Jan C. de Munck

Generalized synchronization of MEG recordings in Alzheimer's Disease: evidence for involvement of the gamma band.

Summary The purpose of this study was to investigate interdependencies in whole-head magnetoencephalography (MEG) of Alzheimer patients and healthy control subjects. Magnetoencephalograms were recorded in 20 Alzheimer patients (11 men; mean age, 69.0 years [standard deviation, 8.2 years]); Mini-Mental State Examination score, 21.3 points; range, 15 to 27 points) and 20 healthy control subjects (9 men; mean age, 66.4 years [standard deviation, 9.0 years]) during a no-task eyes-closed condition with a 151 channel whole-head MEG system. Synchronization likelihood (a new measure for linear as well as nonlinear interdependencies between signals) and coherence were computed for each channel in different frequency bands (2 to 6, 6 to 10, 10 to 14, 14 to 18, 18 to 22, 22 to 40 Hz). Synchronization was lower in Alzheimer patients in the upper &agr; band (10 to 14 Hz), the upper &bgr; band (18 to 22 Hz), and the &ggr; band (22 to 40 Hz). In contrast, coherence did not show significant group differences at the p<0.05 level. The synchronization likelihood showed a spatial pattern (high synchronization central, parietal and right frontal; low synchronization, occipital and temporal). This study confirms a widespread loss of functional interactions in the &agr; and &bgr; bands, and provides the first evidence for loss of &ggr; band synchronization in Alzheimer’s disease. Synchronization likelihood may be more sensitive to detect such changes than the commonly used coherence analysis.

How do brain tumors alter functional connectivity? A magnetoencephalography study

This study was undertaken to test the hypothesis that brain tumors interfere with normal brain function by disrupting functional connectivity of brain networks.

Magnetoencephalography is more successful for screening and localizing frontal lobe epilepsy than electroencephalography.

Purpose: The diagnosis of frontal lobe epilepsy may be compounded by poor electroclinical localization, due to distributed or rapidly propagating epileptiform activity. This study aimed at developing optimal procedures for localizing interictal epileptiform discharges (IEDs) of patients with localization related epilepsy in the frontal lobe. To this end the localization results obtained for magnetoencephalography (MEG) and electroencephalography (EEG) were compared systematically using automated analysis procedures.

Brain network alterations in Alzheimer's disease measured by eigenvector centrality in fMRI are related to cognition and CSF biomarkers.

Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimers disease (AD). Eigenvector centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. This study used voxel‐wise EC mapping (ECM) to analyze individual whole‐brain resting‐state functional magnetic resonance imaging (MRI) scans in 39 AD patients (age 67 ± 8) and 43 healthy controls (age 69 ± 7). Between‐group differences were assessed by a permutation‐based method. Associations of EC with biomarkers for AD pathology in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE) scores were assessed using Spearman correlation analysis. Decreased EC was found bilaterally in the occipital cortex in AD patients compared to controls. Regions of increased EC were identified in the anterior cingulate and paracingulate gyrus. Across groups, frontal and occipital EC changes were associated with pathological concentrations of CSF biomarkers and with cognition. In controls, decreased EC values in the occipital regions were related to lower MMSE scores. Our main finding is that ECM, a hypothesis‐free and computationally efficient analysis method of functional MRI (fMRI) data, identifies changes in brain network organization in AD patients that are related to cognition and underlying AD pathology. The relation between AD‐like EC changes and cognitive performance suggests that resting‐state fMRI measured EC is a potential marker of disease severity for AD. Hum Brain Mapp 35:2383–2393, 2014.

Treatment-related changes in functional connectivity in brain tumor patients: a magnetoencephalography study

Widespread disturbances in resting state functional connectivity between remote brain areas have been demonstrated in patients with brain tumors. Functional connectivity has been associated with neurocognitive deficits in these patients. Thus far, it is unknown how (surgical) treatment affects functional connectivity. Functional connectivity before and after tumor resection was compared in primary brain tumor patients. Data from 15 newly diagnosed brain tumor patients were analyzed. Patients underwent tumor resection, and both preoperative (up to five months prior to surgery) and postoperative (up to ten months following surgery) resting state magnetoencephalography (MEG) recordings. Seven of the patients (47%) underwent radiotherapy after neurosurgery. Functional connectivity was assessed by the phase lag index (PLI), a measure of the correlation between MEG sensors that is not sensitive to volume conduction. PLIs were averaged to one short-distance and two long-distance (interhemispheric and intrahemispheric) scores in seven frequency bands. We found that functional connectivity changed in a complex manner after tumor resection, depending on frequency band and functional connectivity type. Post-hoc analyses yielded a significant decrease of interhemispheric PLI in the theta band after tumor resection. This result proved to be robust and was not influenced by radiotherapy or a variety of tumor- and patient-related factors.

