Neural Substrates of Chronic Pain in the Thalamocortical Circuit

Chronic pain (CP), a pathological condition with a large repertory of signs and symptoms, has no recognizable neural functional common hallmark shared by its diverse expressions. The aim of the present research was to identify potential dynamic markers shared in CP models, by using simultaneous electrophysiological extracellular recordings from the rat ventrobasal thalamus and the primary somatosensory cortex. We have been able to extract a neural signature attributable solely to CP, independent from of the originating conditions. This study showed disrupted functional connectivity and increased redundancy in firing patterns in CP models versus controls, and interpreted these signs as a neural signature of CP. In a clinical perspective, we envisage CP as disconnection syndrome and hypothesize potential novel therapeutic appraisal

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role

Neuronal functional connection graphs among multiple areas of the rat somatosensory system during spontaneous and evoked activities.

Small-World Networks (SWNs) represent a fundamental model for the comprehension of many complex man-made and biological networks. In the central nervous system, SWN models have been shown to fit well both anatomical and functional maps at the macroscopic level. However, the functional microscopic level, where the nodes of a network are represented by single neurons, is still poorly understood. At this level, although recent evidences suggest that functional connection graphs exhibit small-world organization, it is not known whether and how these maps, potentially distributed in multiple brain regions, change across different conditions, such as spontaneous and stimulus-evoked activities. We addressed these questions by analyzing the data from simultaneous multi-array extracellular recordings in three brain regions of rats, diversely involved in somatosensory information processing: the ventropostero-lateral thalamic nuclei, the primary somatosensory cortex and the centro-median thalamic nuclei. From both spike and Local Field Potential (LFP) recordings, we estimated the functional connection graphs by using the Normalized Compression Similarity for spikes and the Phase Synchrony for LFPs. Then, by using graph-theoretical statistics, we characterized the functional topology both during spontaneous activity and sensory stimulation. Our main results show that: (i) spikes and LFPs show SWN organization during spontaneous activity; (ii) after stimulation onset, while substantial functional graph reconfigurations occur both in spike and LFPs, small-worldness is nonetheless preserved; (iii) the stimulus triggers a significant increase of inter-area LFP connections without modifying the topology of intra-area functional connections. Finally, investigating computationally the functional substrate that supports the observed phenomena, we found that (iv) the fundamental concept of cell assemblies, transient groups of activating neurons, can be described by small-world networks. Our results suggest that activity of neurons from multiple areas of the rat somatosensory system contributes to the integration of local computations arisen in distributed functional cell assemblies according to the principles of SWNs

Coding for stimulus occurrence: spike and LFP responsiveness.

<p>A) Raster Plot for a single cell response to fingertip stimulation (big toe at the top and V at the bottom). Different fingertips are separated by black lines, the vertical red line indicates the time of stimulus onset. B) Mutual Information about stimulus onset for cell in (A). C) LFP response recorded simultaneously and from the same electrode of (A). D) LFP responsiveness, computed as .</p

Multi-objective model optimization: extreme solutions and trade-offs.

<p>A) Binarized response for the neuron in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035850#pone-0035850-g001" target="_blank">Fig.1A</a>. B) Predicted response for a model belonging to the optimal Pareto front. The predictive performances of this model represent a suitable trade-off between and . C) Predicted response for a model belonging to the optimal Pareto front. This model has the best predictive performances for at the expense of a significant worsening in . D) Average response for the true neuron (blue) and the model in (B) and (C) (respectively red and green lines). E-H) Same as (A-D) for a different cell. For the model in (F) and are respectively 0.70 and 0.41. For the model in (G)  = 0.64 and  = 0.61. The smallest achievable was 0.36 (but for  = 0.74).</p

The proposed framework for the estimation of neuronal functional connectivity.

<p>(A) A recording session from thalamic and cortical regions. Arrows indicate the effective influence among neurons. The electrode tips record the neurons in dark red. (B) The firing patterns of the cortical neuron B produce common firing patterns both with neurons A and C but with different time delays. In particular, (red spikes) can be easily inferred by correlation analysis instead of (blue spikes) hardly detectable. (C) Recorded signals are processed in overlapping windows lasting hundreds of milliseconds. (D) Spike trains are modeled by VMMs and compressed by LCAs. The functional connectivity strength between the spike trains A and B is estimated by the length of the compressed spike trains (C(A), C(B)) used by the function. Whether is greater than a fixed threshold then we can conclude that . (E) An example of functional graph extracted by recordings in the time window. (F) Typical sites of the rat paw for the tactile stimulation.</p

Network statistics for spontaneous spiking activity.

<p>Abbreviations: win indicates the window size; represents the chosen threshold used in matrix binarization; is the clustering coefficient of the extracted functional graphs; is the clustering coefficient computed on the latticizied version of the extracted graphs; is the clustering coefficient computed on the randomized version of the extracted graphs; is the characteristic path length of the extracted graphs; is the characteristic path length computed on the randomized version of the extracted graphs; is a small-worldness index equal to ; is a small-worldness index equal to .</p