Multiscale Granger causality analysis by \`a trous wavelet transform

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the \`a trous wavelet transform with cubic B-spline filter. We measure GC, at a given scale, by including the wavelet coefficients of the driver times series, at that scale, in the regression model of the target. To validate our method, we apply it to publicly available scalp EEG signals, and we find that the condition of closed eyes, at rest, is characterized by an enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the standard Granger causality is not significantly different in the two conditions.Comment: 4 pages, 3 figure

Effect of non-invasive vagus nerve stimulation on resting-state electroencephalography and laser-evoked potentials in migraine patients : mechanistic insights

A recent multicenter trial provided Class I evidence that for patients with an episodic migraine, non-invasive vagus nerve stimulation (nVNS) significantly increases the probability of having mild pain or being pain-free 2 h post-stimulation. Here we aimed to investigate the potential effect of nVNS in the modulation of spontaneous and pain related bioelectrical activity in a subgroup of migraine patients enrolled in the PRESTO trial by using resting-state electroencephalography and trigeminal laser-evoked potentials (LEPs). LEPs were recorded for 27 migraine patients who received active or sham nVNS over the cervical vagus nerve. We measured power values for frequencies between 1–100 Hz in a resting-state condition and the latency and amplitude of N1, N2, and P2 components of LEPs in a basal condition during and after active or sham vagus nerve stimulation (T0, T1, T2). The P2 evoked by the right and the left trigeminal branch was smaller during active nVNS. The sham device also attenuated the P2 amplitude evoked by the left trigeminal branch at T1 and T2, but this attenuation did not reach significance. No changes were observed for N1 amplitude, N1, N2, P2 latency, or pain rating. nVNS induced an increase of EEG power in both slow and fast rhythms, but this effect was not significant as compared to the sham device. These findings suggest that nVNS acts on the cortical areas that are responsible for trigeminal pain control and pave the ground for future studies aimed at confirming the possible correlations with clinical outcomes, including the effect on symptoms that are directly correlated with trigeminal pain processing and modulation

Dynamic causal modelling of the reduced habituation to painful stimuli in migraine : an EEG study

A consistent finding in migraine is reduced cortical habituation to repetitive sensory stimuli. This study investigated brain dynamics underlying the atypical habituation to painful stimuli in interictal migraine. We investigated modulations in effective connectivity between the sources of laser evoked potentials (LEPs) from a first to final block of trigeminal LEPs using dynamic causal modelling (DCM) in a group of 23 migraine patients and 20 controls. Additionally, we looked whether the strength of dynamical connections in the migrainous brain is initially different. The examined network consisted of the secondary somatosensory areas (lS2, rS2), insulae (lIns, rIns), anterior cingulate cortex (ACC), contralateral primary somatosensory cortex (lS1), and a hidden source assumed to represent the thalamus. Results suggest that migraine patients show initially heightened communication between lS1 and the thalamus, in both directions. After repetitive stimulations, connection strengths from the thalamus to all somatosensory areas habituated in controls whereas this was not apparent in migraine. Together with further abnormalities in initial connectivity strengths and modulations between the thalamus and the insulae, these results are in line with altered thalamo-cortical network dynamics in migraine. Group differences in connectivity from and to the insulae including interhemispheric connections, suggests an important role of the insulae

Multiscale Granger causality analysis by à trous wavelet transform

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the \`a trous wavelet transform with cubic B-spline filter. We measure GC, at a given scale, by including the wavelet coefficients of the driver times series, at that scale, in the regression model of the target. To validate our method, we apply it to publicly available scalp EEG signals, and we find that the condition of closed eyes, at rest, is characterized by an enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the standard Granger causality is not significantly different in the two conditions

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Chronic migraine is characterised by persistent headaches for more than 15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 minutes). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients. The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine