Publications (14)68.42 Total impact
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Article: Alzheimer's disease patients not carrying the apolipoprotein E ε4 allele show more severe slowing of oscillatory brain activity.
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ABSTRACT: The objective of this study was to quantitatively assess the relationship between apolipoprotein (APOE) genotype and electroencephalographic oscillatory brain dynamics in Alzheimer's disease (AD) patients and control subjects and its regional distribution. We obtained resting-state electroencephalographs of 320 AD patients and 246 control subjects, categorized into APOE ε4 carriers and noncarriers. Peak frequency and relative power in 4 different frequency bands were calculated. We tested the associations between APOE genotype and relative power in 4 brain regions. Peak frequency was comparable in APOE ε4 carrying and noncarrying control subjects, but lower in APOE ε4 noncarrying AD patients. In control subjects, APOE ε4 carriers had a different regional distribution of alpha power than noncarriers. We found no APOE effect in beta, delta, and theta bands. In AD, APOE ε4 noncarriers had lower alpha and higher delta power than carriers. This difference was most pronounced in the parieto-occipital region. In the theta band, APOE ε4 noncarriers had a different regional distribution of power compared with carriers. In conclusion, the most pronounced effect of genotype was seen in AD patients, and APOE ε4 noncarriers showed slower activity, especially in parieto-occipital regions.Neurobiology of aging 04/2013; · 5.94 Impact Factor -
Dataset: PLoS ONE 2013 Tijms Supplementary Information
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Article: Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
Neurobiology of Aging 04/2013; · 6.19 Impact Factor -
Article: Alzheimer's disease: connecting findings from graph theoretical studies of brain networks.
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ABSTRACT: The interrelationships between pathological processes and emerging clinical phenotypes in Alzheimer's disease (AD) are important yet complicated to study, because the brain is a complex network where local disruptions can have widespread effects. Recently, properties in brain networks obtained with neuroimaging techniques have been studied in AD with tools from graph theory. However, the interpretation of graph alterations remains unclear, because the definition of connectivity depends on the imaging modality used. Here we examined which graph properties have been consistently reported to be disturbed in AD studies, using a heuristically defined "graph space" to investigate which theoretical models can best explain graph alterations in AD. Findings from structural and functional graphs point to a loss of highly connected areas in AD. However, studies showed considerable variability in reported group differences of most graph properties. This suggests that brain graphs might not be isometric, which complicates the interpretation of graph measurements. We highlight confounding factors such as differences in graph construction methods and provide recommendations for future research.Neurobiology of aging 03/2013; · 5.94 Impact Factor -
Article: Single-Subject Grey Matter Graphs in Alzheimer's Disease
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ABSTRACT: Coordinated patterns of cortical morphology have been described as structural graphs and previous research has demonstrated that properties of such graphs are altered in Alzheimer's disease (AD). However, it remains unknown how these alterations are related to cognitive deficits in individuals, as such graphs are restricted to group-level analysis. In the present study we investigated this question in single-subject grey matter networks. This new method extracts large-scale structural graphs where nodes represent small cortical regions that are connected by edges when they show statistical similarity. Using this method, unweighted and undirected networks were extracted from T1 weighted structural magnetic resonance imaging scans of 38 AD patients (19 female, average age 7264 years) and 38 controls (19 females, average age 7264 years). Group comparisons of standard graph properties were performed after correcting for grey matter volumetric measurements and were correlated to scores of general cognitive functioning. AD networks were characterised by a more random topology as indicated by a decreased small world coefficient (p = 3.53610 25), decreased normalized clustering coefficient (p = 7.25610 26) and decreased normalized path length (p = 1.91610 27). Reduced normalized path length explained significantly (p = 0.004) more variance in measurements of general cognitive decline (32%) in comparison to volumetric measurements (9%). Altered path length of the parahippocampal gyrus, hippocampus, fusiform gyrus and precuneus showed the strongest relationship with cognitive decline. The present results suggest that single-subject grey matter graphs provide a concise quantification of cortical structure that has clinical value, which might be of particular importance for disease prognosis. These findings contribute to a better understanding of structural alterations and cognitive dysfunction in AD. Citation: Tijms BM, Mö ller C, Vrenken H, Wink AM, de Haan W, et al. (2013) Single-Subject Grey Matter Graphs in Alzheimer's Disease. PLoS ONE 8(3): e58921. Copyright: ß 2013 Tijms et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was conducted at the VUmc Alzheimer center that is part of the neurodegeneration research program of the Neuroscience Campus Amsterdam. The VUmc Alzheimer center is supported by Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.PLoS ONE 03/2013; 8(3):e58921. · 4.09 Impact Factor -
Article: Single-Subject Grey Matter Graphs in Alzheimer's Disease.
