic, tonic, decrementing, etc. To investigate the properties of networks that could transform a step input of target position to the four observed discharge patterns of flexor and extensor motor units we trained dynamic network models to generate these firing patterns as outputs. These networks have hidden units with either excitatory or inhibitory connections to each other and to the output “motor units”. Network solutions have been found for a variety of connection matrices corresponding to different network topologies. The activity of many hidden units resembles the discharge patterns that have been observed in physiological recordings of neurons in motor cortex and red nucleus. In networks receiving both sustained (step) input and transient input signals, preferential connections can develop within subsets of phasic and tonic units. The function of specific hidden units in the network can be tested by making selective lesions of particular units and determining the behavior of the remaining network. When relatively few hidden units with similar activations are strongly interconnected, removing a particular unit can have appreciable consequences in eliminating corresponding components of activity in other units. The output effects of a given unit can also be tested by delivering a simulated stimulus and analyzing the propagated network response. Delivering the stimulus pulse during various phases of the ongoing task shows how the impulse response is modulated by the changing activation patterns.

Integration and differentiation in dynamic recurrent neural networks

Dynamic neural networks with recurrent connections were trained by backpropagation to generate the differential or the leaky integral of a nonrepeating frequency-modulated sinusoidal signal. The trained networks performed these operations on arbitrary input waveforms. Reducing the network size by deleting ineffective hidden units and combining redundant units, and then retraining the network produced a minimal network that computed the same function and revealed the underlying computational algorithm. Networks could also be trained to compute simultaneously the differential and integral of the input on two outputs; the two operations were performed in distributed overlapping fashion, and the activations of the hidden units were dominated by the integral. Incorporating units with time constants into model networks generally enhanced their performance as integrators and interfered with their ability to differentiate.