Cornelis J Stam

Publications of Cornelis J Stam

• Cognitive decline in Parkinson's disease is associated with slowing of resting-state brain activity: a longitudinal study.

  Authors: Kim T E Olde Dubbelink, Diederick Stoffers, Jan Berend Deijen, Jos W R Twisk, Cornelis J Stam, Henk W Berendse

  Neurobiology of aging. 04/2012;

  The pathophysiological mechanisms of Parkinson's disease (PD)-related dementia (PDD) are still poorly understood. Previous studies using electroencephalography (EEG) and magnetoencephalography (MEG)The pathophysiological mechanisms of Parkinson's disease (PD)-related dementia (PDD) are still poorly understood. Previous studies using electroencephalography (EEG) and magnetoencephalography (MEG) have demonstrated widespread slowing of oscillatory brain activity as a neurophysiological characteristic of PD-related dementia. Here, we use MEG to longitudinally study early changes in oscillatory brain activity in initially nondemented PD patients that may be associated with cognitive decline. Using a longitudinal design, resting-state MEG recordings were performed twice at an approximate 4-year interval in 14 healthy controls and 49 PD patients. Changes in peak frequency and in relative spectral power for 10 brain regions were analyzed in relation to clinical measures of cognitive and motor function. In contrast to healthy controls, PD patients showed a slowing of the dominant peak frequency. Furthermore, analysis per frequency band revealed an increase in theta power over time, along with decreases in alpha1 and alpha2 power. In PD patients, decreasing cognitive performance was associated with increases in delta and theta power, as well as decreases in alpha1, alpha2, and gamma power, whereas increasing motor impairment was associated with a theta power increase only. The present longitudinal study revealed widespread progressive slowing of oscillatory brain activity in initially nondemented PD patients, independent of aging effects. The slowing of oscillatory brain activity strongly correlated with cognitive decline and therefore holds promise as an early marker for the development of dementia in PD.

• The brain matures with stronger functional connectivity and decreased randomness of its network.

  Authors: Dirk J A Smit, Maria Boersma, Hugo G Schnack, Sifis Micheloyannis, Dorret I Boomsma, Hilleke E Hulshoff Pol, Cornelis J Stam, Eco J C de Geus

  PloS one. 01/2012; 7(5):e36896.

  We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. ConnectivityWe investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998) graph parameters C (local clustering) and L (global path length) for alpha (∼10 Hz), beta (∼20 Hz), and theta (∼4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ∼50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ∼18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.

• Complexity analysis of resting-state MEG activity in early-stage Parkinson's disease patients.

  Authors: Carlos Gómez, Kim T E Olde Dubbelink, Cornelis J Stam, Daniel Abásolo, Henk W Berendse, Roberto Hornero

  Annals of biomedical engineering. 12/2011; 39(12):2935-44.

  The aim of the present study was to analyze resting-state brain activity in patients with Parkinson's disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signalsThe aim of the present study was to analyze resting-state brain activity in patients with Parkinson's disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel-Ziv complexity (LZC), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects' recordings. We found significant group differences (p-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of LZC results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed. LZC results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of LZC to detect an abnormal type of dynamics associated with PD.

• Young Alzheimer patients show distinct regional changes of oscillatory brain dynamics.

  Authors: Hanneke de Waal, Cornelis J Stam, Willem de Haan, Elisabeth C W van Straaten, Philip Scheltens, Wiesje M van der Flier

  Neurobiology of aging. 11/2011;

  The objective of this study was to examine the differences in oscillatory brain dynamics in Alzheimer's disease (AD) according to age at onset using quantitative electroencephalography (EEG). WeThe objective of this study was to examine the differences in oscillatory brain dynamics in Alzheimer's disease (AD) according to age at onset using quantitative electroencephalography (EEG). We examined resting state electroencephalograms of 320 probable AD patients and 246 controls, both categorized into a young (≤ 65 years) and old (> 65 years) group. Relative power in 4 different frequency bands was calculated. The effect of age on global and regional relative power was examined. Globally, young AD patients showed lower alpha- and higher delta-power than old AD patients. Regional analysis showed that these differences were most pronounced in the parieto-occipital region. Young AD patients had lower beta- and higher theta-power than old patients in all but the temporal regions. In controls, there was no age effect on global relative power in any frequency band. Young AD patients present with more severe slowing of spontaneous oscillatory activity than old AD patients, which is most pronounced in the posterior brain areas. This finding supports the hypothesis that early onset AD presents with a distinct endophenotype.

