Bob W. van Dijk

Nonlinear synchronization in EEG and whole-head MEG recordings of healthy subjects.

According to Friston, brain dynamics can be modelled as a large ensemble of coupled nonlinear dynamical subsystems with unstable and transient dynamics. In the present study, two predictions from this model (the existence of nonlinear synchronization between macroscopic field potentials and itinerant nonlinear dynamics) were investigated. The dependence of nonlinearity on the method of measuring brain activity (EEG vs. MEG) was also investigated. Dataset I consisted of 10 MEG recordings in 10 healthy subjects. Dataset II consisted of simultaneously recorded MEG (126 channels) and EEG (19 channels) in 5 healthy subjects. Nonlinear coupling was assessed with the synchronization likelihood S and dynamic itinerancy with the synchronization entropy Hs. Significance was assessed with a bootstrap procedure (“surrogate data testing”), comparing S and Hs with their distribution under the null hypothesis of stationary, linear dynamics. Significant nonlinear synchronization was detected in 14 of 15 subjects. The nonlinear dynamics were associated with a high index of itinerant behaviour. Nonlinear interdependence was significantly more apparent in MEG data than EEG. Synchronous oscillations in MEG and EEG recordings contain a significant nonlinear component that exhibits characteristics of unstable and itinerant behaviour. These findings are in line with Fristons proposal that the brain can be conceived as a large ensemble of coupled nonlinear dynamical subsystems with labile and unstable dynamics. The spatial scale and physical properties of MEG acquisition may increase the sensitivity of the data to underlying nonlinear structure. Hum. Brain Mapping 19:63–78, 2003.

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.

Texture segregation is processed by primary visual cortex in man and monkey. Evidence from VEP experiments.

We investigated whether the process of texture segregation can be allocated to a specific visual cortical area. We designed a stimulus to reveal the presence of a mechanism, which is specifically sensitive to a checkerboard, that is solely defined by textures segregating due to orientation differences of the constituting line segments. We recorded evoked potentials to this stimulus in man and awake monkey. A difference component, signalling texture segregation sensitivity, could be recorded from both types of subjects. Its presence depended on the spatial extent of the textures, in a manner correlating with the perceptibility of the checkerboard. This difference response could be localized in primary visual cortex by means of equivalent dipole estimations.

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.

Multiple sclerosis patients show a highly significant decrease in alpha band interhemispheric synchronization measured using MEG.

MEG data were acquired from a group of relapsing-remitting multiple sclerosis (MS) patients and a group of healthy controls, using an eyes-closed no-task condition. An interhemispheric coherence measure (IHCM), reflecting the synchronization between the left and right hemispheres, showed a decrease in the patients, particularly in the alpha band. No comparable differences were seen in the alpha band power or its distribution over the head. The observed difference is in agreement with a reduced long-range connectivity in the brains of MS patients. The IHCM was found to be reproducible in controls over a period of more than 15 months. Further studies should investigate whether MEG derived synchronization measures may be useful as markers for MS disease load.

Resting-state oscillatory brain dynamics in Alzheimer disease.

Altered oscillatory brain activity in Alzheimer disease (AD) may reflect underlying neuropathological changes, and its characterization might lead to new diagnostic possibilities. The present study using quantitative magnetoencephalography was set up to examine power spectrum changes in AD patients, and their diagnostic strength. Whole-head 151-channel magnetoencephalography was recorded during an eyes-closed resting state. Magnetoencephalography channels were grouped in 10 cortical regions, and both global and regional relative power was analyzed for the commonly used frequency bands. Eighteen AD patients [mean age 72.1 years ± 5.6 (SD); 7 women; mean Mini Mental State Examination score 19.2, range: 13–25] and 18 healthy controls [mean age 69.1 ± 6.8 (SD), 11 women; mean Mini Mental State Examination score 29, range: 27–30] were recruited, controls being mainly spouses of patients. Relative power analysis showed significant differences in most frequency bands, particularly in the temporo-parietal regions, with some relation to Mini Mental State Examination scores. Greatest diagnostic accuracy was found in the beta band, especially in the right occipital area (sensitivity 94%, specificity 78%). Quantitative relative power analysis of magnetoencephalography recordings is able to show widespread abnormalities in oscillatory brain dynamics in AD patients. By analyzing distinct cortical regions, this study provides a more detailed topographical view of abnormal brain activity in AD.

