Brain Noradrenergic Innervation Supports the Development of Parkinson's Tremor: A Study in a Reserpinized Rat Model.

The pathophysiology of tremor in Parkinson's disease (PD) is evolving towards a complex alteration to monoaminergic innervation, and increasing evidence suggests a key role of the locus coeruleus noradrenergic system (LC-NA). However, the difficulties in imaging LC-NA in patients challenge its direct investigation. To this end, we studied the development of tremor in a reserpinized rat model of PD, with or without a selective lesioning of LC-NA innervation with the neurotoxin DSP-4. Eight male rats (Sprague Dawley) received DSP-4 (50 mg/kg) two weeks prior to reserpine injection (10 mg/kg) (DR-group), while seven male animals received only reserpine treatment (R-group). Tremor, rigidity, hypokinesia, postural flexion and postural immobility were scored before and after 20, 40, 60, 80, 120 and 180 min of reserpine injection. Tremor was assessed visually and with accelerometers. The injection of DSP-4 induced a severe reduction in LC-NA terminal axons (DR-group: 0.024 ± 0.01 vs. R-group: 0.27 ± 0.04 axons/um2, p < 0.001) and was associated with significantly less tremor, as compared to the R-group (peak tremor score, DR-group: 0.5 ± 0.8 vs. R-group: 1.6 ± 0.5; p < 0.01). Kinematic measurement confirmed the clinical data (tremor consistency (% of tremor during 180 s recording), DR-group: 37.9 ± 35.8 vs. R-group: 69.3 ± 29.6; p < 0.05). Akinetic-rigid symptoms did not differ between the DR- and R-groups. Our results provide preliminary causal evidence for a critical role of LC-NA innervation in the development of PD tremor and foster the development of targeted therapies for PD patients.

High resolution 3D visualization of the spinal cord in a post-mortem murine model.

A crucial issue in the development of therapies to treat pathologies of the central nervous system is represented by the availability of non-invasive methods to study the three-dimensional morphology of spinal cord, with a resolution able to characterize its complex vascular and neuronal organization. X-ray phase contrast micro-tomography enables a high-quality, 3D visualization of both the vascular and neuronal network simultaneously without the need of contrast agents, destructive sample preparations or sectioning. Until now, high resolution investigations of the post-mortem spinal cord in murine models have mostly been performed in spinal cords removed from the spinal canal. We present here post-mortem phase contrast micro-tomography images reconstructed using advanced computational tools to obtain high-resolution and high-contrast 3D images of the fixed spinal cord without removing the bones and preserving the richness of micro-details available when measuring exposed spinal cords. We believe that it represents a significant step toward the in-vivo application.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome.

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

Single unit activities recorded in the thalamus and the overlying parietal cortex of subjects affected by disorders of consciousness.

The lack of direct neurophysiological recordings from the thalamus and the cortex hampers our understanding of vegetative state/unresponsive wakefulness syndrome and minimally conscious state in humans. We obtained microelectrode recordings from the thalami and the homolateral parietal cortex of two vegetative state/unresponsive wakefulness syndrome and one minimally conscious state patients during surgery for implantation of electrodes in both thalami for chronic deep brain stimulation. We found that activity of the thalamo-cortical networks differed among the two conditions. There were half the number of active neurons in the thalami of patients in vegetative state/unresponsive wakefulness syndrome than in minimally conscious state. Coupling of thalamic neuron discharge with EEG phases also differed in the two conditions and thalamo-cortical cross-frequency coupling was limited to the minimally conscious state patient. When consciousness is physiologically or pharmacologically reversibly suspended there is a significant increase in bursting activity of the thalamic neurons. By contrast, in the thalami of our patients in both conditions fewer than 17% of the recorded neurons showed bursting activity. This indicates that these conditions differ from physiological suspension of consciousness and that increased thalamic inhibition is not prominent. Our findings, albeit obtained in a limited number of patients, unveil the neurophysiology of these conditions at single unit resolution and might be relevant for inspiring novel therapeutic options.

Alternating Dynamics of Segregation and Integration in Human EEG Functional Networks During Working-memory Task.

Brain functional networks show high variability in short time windows but mechanisms governing these transient dynamics remain unknown. In this work, we studied the temporal evolution of functional brain networks involved in a working memory (WM) task while recording high-density electroencephalography (EEG) in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation faded after the prevailing the functional integration. Notably, wrong trials were associated with different or disrupted sequences of the segregation-integration profiles and measures of network centrality and modularity were able to identify crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a WM task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations.

Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.

