Altered directional connectivity in Parkinson's disease during performance of a visually guided task

Human Auditory-Motor Networks Show Frequency-Specific Phase-Based Coupling in Resting-State MEG

Perception and production of music and speech rely on auditory-motor coupling, a mechanism which has been linked to temporally precise oscillatory coupling between auditory and motor regions of the human brain, particularly in the beta frequency band. Recently, brain imaging studies using magnetoencephalography (MEG) have also shown that accurate auditory temporal predictions specifically depend on phase coherence between auditory and motor cortical regions. However, it is not yet clear whether this tight oscillatory phase coupling is an intrinsic feature of the auditory-motor loop, or whether it is only elicited by task demands. Further, we do not know if phase synchrony is uniquely enhanced in the auditory-motor system compared to other sensorimotor modalities, or to which degree it is amplified by musical training. In order to resolve these questions, we measured the degree of phase locking between motor regions and auditory or visual areas in musicians and non-musicians using resting-state MEG. We derived phase locking values (PLVs) and phase transfer entropy (PTE) values from 90 healthy young participants. We observed significantly higher PLVs across all auditory-motor pairings compared to all visuomotor pairings in all frequency bands. The pairing with the highest degree of phase synchrony was right primary auditory cortex with right ventral premotor cortex, a connection which has been highlighted in previous literature on auditory-motor coupling. Additionally, we observed that auditory-motor and visuomotor PLVs were significantly higher across all structures in the right hemisphere, and we found the highest differences between auditory and visual PLVs in the theta, alpha, and beta frequency bands. Last, we found that the theta and beta bands exhibited a preference for a motor-to-auditory PTE direction and that the alpha and gamma bands exhibited the opposite preference for an auditory-to-motor PTE direction. Taken together, these findings confirm our hypotheses that motor phase synchrony is significantly enhanced in auditory compared to visual cortical regions at rest, that these differences are highest across the theta-beta spectrum of frequencies, and that there exist alternating information flow loops across auditory-motor structures as a function of frequency. In our view, this supports the existence of an intrinsic, time-based coupling for low-latency integration of sounds and movements which involves synchronized phasic activity between primary auditory cortex with motor and premotor cortical areas.

(A)symmetry during gait initiation in people with Parkinson's disease: A motor and cortical activity exploratory study

Background

Gait asymmetry and deficits in gait initiation (GI) are among the most disabling symptoms in people with Parkinson's disease (PwPD). Understanding if PwPD with reduced asymmetry during GI have higher asymmetry in cortical activity may provide support for an adaptive mechanism to improve GI, particularly in the presence of an obstacle.

Objective

This study quantified the asymmetry of anticipatory postural adjustments (APAs), stepping parameters and cortical activity during GI, and tested if the presence of an obstacle regulates asymmetry in PwPD.

Methods

Sixteen PwPD and 16 control group (CG) performed 20-trials in two conditions: unobstructed and obstructed GI with right and left limbs. We measured, through symmetry index, (i) motor parameters: APAs and stepping, and (ii) cortical activity: the PSD of the frontal, sensorimotor and occipital areas during APA, STEP-I (moment of heel-off of the leading foot in the GI until the heel contact of the same foot); and STEP-II (moment of the heel-off of the trailing foot in the GI until the heel contact of the same foot) phases.

Results

Parkinson's disease showed higher asymmetry in cortical activity during APA, STEP-I and STEP-II phases and step velocity (STEP-II phase) during unobstructed GI than CG. However, unexpectedly, PwPD reduced the level of asymmetry of anterior-posterior displacement (p < 0.01) and medial-lateral velocity (p < 0.05) of the APAs. Also, when an obstacle was in place, PwPD showed higher APAs asymmetry (medial-lateral velocity: p < 0.002), with reduced and increased asymmetry of the cortical activity during APA and STEP-I phases, respectively.

Conclusion

Parkinson's disease were not motor asymmetric during GI, indicating that higher cortical activity asymmetry can be interpreted as an adaptive behavior to reduce motor asymmetry. In addition, the presence of obstacle did not regulate motor asymmetry during GI in PwPD.

Classification of Parkinson's disease motor phenotype: a machine learning approach

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.