Magnetoencephalography as a putative biomarker for Alzheimer's disease

Alzheimers Disease (AD) is the most common dementia in the elderly and is estimated to affect tens of millions of people worldwide. AD is believed to have a prodromal stage lasting ten or more years. While amyloid deposits, tau filaments, and loss of brain cells are characteristics of the disease, the loss of dendritic spines and of synapses predate such changes. Popular preclinical detection strategies mainly involve cerebrospinal fluid biomarkers, magnetic resonance imaging, metabolic PET scans, and amyloid imaging. One strategy missing from this list involves neurophysiological measures, which might be more sensitive to detect alterations in brain function. The Magnetoencephalography International Consortium of Alzheimers Disease arose out of the need to advance the use of Magnetoencephalography (MEG), as a tool in AD and pre-AD research. This paper presents a framework for using MEG in dementia research, and for short-term research priorities.

Self-organized dynamics in plastic neural networks: bistability and coherence

Abstract. In this paper, we study the combined dynamics of the neural activity and the synaptic efficiency changes in a fully connected network of biologically realistic neurons with simple synaptic plasticity dynamics including both potentiation and depression. Using a mean-field of technique, we analyzed the equilibrium states of neural networks with dynamic synaptic connections and found a class of bistable networks. For this class of networks, one of the stable equilibrium states shows strong connectivity and coherent responses to external input. In the other stable equilibrium, the network is loosely connected and responds non coherently to external input. Transitions between the two states can be achieved by positively or negatively correlated external inputs. Such networks can therefore switch between their phases according to the statistical properties of the external input. Non-coherent input can only “rcad” the state of the network, while a correlated one can change its state. We speculate that this property, specific for plastic neural networks, can give a clue to understand fully unsupervised learning models.

A healthy brain in a healthy body: brain network correlates of physical and mental fitness.

A healthy lifestyle is an important focus in todays society. The physical benefits of regular exercise are abundantly clear, but physical fitness is also associated with better cognitive performance. How these two factors together relate to characteristics of the brain is still incompletely understood. By applying mathematical concepts from ‘network theory’, insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network organization mediates the association between cardio respiratory fitness (i.e. VO2 max) and cognitive functioning. A healthy cohort was studied (n = 219, 113 women, age range 41–44 years). Subjects underwent resting-state eyes-closed magneto-encephalography (MEG). Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma). The phase lag index (PLI) was used as a measure of functional connectivity between all sensors. Modularity analysis was performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a maximum oxygen uptake (VO2 max) measurement as an indicator of cardio respiratory fitness. All subjects were tested with a commonly used Dutch intelligence test. Intelligence quotient (IQ) was related to VO2 max. In addition, VO2 max was negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the beta band was associated with higher VO2 max and IQ, further indicating a benefit of more global network integration as opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as opposed to within-module connections, is related to better physical and mental fitness.

Determination of individual stimulus-response curves in the visual cortex

Activation in the visual cortex is typically studied using group average changes in an on–off paradigm for a single flicker frequency. We used functional magnetic resonance imaging (fMRI) to characterize the stimulus–response curve in the visual cortex as a function of flicker frequency in individual subjects, using LED goggles with 17 frequency steps between 0 and 30 Hz. Ten healthy young individuals were studied on two different occasions (mean interval; 22 days). In all but one subject, a third‐order polynomial curve could be fitted to the data. From the response curve we calculated the peak response (the frequency where the response amplitude was maximal), the percentage change (relative difference) of the response amplitudes between 8 Hz and the peak frequency, and the average slope of response (towards the peak). On both occasions we could determine a peak response for each subject with small within‐subject variability. The average absolute difference in peak response between both sessions was 1.37 Hz (range, 0.2–4.3 Hz), indicating that the peak frequency is rather stable for a given individual. In conclusion, our study illustrates the ability of fMRI to examine the stimulus–response curve in individual subjects in the visual cortex. Based on our findings, the peak response and the slope of response seem highly reproducible within subjects. A similar analysis of the stimulus–response curve may be applicable to other types of stimuli. Hum. Brain Mapping 17:245–251, 2002.