The thalamo-cortical complex network correlates of chronic pain.

Chronic pain (CP) is a condition with a large repertory of clinical signs and symptoms with diverse expressions. Though widely analyzed, an appraisal at the level of single neuron and neuronal networks in CP is however missing. The present research proposes an empirical and theoretic framework which identifies a complex network correlate nested in the somatosensory thalamocortical (TC) circuit in diverse CP models. In vivo simultaneous extracellular neuronal electrophysiological high-density recordings have been performed from the TC circuit in rats. Wide functional network statistics neatly discriminated CP from control animals identifying collective dynamical traits. In particular, a collapsed functional connectivity and an altered modular architecture of the thalamocortical circuit have been evidenced. These results envisage CP as a functional connectivity disorder and give the clue for unveiling innovative therapeutic strategies.

Electrophysiological and Anatomical Correlates of Spinal Cord Optical Coherence Tomography.

Despite the continuous improvement in medical imaging technology, visualizing the spinal cord poses severe problems due to structural or incidental causes, such as small access space and motion artifacts. In addition, positional guidance on the spinal cord is not commonly available during surgery, with the exception of neuronavigation techniques based on static pre-surgical data and of radiation-based methods, such as fluoroscopy. A fast, bedside, intraoperative real-time imaging, particularly necessary during the positioning of endoscopic probes or tools, is an unsolved issue. The objective of our work, performed on experimental rats, is to demonstrate potential intraoperative spinal cord imaging and probe guidance by optical coherence tomography (OCT). Concurrently, we aimed to demonstrate that the electromagnetic OCT irradiation exerted no particular effect at the neuronal and synaptic levels. OCT is a user-friendly, low-cost and endoscopy-compatible photonics-based imaging technique. In particular, by using a Fourier-domain OCT imager, operating at 850 nm wavelength and scanning transversally with respect to the spinal cord, we have been able to: 1) accurately image tissue structures in an animal model (muscle, spine bone, cerebro-spinal fluid, dura mater and spinal cord), and 2) identify the position of a recording microelectrode approaching and inserting into the cord tissue 3) check that the infrared radiation has no actual effect on the electrophysiological activity of spinal neurons. The technique, potentially extendable to full three-dimensional image reconstruction, shows prospective further application not only in endoscopic intraoperative analyses and for probe insertion guidance, but also in emergency and adverse situations (e.g. after trauma) for damage recognition, diagnosis and fast image-guided intervention.

Results of a prospective study (CATS) on the effects of thalamic stimulation in minimally conscious and vegetative state patients.

OBJECTIVE Deep brain stimulation of the thalamus was introduced more than 40 years ago with the objective of improving the performance and attention of patients in a vegetative or minimally conscious state. Here, the authors report the results of the Cortical Activation by Thalamic Stimulation (CATS) study, a prospective multiinstitutional study on the effects of bilateral chronic stimulation of the anterior intralaminar thalamic nuclei and adjacent paralaminar regions in patients affected by a disorder of consciousness. METHODS The authors evaluated the clinical and radiological data of 29 patients in a vegetative state (unresponsive wakefulness syndrome) and 11 in a minimally conscious state that lasted for more than 6 months. Of these patients, 5 were selected for bilateral stereotactic implantation of deep brain stimulating electrodes into their thalamus. A definitive consensus for surgery was obtained for 3 of the selected patients. All 3 patients (2 in a vegetative state and 1 in a minimally conscious state) underwent implantation of bilateral thalamic electrodes and submitted to chronic stimulation for a minimum of 18 months and a maximum of 48 months. RESULTS In each case, there was an increase in desynchronization and the power spectrum of electroencephalograms, and improvement in the Coma Recovery Scale-Revised scores was found. Furthermore, the severity of limb spasticity and the number and severity of pathological movements were reduced. However, none of these patients returned to a fully conscious state. CONCLUSIONS Despite the limited number of patients studied, the authors confirmed that bilateral thalamic stimulation can improve the clinical status of patients affected by a disorder of consciousness, even though this stimulation did not induce persistent, clinically evident conscious behavior in the patients. Clinical trial registration no.: NCT01027572 ( ClinicalTrials.gov ).

Electrophysiological effects of non-invasive Radio Electric Asymmetric Conveyor (REAC) on thalamocortical neural activities and perturbed experimental conditions.

The microwave emitting Radio Electric Asymmetric Conveyor (REAC) is a technology able to interact with biological tissues at low emission intensity (2 mW at the emitter and 2.4 or 5.8 GHz) by inducing radiofrequency generated microcurrents. It shows remarkable biological effects at many scales from gene modulations up to functional global remodeling even in human subjects. Previous REAC experiments by functional Magnetic Resonance Imaging (fMRI) on healthy human subjects have shown deep modulations of cortical BOLD signals. In this paper we studied the effects of REAC application on spontaneous and evoked neuronal activities simultaneously recorded by microelectrode matrices from the somatosensory thalamo-cortical axis in control and chronic pain experimental animal models. We analyzed the spontaneous spiking activity and the Local Field Potentials (LFPs) before and after REAC applied with a different protocol. The single neuron spiking activities, the neuronal responses to peripheral light mechanical stimuli, the population discharge synchronies as well as the correlations and the network dynamic connectivity characteristics have been analyzed. Modulations of the neuronal frequency associated with changes of functional correlations and significant LFP temporal realignments have been diffusely observed. Analyses by topological methods have shown changes in functional connectivity with significant modifications of the network features.

Quantifying the Number of Discriminable Coincident Dendritic Input Patterns through Dendritic Tree Morphology.

Current developments in neuronal physiology are unveiling novel roles for dendrites. Experiments have shown mechanisms of non-linear synaptic NMDA dependent activations, able to discriminate input patterns through the waveforms of the excitatory postsynaptic potentials. Contextually, the synaptic clustering of inputs is the principal cellular strategy to separate groups of common correlated inputs. Dendritic branches appear to work as independent discriminating units of inputs potentially reflecting an extraordinary repertoire of pattern memories. However, it is unclear how these observations could impact our comprehension of the structural correlates of memory at the cellular level. This work investigates the discrimination capabilities of neurons through computational biophysical models to extract a predicting law for the dendritic input discrimination capability (M). By this rule we compared neurons from a neuron reconstruction repository (neuromorpho.org). Comparisons showed that primate neurons were not supported by an equivalent M preeminence and that M is not uniformly distributed among neuron types. Remarkably, neocortical neurons had substantially less memory capacity in comparison to those from non-cortical regions. In conclusion, the proposed rule predicts the inherent neuronal spatial memory gathering potentially relevant anatomical and evolutionary considerations about the brain cytoarchitecture.

A novel wireless recording and stimulating multichannel epicortical grid for supplementing or enhancing the sensory-motor functions in monkey (Macaca fascicularis).

Artificial brain-machine interfaces (BMIs) represent a prospective step forward supporting or replacing faulty brain functions. So far, several obstacles, such as the energy supply, the portability and the biocompatibility, have been limiting their effective translation in advanced experimental or clinical applications. In this work, a novel 16 channel chronically implantable epicortical grid has been proposed. It provides wireless transmission of cortical recordings and stimulations, with induction current recharge. The grid has been chronically implanted in a non-human primate (Macaca fascicularis) and placed over the somato-motor cortex such that 13 electrodes recorded or stimulated the primary motor cortex and three the primary somatosensory cortex, in the deeply anaesthetized animal. Cortical sensory and motor recordings and stimulations have been performed within 3 months from the implant. In detail, by delivering motor cortex epicortical single spot stimulations (1-8 V, 1-10 Hz, 500 ms, biphasic waves), we analyzed the motor topographic precision, evidenced by tunable finger or arm movements of the anesthetized animal. The responses to light mechanical peripheral sensory stimuli (blocks of 100 stimuli, each single stimulus being <1 ms and interblock intervals of 1.5-4 s) have been analyzed. We found 150-250 ms delayed cortical responses from fast finger touches, often spread to nearby motor stations. We also evaluated the grid electrical stimulus interference with somatotopic natural tactile sensory processing showing no suppressing interference with sensory stimulus detection. In conclusion, we propose a chronically implantable epicortical grid which can accommodate most of current technological restrictions, representing an acceptable candidate for BMI experimental and clinical uses.

Radio electric asymmetric conveyer: a novel neuromodulation technology in Alzheimer's and other neurodegenerative diseases.

Global research in the field of pharmacology has not yet found effective drugs to treat Alzheimer's disease (AD). Thus, alternative therapeutic strategies are under investigation, such as neurostimulation by physical means. Radio electric asymmetric conveyer (REAC) is one of these technologies and has, until now, been used in clinical studies on several psychiatric and neurological disorders with encouraging results in the absence of side effects. Moreover, studies at the cellular level have shown that REAC technology, with the appropriate protocols, is able to induce neuronal differentiation both in murine embryonic cells and in human adult differentiated cells. Other studies have shown that REAC technology is able to positively influence senescence processes. Studies conducted on AD patients and in transgenic mouse models have shown promising results, suggesting REAC could be a useful therapy for certain components of AD.

