Phase-amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task.

Although it has been consistently found that local blood-oxygen-level-dependent (BOLD) changes are better modelled by a combination of the power of multiple EEG frequency bands rather than by the power of a unique band alone, the local electro-haemodynamic coupling function is not yet fully characterised. Electrophysiological studies have revealed that the strength of the coupling between the phase of low- and the amplitude of high- frequency EEG activities (phase-amplitude coupling - PAC) has an important role in brain function in general, and in preparation and execution of movement in particular. Using electrocorticographic (ECoG) and functional magnetic resonance imaging (fMRI) data recorded simultaneously in humans performing a finger-tapping task, we investigated the single-trial relationship between the amplitude of the BOLD signal and the strength of PAC and the power of α, ß, and γ bands, at a local level. In line with previous studies, we found a positive correlation for the γ band, and negative correlations for the PACßγ strength, and the α and ß bands. More importantly, we found that the PACßγ strength explained variance of the amplitude of the BOLD signal that was not explained by a combination of the α, ß, and γ band powers. Our main finding sheds further light on the distinct nature of PAC as a functionally relevant mechanism and suggests that the sensitivity of EEG-informed fMRI studies may increase by including the PAC strength in the BOLD signal model, in addition to the power of the low- and high- frequency EEG bands.

A study of the electro-haemodynamic coupling using simultaneously acquired intracranial EEG and fMRI data in humans.

In current fMRI studies designed to map BOLD changes related to interictal epileptiform discharges (IED), which are recorded on simultaneous EEG, the information contained in the morphology and field extent of the EEG events is exclusively used for their classification. Usually, a BOLD predictor based on IED onset times alone is constructed, effectively treating all events as identical. We used intracranial EEG (icEEG)-fMRI data simultaneously recorded in humans to investigate the effect of including any of the features: amplitude, width (duration), slope of the rising phase, energy (area under the curve), or spatial field extent (number of contacts over which the sharp wave was observed) of the fast wave of the IED (the sharp wave), into the BOLD model, to better understand the neurophysiological origin of sharp wave-related BOLD changes, in the immediate vicinity of the recording contacts. Among the features considered, the width was the only one found to explain a significant amount of additional variance, suggesting that the amplitude of the BOLD signal depends more on the duration of the underlying field potential (reflected in the sharp wave width) than on the degree of neuronal activity synchrony (reflected in the sharp wave amplitude), and, consequently, that including inter-event variations of the sharp wave width in the BOLD signal model may increase the sensitivity of forthcoming EEG-fMRI studies of epileptic activity.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.

Improving the sensitivity of EEG-fMRI studies of epileptic activity by modelling eye blinks, swallowing and other video-EEG detected physiological confounds.

RATIONALE: To improve the sensitivity and specificity of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) it is prudent to devise modelling strategies explaining the residual variance. The purpose of this study is to investigate the potential value of including additional regressors for physiological activities, derived from video-EEG, in the modelling of haemodynamic patterns linked to interictal epileptiform discharges (IEDs) using simultaneously recorded video-EEG-fMRI. METHODS: Ten patients with IED (focal epilepsy: 6, idiopathic generalized epilepsy (IGE):4) were studied. BOLD-sensitive fMRI images were acquired on a 3T MRI scanner. 64-channel EEG was recorded using MR-compatible system. A custom made, dual-video-camera system synchronised with EEG was used to record video simultaneously. IEDs and physiological activities were identified and labelled on video-EEG using Brain Analyzer2. fMRI time-series data were pre-processed and analysed using SPM5 software. Two general linear models (GLM) were created; GLM1: IEDs were convolved with the canonical haemodynamic response function and its derivatives. Realignment parameters and pulse regressors were included in the design matrix as confounds, GLM2: GLM1 and additional regressors identified on video-EEG including: eye blinks, hand or foot movement, chewing and swallowing were also included in the design matrix. SPM [F] maps (p<0.05, corrected for family wise error and p<0.001, uncorrected) were generated for both models. We compared the resulting blood oxygen level dependent (BOLD) maps for cluster size, statistical significance and degree of concordance with the irritative zone. RESULTS: BOLD changes relating to physiological activities were generally seen in expected brain areas. In patients with focal epilepsy, the extent and Z-score of the IED-related global maximum BOLD clusters increased in 4/6 patients and additional IED-related BOLD clusters were observed in 3/6 patients for GLM2. Also, the degree of concordance of IED-related maps with irritative zone improved for one patient for GLM2 and was unchanged for the other cases. In patients with IGE, the size and statistical significance for global maximum and other BOLD clusters increased in 2/4 patients. We conclude that the inclusion of additional regressors, derived from video based information, in the design matrix explains a greater amount of variance and can reveal additional IED-related BOLD clusters which may be part of the epileptic networks.

The Microbleed Anatomical Rating Scale (MARS): reliability of a tool to map brain microbleeds.

OBJECTIVE: Brain microbleeds on gradient-recalled echo (GRE) T2*-weighted MRI may be a useful biomarker for bleeding-prone small vessel diseases, with potential relevance for diagnosis, prognosis (especially for antithrombotic-related bleeding risk), and understanding mechanisms of symptoms, including cognitive impairment. To address these questions, it is necessary to reliably measure their presence and distribution in the brain. We designed and systematically validated the Microbleed Anatomical Rating Scale (MARS). We measured intrarater and interrater agreement for presence, number, and anatomical distribution of microbleeds using MARS across different MRI sequences and levels of observer experience. METHODS: We studied a population of 301 unselected consecutive patients admitted to our stroke unit using 2 GRE T2*-weighted MRI sequences (echo time [TE] 40 and 26 ms). Two independent raters with different MRI rating expertise identified, counted, and anatomically categorized microbleeds. RESULTS: At TE = 40 ms, agreement for microbleed presence in any brain location was good to very good (intrarater kappa = 0.85 [95% confidence interval (CI) 0.77-0.93]; interrater kappa = 0.68 [95% CI 0.58-0.78]). Good to very good agreement was reached for the presence of microbleeds in each anatomical region and in individual cerebral lobes. Intrarater and interrater reliability for the number of microbleeds was excellent (intraclass correlation coefficient [ICC] = 0.98 [95% CI 0.97-0.99] and ICC = 0.93 [0.91-0.94]). Very good interrater reliability was obtained at TE = 26 ms (kappa = 0.87 [95% CI 0.61-1]) for definite microbleeds in any location. CONCLUSION: The Microbleed Anatomical Rating Scale has good intrarater and interrater reliability for the presence of definite microbleeds in all brain locations when applied to different MRI sequences and levels of observer experience.