Time-frequency analysis of brain activity in response to directional and non-directional visual stimuli: an event related spectral perturbations (ERSP) study

Objective.A large part of the cerebral cortex is dedicated to the processing of visual stimuli and there is still much to understand about such processing modalities and hierarchies. The main aim of the present study is to investigate the differences between directional visual stimuli (DS) and non-directional visual stimuli (n-DS) processing by time-frequency analysis of brain electroencephalographic activity during a visuo-motor task. Electroencephalography (EEG) data were divided into four regions of interest (ROIs) (frontal, central, parietal, occipital).Approach.The analysis of the visual stimuli processing was based on the combination of electroencephalographic recordings and time-frequency analysis. Event related spectral perturbations (ERSPs) were computed with spectrum analysis that allow to obtain the average time course of relative changes induced by the stimulus presentation in spontaneous EEG amplitude spectrum.Main results.Visual stimuli processing enhanced the same pattern of spectral modulation in all investigated ROIs with differences in amplitudes and timing. Additionally, statistically significant differences in occipital ROI between the DS and n-DS visual stimuli processing in theta, alpha and beta bands were found.Significance.These evidences suggest that ERSPs could be a useful tool to investigate the encoding of visual information in different brain regions. Because of their simplicity and their capability in the representation of brain activity, the ERSPs might be used as biomarkers of functional recovery for example in the rehabilitation of visual dysfunction and motor impairment following a stroke, as well as diagnostic tool of anomalies in brain functions in neurological diseases tailored to personalized treatments in clinical environment.

Deep Transfer Learning for Parkinson’s Disease Monitoring by Image-Based Representation of Resting-State EEG Using Directional Connectivity

Parkinson’s disease (PD) is characterized by abnormal brain oscillations that can change rapidly. Tracking neural alternations with high temporal resolution electrophysiological monitoring methods such as EEG can lead to valuable information about alterations observed in PD. Concomitantly, there have been advances in the high-accuracy performance of deep neural networks (DNNs) using few-patient data. In this study, we propose a method to transform resting-state EEG data into a deep latent space to classify PD subjects from healthy cases. We first used a general orthogonalized directed coherence (gOPDC) method to compute directional connectivity (DC) between all pairwise EEG channels in four frequency bands (Theta, Alpha, Beta, and Gamma) and then converted the DC maps into 2D images. We then used the VGG-16 architecture (trained on the ImageNet dataset) as our pre-trained model, enlisted weights of convolutional layers as initial weights, and fine-tuned all layer weights with our data. After training, the classification achieved 99.62% accuracy, 100% precision, 99.17% recall, 0.9958 F1 score, and 0.9958 AUC averaged for 10 random repetitions of training/evaluating on the proposed deep transfer learning (DTL) network. Using the latent features learned by the network and employing LASSO regression, we found that latent features (as opposed to the raw DC values) were significantly correlated with five clinical indices routinely measured: left and right finger tapping, left and right tremor, and body bradykinesia. Our results demonstrate the power of transfer learning and latent space derivation for the development of oscillatory biomarkers in PD.

Reduced Effective Connectivity in the Motor Cortex in Parkinson's Disease

Fast rhythms excess is a hallmark of Parkinson’s Disease (PD). To implement innovative, non-pharmacological, neurostimulation interventions to restore cortical-cortical interactions, we need to understand the neurophysiological mechanisms underlying these phenomena. Here, we investigated effective connectivity on source-level resting-state electroencephalography (EEG) signals in 15 PD participants and 10 healthy controls. First, we fitted multivariate auto-regressive models to the EEG source waveforms. Second, we estimated causal connections using Granger Causality, which provide information on connections’ strength and directionality. Lastly, we sought significant differences connectivity patterns between the two populations characterizing the network graph features—i.e., global efficiency and node strength. Causal brain networks in PD show overall poorer and weaker connections compared to controls quantified as a reduction of global efficiency. Motor areas appear almost isolated, with a strongly impoverished information flow particularly from parietal and occipital cortices. This striking isolation of motor areas may reflect an impaired sensory-motor integration in PD. The identification of defective nodes/edges in PD network may be a biomarker of disease and a potential target for future interventional trials.