• Does sleep restore the topology of functional brain networks?

  Authors: Maria M G Koenis, Nico Romeijn, Giovanni Piantoni, Ilse Verweij, Ysbrand D Van der Werf, Eus J W Van Someren, Cornelis J Stam

  Human brain mapping. 11/2011;

  Previous studies have shown that healthy anatomical as well as functional brain networks have small-world properties and become less optimal with brain disease. During sleep, the functional brainPrevious studies have shown that healthy anatomical as well as functional brain networks have small-world properties and become less optimal with brain disease. During sleep, the functional brain network becomes more small-world-like. Here we test the hypothesis that the functional brain network during wakefulness becomes less optimal after sleep deprivation (SD). Electroencephalography (EEG) was recorded five times a day after a night of SD and after a night of normal sleep in eight young healthy subjects, both during eyes-closed and eyes-open resting state. Overall synchronization was determined with the synchronization likelihood (SL) and the phase lag index (PLI). From these coupling strength matrices the normalized clustering coefficient C (a measurement of local clustering) and path length L (a measurement of global integration) were computed. Both measures were normalized by dividing them by their corresponding C-s and L-s values of random control networks. SD reduced alpha band C/C-s and L/L-s and theta band C/C-s during eyes-closed resting state. In contrast, SD increased gamma-band C/C-s and L/L-s during eyes-open resting state. Functional relevance of these changes in network properties was suggested by their association with sleep deprivation-induced performance deficits on a sustained attention simple reaction time task. The findings indicate that SD results in a more random network of alpha-coupling and a more ordered network of gamma-coupling. The present study shows that SD induces frequency-specific changes in the functional network topology of the brain, supporting the idea that sleep plays a role in the maintenance of an optimal functional network. Hum Brain Mapp, 2011. © 2011 Wiley Periodicals, Inc.

• Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution.

  Authors: Arjan Hillebrand, Gareth R Barnes, Johannes L Bosboom, Henk W Berendse, Cornelis J Stam

  NeuroImage. 11/2011; 59(4):3909-21.

  The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units.The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach. Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI). Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected. Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.

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• Functional connectivity changes in multiple sclerosis patients: A graph analytical study of MEG resting state data.

  Authors: Menno M Schoonheim, Jeroen J G Geurts, Doriana Landi, Linda Douw, Marieke L van der Meer, Hugo Vrenken, Chris H Polman, Frederik Barkhof, Cornelis J Stam

  Human brain mapping. 09/2011;

  Multiple sclerosis (MS) is characterized by extensive damage in the central nervous system. Within this field, there is a strong need for more advanced, functional imaging measures, as abnormalitiesMultiple sclerosis (MS) is characterized by extensive damage in the central nervous system. Within this field, there is a strong need for more advanced, functional imaging measures, as abnormalities measured with structural imaging insufficiently explain clinicocognitive decline in MS. In this study we investigated functional connectivity changes in MS using resting-state magnetoencephalography (MEG). Data from 34 MS patients and 28 age and gender-matched controls was assessed using synchronization likelihood (SL) as a measure of functional interaction strength between brain regions, and graph analysis to characterize topological patterns of connectivity changes. Cognition was assessed using extensive neuropsychological evaluation. Structural measures included brain and lesion volumes, using MRI. Results show SL increases in MS patients in theta, lower alpha and beta bands, with decreases in the upper alpha band. Graph analysis revealed a more regular topology in the lower alpha band in patients, indicated by an increased path length (λ) and clustering coefficient (γ). Attention and working memory domains were impaired, with decreased brain volumes. A stepwise linear regression model using clinical, MRI and MEG parameters as predictors revealed that only increases in lower alpha band γ predicted impaired cognition. Cognitive impairments and related altered connectivity patterns were found to be especially predominant in male patients. These results show specific functional changes in MS as measured with MEG. Only changes in network topology were related to poorer cognitive outcome. This indicates the value of graph analysis beyond traditional structural and functional measures, with possible implications for diagnostic and/or prognostic purposes in MS. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

• Gender-related differences in functional connectivity in multiple sclerosis.

  Authors: Menno M Schoonheim, Hanneke E Hulst, Doriana Landi, Olga Ciccarelli, Stefan D Roosendaal, Ernesto J Sanz-Arigita, Hugo Vrenken, Chris H Polman, Cornelis J Stam, Frederik Barkhof, Jeroen Jg Geurts

  Multiple sclerosis (Houndmills, Basingstoke, England). 09/2011; 18(2):164-73.

  Background: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contributeBackground: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contribute to limit disability, and possible gender differences in this process may have important clinical implications. Objectives: The aim of this study was to explore gender-related changes in functional connectivity and network efficiency in MS patients. Additionally, we explored the association of functional changes with cognitive function. Methods: Sixty subjects were included in the study, matched for age, education level and intelligence quotient (IQ). Male and female patients were matched for disability, disease duration and white matter lesion load. Two cognitive domains often impaired in MS, i.e. visuospatial memory and information processing speed, were evaluated in all subjects. Functional connectivity between brain regions and network efficiency was explored using resting-state functional magnetic resonance imaging and graph analysis. Differences in cognitive and functional characteristics between groups, and correlations with cognitive performance, were examined. Results: Male patients showed worse performance on cognitive tests than female and male controls, while female patients were cognitively normal. Decreases in functional connectivity and network efficiency, observed in male patients, correlated with reduced visuospatial memory (r = -0.6 and r = -0.5, respectively). In the control group, no cognitive differences were found between genders, despite differences in functional connectivity between healthy men and women. Conclusions: Functional connectivity differences were found in male patients only and were related to impaired visuospatial memory. These results underline the importance of gender in MS and require further investigation in larger and longitudinal studies.

• Resting-state networks in awake five- to eight-year old children.

  Authors: Henrica M A de Bie, Maria Boersma, Sofie Adriaanse, Dick J Veltman, Alle Meije Wink, Stefan D Roosendaal, Frederik Barkhof, Cornelis J Stam, Kim J Oostrom, Henriette A Delemarre-van de Waal, Ernesto J Sanz-Arigita

  Human brain mapping. 04/2011;

  During the first 6-7 years of life children undergo a period of major neurocognitive development. Higher-order cognitive functions such as executive control of attention, encoding and retrieving ofDuring the first 6-7 years of life children undergo a period of major neurocognitive development. Higher-order cognitive functions such as executive control of attention, encoding and retrieving of stored information and goal-directed behavior are present but less developed compared to older individuals. There is only very limited information from functional magnetic resonance imaging (fMRI) studies about the level of organization of functional networks in children in the early school period. In this study we perform continuous resting-state functional connectivity MRI in 5- to 8-year-old children in an awake state to identify and characterize resting-state networks (RSNs). Temporal concatenation independent component analysis (ICA) approach was applied to analyze the data. We identified 14 components consisting of regions known to be involved in visual and auditory processing, motor function, attention control, memory, and the default mode network (DMN). Most networks, in particular those supporting basic motor function and sensory related processing, had a robust functional organization similar to mature adult patterns. In contrast, the DMN and other RSNs involved in higher-order cognitive functions had immature characteristics, revealing incomplete and fragmented patterns indicating less developed functional connectivity. We therefore conclude that the DMN and other RSNs involved in higher order cognitive functioning are detectable, yet in an immature state, at an age when these cognitive abilities are mastered. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

• Epilepsy with central spikes provoked by fever with a benign disease course.

  Authors: Maaike J Vos, Cornelis J Stam, Hanneke E Ronner, Nicole I Wolf

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 04/2011; 122(10):2110-2.

• Network analysis of resting state EEG in the developing young brain: structure comes with maturation.

  Authors: Maria Boersma, Dirk J A Smit, Henrica M A de Bie, G Caroline M Van Baal, Dorret I Boomsma, Eco J C de Geus, Henriette A Delemarre-van de Waal, Cornelis J Stam

  Human brain mapping. 03/2011; 32(3):413-25.

  During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combineDuring childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths. We used graph theoretical concepts to examine changes in functional brain networks during normal development in young children. Resting-state eyes-closed electroencephalography (EEG) was recorded (14 channels) from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) was calculated in three different frequency bands and between each pair of electrodes to obtain SL-weighted graphs. Mean normalized clustering index, average path length and weight dispersion were calculated to characterize network organization. Repeated measures analysis of variance tested for time and gender effects. For all frequency bands mean SL decreased from 5 to 7 years. Clustering coefficient increased in the alpha band. Path length increased in all frequency bands. Mean normalized weight dispersion decreased in beta band. Girls showed higher synchronization for all frequency bands and a higher mean clustering in alpha and beta bands. The overall decrease in functional connectivity (SL) might reflect pruning of unused synapses and preservation of strong connections resulting in more cost-effective networks. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small-world functional networks. This developmental process is influenced by gender differences early in development.

• EEG abnormalities are associated with different cognitive profiles in Alzheimer's disease.

  Authors: Lieke L Smits, Maarten Liedorp, Teddy Koene, Ilona E W Roos-Reuling, Afina W Lemstra, Philip Scheltens, Cornelis J Stam, Wiesje M van der Flier

  Dementia and geriatric cognitive disorders. 01/2011; 31(1):1-6.

  Our purpose was to investigate associations between different cognitive profiles and their underlying functional brain changes as measured by electroencephalogram (EEG) in Alzheimer's diseaseOur purpose was to investigate associations between different cognitive profiles and their underlying functional brain changes as measured by electroencephalogram (EEG) in Alzheimer's disease (AD). EEG was obtained and neuropsychological performance assessed in 254 patients with AD. The EEGs were visually assessed for the presence of focal and/or diffuse abnormalities. Multivariate analysis of variance for repeated measures was performed with presence of focal and/or diffuse abnormalities as between-subjects factor and neuropsychological tests as within-subject factor. Age, sex and education were entered as covariates. Twenty-eight percent of the patients had a normal EEG, 32% had focal abnormalities, 14% diffuse abnormalities and 26% had both focal and diffuse abnormalities. Patients with a normal EEG presented with a cognitive profile in which memory was mostly impaired. Patients with focal and diffuse EEG abnormalities presented with a nonmemory profile. These results illustrate that specific types of EEG abnormalities are associated with different cognitive profiles in AD, providing biological support in terms of brain functioning for variability in cognitive impairment.

• Data-driven modeling of phase interactions between spontaneous MEG oscillations.

  Authors: Rikkert Hindriks, Fetsje Bijma, Bob W van Dijk, Cornelis J Stam, Ysbrand Y van der Werf, Eus J W van Someren, Jan C de Munck, Aad W van der Vaart

  Human brain mapping. 01/2011; 32(7):1161-78.

  Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate theSynchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. The methodology is tested using simulations and is applied to ongoing occipital-frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital-frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking.

• Is disturbed intracortical excitability a stable trait of chronic insomnia? A study using transcranial magnetic stimulation before and after multimodal sleep therapy.

  Authors: Ysbrand D van der Werf, Ellemarije Altena, Karin D van Dijk, Rob L M Strijers, Wim De Rijke, Cornelis J Stam, Eus J W van Someren

  Biological psychiatry. 11/2010; 68(10):950-5.

  Chronic insomnia is a poorly understood disorder. Risk factors for developing chronic insomnia are largely unknown, yet disturbances in brain indexes of arousal seem to accompany the disorder. WeChronic insomnia is a poorly understood disorder. Risk factors for developing chronic insomnia are largely unknown, yet disturbances in brain indexes of arousal seem to accompany the disorder. We here investigate whether insomnia patients and control participants differ with respect to brain responses to direct stimulation, i.e., cortical excitability. Transcranial magnetic stimulation (TMS) offers a method to directly investigate the excitability level of the human cerebral cortex in psychiatric and neurological disease. We investigated cortical excitability in 16 insomnia patients and 14 carefully matched control participants using absolute and relative amplitudes of motor evoked potentials in response to single- and paired-pulse stimulation using TMS. Nonmedicated insomnia patients showed, first, an exaggerated absolute response to both suprathreshold single- and paired-pulse stimulation compared with control participants and second, a reduced relative response to paired-pulse stimulation at long interpulse intervals (i.e., a reduced intracortical facilitation). The abnormal excitability persisted despite sleep therapy that effectively improved sleep quality as well as behavioral and neuroimaging indexes of brain function. The results suggest that a subtly disturbed intracortical excitability characterizes patients with chronic insomnia: a relatively reduced intracortical facilitation in the context of a globally increased absolute excitability. The findings do not resemble TMS findings after sleep deprivation or in sleep apnea and thus seem specific to insomnia. They may offer diagnostic value and implications for assessment of risk to develop this common and disabling disorder.

• EEG abnormalities in early and late onset Alzheimer's disease: understanding heterogeneity.

  Authors: Hanneke de Waal, Cornelis J Stam, Marinus A Blankenstein, Yolande A L Pijnenburg, Philip Scheltens, Wiesje M van der Flier

  Journal of neurology, neurosurgery, and psychiatry. 10/2010; 82(1):67-71.

  To compare differences in severity and type of electroencephalography (EEG) abnormalities between early and late onset Alzheimer's disease (AD) and to assess the influence of APOE genotype on thisTo compare differences in severity and type of electroencephalography (EEG) abnormalities between early and late onset Alzheimer's disease (AD) and to assess the influence of APOE genotype on this association, in order to understand the biological differences in AD according to age at onset Of 460 probable AD patients and 336 patients with subjective complaints, serving as controls, EEG and APOE genotype were obtained. Subjects were categorised by age into a younger (≤65 years) and an older group (>65 years), based on age at diagnosis. Severity and type of EEG abnormalities were visually assessed. Severity of EEG abnormalities ranged from normal to slightly abnormal to moderately severe. EEG abnormalities were characterised as only focal abnormalities, only diffuse abnormalities or both focal and diffuse abnormalities. Logistic regression revealed that younger AD patients more often had EEG abnormalities, which were more severe, with a predominance of both focal and diffuse abnormalities. In controls, we observed the opposite, as older controls more often had EEG abnormalities than younger controls. Furthermore, APOE ε4 negative AD patients had more severe EEG abnormalities than APOE ε4 positive AD patients, while no such effect was observed in controls. There was no interaction between age at onset and APOE ε4 genotype. Early onset and APOE ε4 negative AD patients present with more severe EEG abnormalities than late onset and APOE ε4 positive AD patients. These results suggest that in younger patients, AD manifests with more prominent functional brain changes.

• Comparing brain networks of different size and connectivity density using graph theory.

  Authors: Bernadette C M van Wijk, Cornelis J Stam, Andreas Daffertshofer

  PloS one. 01/2010; 5(10):e13701.

  Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties.Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

• Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity.

  Authors: Ernesto J Sanz-Arigita, Menno M Schoonheim, Jessica S Damoiseaux, Serge A R B Rombouts, Erik Maris, Frederik Barkhof, Philip Scheltens, Cornelis J Stam

  PloS one. 01/2010; 5(11):e13788.

  Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show moreLocal network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data. 18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions. We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

• Topographical organization of mu and Beta band activity associated with hand and foot movements in patients with perirolandic lesions.

  Authors: Ronald B Willemse, Jan C de Munck, Jeroen P A Verbunt, Dennis van 't Ent, Peterjan Ris, Johannes C Baayen, Cornelis J Stam, W Peter Vandertop

  The open neuroimaging journal. 01/2010; 4:93-9.

  To study the topographical organization of mu and beta band event-related desynchronization (ERD) associated with voluntary hand and foot movements, we used magnetoencephalographic (MEG) recordingsTo study the topographical organization of mu and beta band event-related desynchronization (ERD) associated with voluntary hand and foot movements, we used magnetoencephalographic (MEG) recordings from 19 patients with perirolandic lesions. Synthetic aperture magnetometry (SAM) was used to detect and localize changes in the mu (7 - 11 Hz) and beta (13 - 30 Hz) frequency bands associated with repetitive movements of the hand and foot and overlaid on individual coregistered magnetic resonance (MR) images. Hand movements showed homotopic and contralateral ERD at the sensorimotor (S/M) cortex in the majority of cases for mu and to a lesser extent for beta rhythms. Foot movements showed an increased heterotopic distribution with bilateral and ipsilateral ERD compared to hand movements. No systematic topographical segregation between mu and beta ERD could be observed. In patients with perirolandic lesions, the mu and beta band spatial characteristics associated with hand movements retain the expected functional-anatomical boundaries to a large extent. Foot movements have altered patterns of mu and beta band ERD, which may give more insight into the differential functional role of oscillatory activity in different voluntary movements.

• 'Functional connectivity' is a sensitive predictor of epilepsy diagnosis after the first seizure.

  Authors: Linda Douw, Marjolein de Groot, Edwin van Dellen, Jan J Heimans, Hanneke E Ronner, Cornelis J Stam, Jaap C Reijneveld

  PloS one. 01/2010; 5(5):e10839.

  Although epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but theAlthough epilepsy affects almost 1% of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30-50%. Here we investigate whether using 'functional connectivity' can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy. Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. > or = two seizures) were compared to matched non-epilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76% and sensitivity of 62%. Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy, especially in those patients who do not show IEDs on their first EEG. Our results indicate that epilepsy diagnosis could be improved by using functional connectivity.

• Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients.

  Authors: Linda Douw, Edwin van Dellen, Marjolein de Groot, Jan J Heimans, Martin Klein, Cornelis J Stam, Jaap C Reijneveld

  BMC neuroscience. 01/2010; 11:103.

  Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band.Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length. At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology. These results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.