Fast Eigenvector Centrality Mapping of Voxel-Wise Connectivity in Functional Magnetic Resonance Imaging: Implementation, Validation, and Interpretation

Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra- and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.

Localization of slow wave activity in patients with tumor-associated epilepsy.

Objective: Brain tumors are frequently accompanied by abnormal low frequency magnetic activity (ALFMA). The prevalence and clinical meaning of ALFMA are not well known, although a relation with epileptic brain tissue has been suggested. We studied the prevalence, characteristics and clinical correlates of ALFMA in 20 patients with brain tumors. Methods: In 20 patients with clinical seizures due to a supratentorial tumor, MEG was performed, followed by MR imaging. MEG signals were band pass-filtered (1-4 Hz); the sources of this activity were localized and projected onto the MRI of the patient. Results: Peritumoral ALFMA could be detected in 13 of 20 patients. A pattern of ALFMA distribution around the tumor could be recognized. In eight cases ALFMA also appeared to be localized within the tumor. In three cases ALFMA was also detected in peritumoral white matter. Conclusions: Automatic detection of abnormal delta-activity in patients with a brain tumor and seizures can be performed in a clinical setting. When detected, ALFMA is mostly present in circumscribed regions around the tumor. Presence of ALFMA within the tumor might be an important warning signal for the neurosurgeon that the tumor area comprises functional brain tissue.

The development of hemispheric asymmetry in human motion VEPs

In six healthy adults we examined the sources underlying P1 and N2 of the motion VEP. For this purpose was acquired, in addition to the VEP, MRI images and patterns of regional cerebral blood flow with SPECT for three of the subjects. With the same motion stimulus we also examined the spatial distribution of N2 in children. In both adults and children left and right half-field responses were compared. It was found that N2 is generated by extrastriate activity and that motion stimuli are not equivalently processed in the two cerebral hemispheres. In adults, N2 dominates in one hemisphere irrespective of the visual half-field used for stimulation whereas children show an ipsilateral maximum for N2 upon half-field presentation.

EEG-fMRI correlation patterns in the presurgical evaluation of focal epilepsy: A comparison with electrocorticographic data and surgical outcome measures

EEG-correlated functional MRI (EEG-fMRI) visualizes brain regions associated with interictal epileptiform discharges (IEDs). This technique images the epileptiform network, including multifocal, superficial and deeply situated cortical areas. To understand the role of EEG-fMRI in presurgical evaluation, its results should be validated relative to a gold standard. For that purpose, EEG-fMRI data were acquired for a heterogeneous group of surgical candidates (n=16) who were later implanted with subdural grids and strips (ECoG). The EEG-fMRI correlation patterns were systematically compared with brain areas involved in IEDs ECoG, using a semi-automatic analysis method, as well as to the seizure onset zone, resected area, and degree of seizure freedom. In each patient at least one of the EEG-fMRI areas was concordant with an interictally active ECoG area, always including the early onset area of IEDs in the ECoG data. This confirms that EEG-fMRI reflects a pattern of onset and propagation of epileptic activity. At group level, 76% of the BOLD regions that were covered with subdural grids, were concordant with interictally active ECoG electrodes. Due to limited spatial sampling, 51% of the BOLD regions were not covered with electrodes and could, therefore, not be validated. From an ECoG perspective it appeared that 29% of the interictally active ECoG regions were missed by EEG-fMRI and that 68% of the brain regions were correctly identified as inactive with EEG-fMRI. Furthermore, EEG-fMRI areas included the complete seizure onset zone in 83% and resected area in 93% of the data sets. No clear distinction was found between patients with a good or poor surgical outcome: in both patient groups, EEG-fMRI correlation patterns were found that were either focal or widespread. In conclusion, by comparison of EEG-fMRI with interictal invasive EEG over a relatively large patient population we were able to show that the EEG-fMRI correlation patterns are spatially accurate at the level of neurosurgical units (i.e. anatomical brain regions) and reflect the underlying network of IEDs. Therefore, we expect that EEG-fMRI can play an important role for the determination of the implantation strategy.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the techniques usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.