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

Effective Connectivity During Rest and Music Listening: An EEG Study on Parkinson's Disease

Music-based interventions seem to enhance motor, sensory and cognitive functions in Parkinson’s disease (PD), but the underlying action mechanisms are still largely unknown. This electroencephalography (EEG) study aimed to investigate the effective connectivity patterns characterizing PD in the resting state and during music listening. EEG recordings were obtained from fourteen non-demented PD patients and 12 healthy controls, at rest and while listening to three music tracks. Theta- and alpha-band power spectral density and multivariate partial directed coherence were computed. Power and connectivity measures were compared between patients and controls in the four conditions and in music vs. rest. Compared to controls, patients showed enhanced theta-band power and slightly enhanced alpha-band power, but markedly reduced theta- and alpha-band interactions among EEG channels, especially concerning the information received by the right central channel. EEG power differences were partially reduced by music listening, which induced power increases in controls but not in patients. Connectivity differences were slightly compensated by music, whose effects largely depended on the track. In PD, music enhanced the frontotemporal inter-hemispheric communication. Our findings suggest that PD is characterized by enhanced activity but reduced information flow within the EEG network, being only partially normalized by music. Nevertheless, music capability to facilitate inter-hemispheric communication might underlie its beneficial effects on PD pathophysiology and should be further investigated.

Galvanic Vestibular Stimulation (GVS) effects on impaired interhemispheric connectivity in Parkinson's Disease

Disrupted interhemispheric connectivity (IHC) is being increasingly recognized as a robust feature of a number of neurological diseases, including Parkinson's Disease (PD). We investigated whether or not Galvanic Vestibular Stimulation (GVS) - a proposed treatment for PD - affected IHC in 11 PD subjects (off medication). In order to avoid the confound of stimulation artifact disrupting the EEG, we investigated the immediate period (52 sec) after stimulation (72 sec) with noisy 1/f-type GVS stimuli. Partial Least Squares (PLS) regression was used to determine the subject-specific linear combination of EEG electrodes that maximized covariance between hemispheres and then computed coherence between the dominant PLS components. PD subjects had increased IHC <;~10 Hz and decreased IHC >13 Hz compared to 11 healthy, age-matched controls. After GVS in PD subjects, a significant increase in IHC was detected, especially above 30 Hz. We suggest that GVS may partially exert its beneficial effects in PD by "normalizing" impaired IHC.

Detecting a Cortical Fingerprint of Parkinson's Disease for Closed-Loop Neuromodulation

Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that-unlike healthy subjects-PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients.

Resting-state cortical connectivity predicts motor skill acquisition

Many studies have examined brain states in an effort to predict individual differences in the capacity for learning, with overall moderate results. The present study investigated how measures of cortical network function acquired at rest using dense-array EEG (256 leads) predict subsequent acquisition of a new motor skill. Brain activity was recorded in 17 healthy young subjects during 3min of wakeful rest prior to a single motor skill training session on a digital version of the pursuit rotor task. Practice was associated with significant gains in task performance (% time on target increased from 24% to 41%, p<0.0001). Using a partial least squares regression (PLS) model, coherence with the region of the left primary motor area (M1) in resting EEG data was a strong predictor of motor skill acquisition (R(2)=0.81 in a leave-one-out cross-validation analysis), exceeding the information provided by baseline behavior and demographics. Within this PLS model, greater skill acquisition was predicted by higher connectivity between M1 and left parietal cortex, possibly reflecting greater capacity for visuomotor integration, and by lower connectivity between M1 and left frontal-premotor areas, possibly reflecting differences in motor planning strategies. EEG coherence, which reflects functional connectivity, predicts individual motor skill acquisition with a level of accuracy that is remarkably high compared to prior reports using EEG or fMRI measures.

An EEMD-IVA framework for concurrent multidimensional EEG and unidimensional kinematic data analysis

Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson's disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications.

The measurement of visual sampling during real-world activity in Parkinson's disease and healthy controls: a structured literature review

BACKGROUND

Visual sampling techniques are used to investigate the complex role of vision during real-world activities in Parkinson's disease. Earlier research is limited to static simple tasks or measurement of eye movements alone, but more recent investigations involve more real-world activities. The approach to the objective measurement of eye movements varies with respect to instrumentation, testing protocols, and mediating factors that may influence visual sampling.

OBJECTIVES

The aim of this review was to examine previous work measuring visual sampling during real-world activities in Parkinson's disease to inform the development of robust protocols. Within this review a real-world activity was considered to be a goal-orientated motor task involving more than one body segment such as reaching or walking.

METHODS

Medline, Embase, PsychInfo, Scopus, Web of Knowledge, PubMed and the Cochrane library databases were searched. Two independent reviewers and an adjudicator screened articles that described quantitative visual sampling in people with Parkinson's disease and healthy controls.

RESULTS

Twenty full-text articles were screened and 15 met inclusion/exclusion criteria. A wide range of instruments and outcome measures were reported which were generally used in a task-dependent manner. Instrument reliability and validity was insufficiently reported in all studies. Few studies considered mediators of visual sampling such as visual or cognitive deficits.

CONCLUSIONS

Future research is required to accurately characterise visual impairments in Parkinson's disease and during real-world activities. Composite use of instruments may be required to achieve reliability and validity of visual sampling outcomes which need to be standardised. Recommendations also include assessment of cognition and basic visual function.

Levodopa reinstates connectivity from prefrontal to premotor cortex during externally paced movement in Parkinson's disease

Dopamine deficiency affects functional integration of activity in distributed neural regions. It has been suggested that lack of dopamine induces disruption of neural interactions between prefrontal and premotor areas, which might underlie impairment of motor control observed in patients with Parkinson's disease (PD). In this study we recorded cortical activity with high-density electroencephalography in 11 patients with PD as a pathological model of dopamine deficiency, and 13 healthy control subjects. Participants performed repetitive extension-flexion movements of their right index finger, which were externally paced at a rate of 0.5 Hz. This required participants to align their movement velocity to the slow external pace. Patients were studied after at least 12-hour withdrawal of dopaminergic medication (OFF state) and after intake of the dopamine precursor levodopa (ON state) in order to examine oscillatory coupling between prefrontal and premotor areas during respectively low and high levels of dopamine. In 10 patients and 12 control participants multiple source beamformer analysis yielded task-related activation of a contralateral cortical network comprising prefrontal cortex (PFC), lateral premotor cortex (lPM), supplementary motor area (SMA) and primary motor cortex (M1). Dynamic causal modelling was used to characterize task-related oscillatory coupling between prefrontal and premotor cortical areas. Healthy participants showed task-induced coupling from PFC to SMA, which was modulated within the γ-band. In the OFF state, PD patients did not express any frequency-specific coupling between prefrontal and premotor areas. Application of levodopa reinstated task-related coupling from PFC to SMA, which was expressed as high-β-γ coupling. Additionally, strong within-frequency γ-coupling as well as cross-frequency θ-γ coupling was observed from PFC to lPM. Enhancement of this cross-frequency θ-γ coupling after application of levodopa was positively correlated with individual improvement in motor function. The results demonstrate that dopamine deficiency impairs the ability to establish oscillatory coupling between prefrontal and premotor areas during an externally paced motor task. Application of extrinsic dopamine in PD patients reinstates physiological prefrontal-premotor coupling and additionally induces within- and cross-frequency coupling from prefrontal to premotor areas, which is not expressed in healthy participants.

Noisy galvanic vestibular stimulation modulates the amplitude of EEG synchrony patterns

Noisy galvanic vestibular stimulation has been associated with numerous cognitive and behavioural effects, such as enhancement of visual memory in healthy individuals, improvement of visual deficits in stroke patients, as well as possibly improvement of motor function in Parkinson’s disease; yet, the mechanism of action is unclear. Since Parkinson’s and other neuropsychiatric diseases are characterized by maladaptive dynamics of brain rhythms, we investigated whether noisy galvanic vestibular stimulation was associated with measurable changes in EEG oscillatory rhythms within theta (4–7.5 Hz), low alpha (8–10 Hz), high alpha (10.5–12 Hz), beta (13–30 Hz) and gamma (31–50 Hz) bands. We recorded the EEG while simultaneously delivering noisy bilateral, bipolar stimulation at varying intensities of imperceptible currents – at 10, 26, 42, 58, 74 and 90% of sensory threshold – to ten neurologically healthy subjects. Using standard spectral analysis, we investigated the transient aftereffects of noisy stimulation on rhythms. Subsequently, using robust artifact rejection techniques and the Least Absolute Shrinkage Selection Operator regression and cross-validation, we assessed the combinations of channels and power spectral features within each EEG frequency band that were linearly related with stimulus intensity. We show that noisy galvanic vestibular stimulation predominantly leads to a mild suppression of gamma power in lateral regions immediately after stimulation, followed by delayed increase in beta and gamma power in frontal regions approximately 20–25 s after stimulation ceased. Ongoing changes in the power of each oscillatory band throughout frontal, central/parietal, occipital and bilateral electrodes predicted the intensity of galvanic vestibular stimulation in a stimulus-dependent manner, demonstrating linear effects of stimulation on brain rhythms. We propose that modulation of neural oscillations is a potential mechanism for the previously-described cognitive and motor effects of vestibular stimulation, and noisy galvanic vestibular stimulation may provide an additional non-invasive means for neuromodulation of functional brain networks.

