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2.  Dynamics of Cortical Neuronal Ensembles Transit from Decision Making to Storage for Later Report 
The Journal of Neuroscience  2012;32(35):11956-11969.
Decisions based on sensory evaluation during single trials may depend on the collective activity of neurons distributed across brain circuits. Previous studies have deepened our understanding of how the activity of individual neurons relates to the formation of a decision and its storage for later report. However, little is known about how decision-making and decision maintenance processes evolve in single trials. We addressed this problem by studying the activity of simultaneously recorded neurons from different somatosensory and frontal lobe cortices of monkeys performing a vibrotactile discrimination task. We used the hidden Markov model to describe the spatiotemporal pattern of activity in single trials as a sequence of firing rate states. We show that the animal's decision was reliably maintained in frontal lobe activity through a selective state sequence, initiated by an abrupt state transition, during which many neurons changed their activity in a concomitant way, and for which both latency and variability depended on task difficulty. Indeed, transitions were more delayed and more variable for difficult trials compared with easy trials. In contrast, state sequences in somatosensory cortices were weakly decision related, had less variable transitions, and were not affected by the difficulty of the task. In summary, our results suggest that the decision process and its subsequent maintenance are dynamically linked by a cascade of transient events in frontal lobe cortices.
PMCID: PMC3865507  PMID: 22933781
3.  Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements 
Grasping an object involves shaping the hand and fingers in relation to the object’s physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks. However, it is still unclear how information originating from these networks is processed and integrated. We addressed this issue by analyzing massively parallel signals from population measures (local field potentials, LFPs) and single neuron spiking activities recorded simultaneously during a delayed reach-to-grasp task, by using a 100-electrode array chronically implanted in monkey motor cortex. Motor cortical LFPs exhibit a large multi-component movement-related potential (MRP) around movement onset. Here, we show that the peak amplitude of each MRP component and its latency with respect to movement onset vary along the cortical surface covered by the array. Using a comparative mapping approach, we suggest that the spatio-temporal structure of the MRP reflects the complex physical properties of the reach-to-grasp movement. In addition, we explored how the spatio-temporal structure of the MRP relates to two other measures of neuronal activity: the temporal profile of single neuron spiking activity at each electrode site and the somatosensory receptive field properties of single neuron activities. We observe that the spatial representations of LFP and spiking activities overlap extensively and relate to the spatial distribution of proximal and distal representations of the upper limb. Altogether, these data show that, in motor cortex, a precise spatio-temporal pattern of activation is involved for the control of reach-to-grasp movements and provide some new insight about the functional organization of motor cortex during reaching and object manipulation.
PMCID: PMC3608913  PMID: 23543888
cortical map; high-density recordings; monkey motor cortex; spiking activity; LFP
4.  The Local Field Potential Reflects Surplus Spike Synchrony 
Cerebral Cortex (New York, NY)  2011;21(12):2681-2695.
While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes. This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations but contribute only a fraction of their spikes to temporally precise spike configurations. This finding provides direct evidence for the hypothesized relation that precise spike synchrony constitutes a major temporally and spatially organized component of the LFP.
PMCID: PMC3209854  PMID: 21508303
motor cortex; oscillation; population signals; synchrony
6.  Estimating the contribution of assembly activity to cortical dynamics from spike and population measures 
The hypothesis that cortical networks employ the coordinated activity of groups of neurons, termed assemblies, to process information is debated. Results from multiple single-unit recordings are not conclusive because of the dramatic undersampling of the system. However, the local field potential (LFP) is a mesoscopic signal reflecting synchronized network activity. This raises the question whether the LFP can be employed to overcome the problem of undersampling. In a recent study in the motor cortex of the awake behaving monkey based on the locking of coincidences to the LFP we determined a lower bound for the fraction of spike coincidences originating from assembly activation. This quantity together with the locking of single spikes leads to a lower bound for the fraction of spikes originating from any assembly activity. Here we derive a statistical method to estimate the fraction of spike synchrony caused by assemblies—not its lower bound—from the spike data alone. A joint spike and LFP surrogate data model demonstrates consistency of results and the sensitivity of the method. Combining spike and LFP signals, we obtain an estimate of the fraction of spikes resulting from assemblies in the experimental data.
PMCID: PMC2978895  PMID: 20480218
LFP; Synchrony; Oscillations; Network dynamics; Motor cortex

Results 1-6 (6)