The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18FDG-PET Alzheimer's Disease Connectomes.

The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. These fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD). The AD represents a multifarious syndrome characterized by structural, functional, and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI) and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF) to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI) repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI) acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to track the individual progression of the impairments prospecting a proficient usage in a wide range of pathophysiological and physiological studies as well.

A simple stimulatory device for evoking point-like tactile stimuli: a searchlight for LFP to spike transitions.

Current neurophysiological research has the aim to develop methodologies to investigate the signal route from neuron to neuron, namely in the transitions from spikes to Local Field Potentials (LFPs) and from LFPs to spikes. LFPs have a complex dependence on spike activity and their relation is still poorly understood(1). The elucidation of these signal relations would be helpful both for clinical diagnostics (e.g. stimulation paradigms for Deep Brain Stimulation) and for a deeper comprehension of neural coding strategies in normal and pathological conditions (e.g. epilepsy, Parkinson disease, chronic pain). To this aim, one has to solve technical issues related to stimulation devices, stimulation paradigms and computational analyses. Therefore, a custom-made stimulation device was developed in order to deliver stimuli well regulated in space and time that does not incur in mechanical resonance. Subsequently, as an exemplification, a set of reliable LFP-spike relationships was extracted. The performance of the device was investigated by extracellular recordings, jointly spikes and LFP responses to the applied stimuli, from the rat Primary Somatosensory cortex. Then, by means of a multi-objective optimization strategy, a predictive model for spike occurrence based on LFPs was estimated. The application of this paradigm shows that the device is adequately suited to deliver high frequency tactile stimulation, outperforming common piezoelectric actuators. As a proof of the efficacy of the device, the following results were presented: 1) the timing and reliability of LFP responses well match the spike responses, 2) LFPs are sensitive to the stimulation history and capture not only the average response but also the trial-to-trial fluctuations in the spike activity and, finally, 3) by using the LFP signal it is possible to estimate a range of predictive models that capture different aspects of the spike activity.

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.

Small-world networks in neuronal populations: a computational perspective.

The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level.

Comparison of latency and rate coding for the direction of whisker deflection in the subcortical somatosensory pathway.

The response of many neurons in the whisker somatosensory system depends on the direction in which a whisker is deflected. Although it is known that the spike count conveys information about this parameter, it is not known how important spike timing might be. The aim of this study was to compare neural codes based on spike count and first-spike latency, respectively. We extracellularly recorded single units from either the rat trigeminal ganglion (primary sensory afferents) or ventroposteromedial (VPM) thalamic nucleus in response to deflection in different directions and quantified alternative neural codes using mutual information. We found that neurons were diverse: some (58% in ganglion, 32% in VPM) conveyed information only by spike count; others conveyed additional information by latency. An issue with latency coding is that latency is measured with respect to the time of stimulus onset, a quantity known to the experimenter but not directly to the subject's brain. We found a potential solution using the integrated population activity as an internal timing signal: in this way, 91% of the first-spike latency information could be recovered. Finally, we asked how well direction could be decoded. For large populations, spike count and latency codes performed similarly; for small ones, decoding was more accurate using the latency code. Our findings indicate that whisker deflection direction is more efficiently encoded by spike timing than by spike count. Spike timing decreases the population size necessary for reliable information transmission and may thereby bring significant advantages in both wiring and metabolic efficiency.

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 neuronal 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.

A role for locus coeruleus in Parkinson tremor.

We analyzed rest tremor, one of the etiologically most elusive hallmarks of Parkinson disease (PD), in 12 consecutive PD patients during a specific task activating the locus coeruleus (LC) to investigate a putative role of noradrenaline (NA) in tremor generation and suppression. Clinical diagnosis was confirmed in all subjects by reduced dopamine reuptake transporter (DAT) binding values investigated by single photon computed tomography imaging (SPECT) with [(123)I] N-ω-fluoropropyl-2ß-carbomethoxy-3ß-(4-iodophenyl) tropane (FP-CIT). The intensity of tremor (i.e., the power of Electromyography [EMG] signals), but not its frequency, significantly increased during the task. In six subjects, tremor appeared selectively during the task. In a second part of the study, we retrospectively reviewed SPECT with FP-CIT data and confirmed the lack of correlation between dopaminergic loss and tremor by comparing DAT binding values of 82 PD subjects with bilateral tremor (n = 27), unilateral tremor (n = 22), and no tremor (n = 33). This study suggests a role of the LC in Parkinson tremor.