Comparison of quantitative EEG between patients with Alzheimer's disease and those with Parkinson's disease dementia

OBJECTIVE

Dementia frequently occurs in Parkinson's disease (PD) but its pathophysiological basis is little known. Comparative EEG studies of Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) are still rare, but could provide knowledge on the different pathophysiological mechanisms involved. The objective of the present study was to comparatively evaluate the absolute power and coherence on the EEG for patients with AD and PDD.

METHODS

This study assessed 38 adults with AD, 12 with PDD, 31 with Parkinson's disease without dementia, and 37 controls (CG) by a neurological evaluation, CERAD neuropsychological battery, executive functions tests and qEEG, calculating global absolute powers for the delta, theta, alpha and beta bands and inter- and intra-hemispheric coherences.

RESULTS

The delta and theta powers were highest in PDD and lowest in CG (p<0.05). The beta frontal-occipital inter-hemispheric coherence was highest in PDD (p<0.05). Whereas, alpha and beta frontal inter-hemispheric coherence was highest in PDD and lowest in AD (p<0.05).

CONCLUSION

These results suggest that qEEG power and coherence measures are different in AD and PDD.

SIGNIFICANCE

These qEEG differences must be related to the distinct mechanisms of cortical neural connections in AD and PDD.

Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson's disease

Efficient neural communication between premotor and motor cortical areas is critical for manual motor control. Here, we used high-density electroencephalography to study cortical connectivity in patients with Parkinson's disease (PD) and age-matched healthy controls while they performed repetitive movements of the right index finger at maximal repetition rate. Multiple source beamformer analysis and dynamic causal modeling were used to assess oscillatory coupling between the lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1) in the contralateral hemisphere. Elderly healthy controls showed task-related modulation in connections from lPM to SMA and M1, mainly within the γ-band (>30 Hz). Nonmedicated PD patients also showed task-related γ-γ coupling from lPM to M1, but γ coupling from lPM to SMA was absent. Levodopa reinstated physiological γ-γ coupling from lPM to SMA and significantly strengthened coupling in the feedback connection from M1 to lPM expressed as β-β as well as θ-β coupling. Enhancement in cross-frequency θ-β coupling from M1 to lPM was correlated with levodopa-induced improvement in motor function. The results show that PD is associated with an altered neural communication between premotor and motor cortical areas, which can be modulated by dopamine replacement.

Dynamics of Circadian Thalamocortical Flow of Information during a Peripheral Neuropathic Pain Condition

It is known that the thalamocortical loop plays a crucial role in the encoding of sensory–discriminative features of painful stimuli. However, only a few studies have addressed the changes in thalamocortical dynamics that may occur after the onset of chronic pain. Our goal was to evaluate how the induction of chronic neuropathic pain affected the flow of information within the thalamocortical loop throughout the brain states of the sleep–wake cycle. To address this issue we recorded local field potentials (LFPs) – both before and after the establishment of neuropathic pain in awake freely moving adult rats chronically implanted with arrays of multielectrodes in the lateral thalamus and primary somatosensory cortex. Our results show that the neuropathic injury induced changes in the number of wake and slow-wave-sleep (SWS) state episodes, and especially in the total number of transitions between brain states. Moreover, partial directed coherence – analysis revealed that the amount of information flow between cortex and thalamus in neuropathic animals decreased significantly, indicating that the overall thalamic activity had less weight over the cortical activity. However, thalamocortical LFPs displayed higher phase-locking during awake and SWS episodes after the nerve lesion, suggesting faster transmission of relevant information along the thalamocortical loop. The observed changes are in agreement with the hypothesis of thalamic dysfunction after the onset of chronic pain, and may result from diminished inhibitory effect of the primary somatosensory cortex over the lateral thalamus.