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ABSTRACT: Coordinated patterns of cortical morphology have been described as structural graphs and previous research has demonstrated that properties of such graphs are altered in Alzheimer's disease (AD). However, it remains unknown how these alterations are related to cognitive deficits in individuals, as such graphs are restricted to group-level analysis. In the present study we investigated this question in single-subject grey matter networks. This new method extracts large-scale structural graphs where nodes represent small cortical regions that are connected by edges when they show statistical similarity. Using this method, unweighted and undirected networks were extracted from T1 weighted structural magnetic resonance imaging scans of 38 AD patients (19 female, average age 72±4 years) and 38 controls (19 females, average age 72±4 years). Group comparisons of standard graph properties were performed after correcting for grey matter volumetric measurements and were correlated to scores of general cognitive functioning. AD networks were characterised by a more random topology as indicated by a decreased small world coefficient (p = 3.53×10(-5)), decreased normalized clustering coefficient (p = 7.25×10(-6)) and decreased normalized path length (p = 1.91×10(-7)). Reduced normalized path length explained significantly (p = 0.004) more variance in measurements of general cognitive decline (32%) in comparison to volumetric measurements (9%). Altered path length of the parahippocampal gyrus, hippocampus, fusiform gyrus and precuneus showed the strongest relationship with cognitive decline. The present results suggest that single-subject grey matter graphs provide a concise quantification of cortical structure that has clinical value, which might be of particular importance for disease prognosis. These findings contribute to a better understanding of structural alterations and cognitive dysfunction in AD.PLoS ONE 01/2013; 8(3):e58921. · 4.09 Impact Factor -
Article: Activity dependent degeneration explains hub vulnerability in Alzheimer's disease.
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ABSTRACT: Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.PLoS Computational Biology 08/2012; 8(8):e1002582. · 5.22 Impact Factor -
Article: Disturbed oscillatory brain dynamics in subcortical ischemic vascular dementia.
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ABSTRACT: White matter hyperintensities (WMH) can lead to dementia but the underlying physiological mechanisms are unclear. We compared relative oscillatory power from electroencephalographic studies (EEGs) of 17 patients with subcortical ischemic vascular dementia, based on extensive white matter hyperintensities (SIVD-WMH) with 17 controls to investigate physiological changes underlying this diagnosis. Differences between the groups were large, with a decrease of relative power of fast activity in patients (alpha power 0.25 ± 0.12 versus 0.38 ± 0.13, p = 0.01; beta power 0.08 ± 0.04 versus 0.19 ± 0.07; p<0.001) and an increase in relative powers of slow activity in patients (theta power 0.32 ± 0.11 versus 0.14 ± 0.09; p<0.001 and delta power 0.31 ± 0.14 versus 0.23 ± 0.09; p<0.05). Lower relative beta power was related to worse cognitive performance in a linear regression analysis (standardized beta = 0.67, p<0.01). This pattern of disturbance in oscillatory brain activity indicate loss of connections between neurons, providing a first step in the understanding of cognitive dysfunction in SIVD-WMH.BMC Neuroscience 07/2012; 13:85. · 3.04 Impact Factor -
Article: Disruption of functional brain networks in Alzheimer's disease: what can we learn from graph spectral analysis of resting-state magnetoencephalography?
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ABSTRACT: In Alzheimer's disease (AD), structural and functional brain network organization is disturbed. However, many of the present network analysis measures require a priori assumptions and methodological choices that influence outcomes and interpretations. Graph spectral analysis (GSA) is a more direct algebraic method that describes network properties, which might lead to more reliable results. In this study, GSA was applied to magnetoencephalography (MEG) data to explore functional network integrity in AD. Sensor-level resting-state MEG was performed in 18 Alzheimer patients (age 67 ± 9, 6 women) and 18 healthy controls (age 66 ± 9, 11 women). Weighted, undirected graphs were constructed based on functional connectivity analysis using the Synchronization likelihood, and GSA was performed with a focus on network connectivity, synchronizability, and node centrality. The main outcomes were a global loss of network connectivity and altered synchronizability in most frequency bands. Eigenvector centrality mapping confirmed the hub status of the parietal areas, and demonstrated a low centrality of the left temporal region in the theta band in AD patients that was strongly related to the mini mental state examination (global cognitive function test) score (r=0.67, p=0.001). Summarizing, GSA is a theoretically solid approach that is able to detect the disruption of functional network topology in AD. In addition to the previously reported overall connectivity losses and parietal area hub status, impaired network synchronizability and a clinically relevant left temporal centrality loss were found in AD patients. Our findings imply that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.Brain connectivity. 04/2012; 2(2):45-55. -
Article: Young Alzheimer patients show distinct regional changes of oscillatory brain dynamics.
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ABSTRACT: 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). 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.Neurobiology of aging 11/2011; 33(5):1008.e25-31. · 5.94 Impact Factor -
Article: Functional network disruption in the degenerative dementias.
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ABSTRACT: Despite advances towards understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and neuropathological changes to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is crucial for normal functioning. A better understanding of network disruption in the neurodegenerative dementias might help bridge the gap between molecular changes, pathological changes, and symptoms. Recent findings on functional network disruption as assessed with resting-state or intrinsic connectivity functional MRI and electroencephalography and magnetoencephalography have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are somewhat specific to the clinical syndromes and, in Alzheimer's disease and frontotemporal dementia, network disruption tracks the pattern of pathological changes. These findings might have practical implications for diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the presymptomatic stage, and tracking of disease progression.The Lancet Neurology 09/2011; 10(9):829-43. · 23.46 Impact Factor -
Article: Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory.
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ABSTRACT: Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5-4 Hz, 4-8 Hz, 8-10 Hz, 10-13 Hz, 13-30 Hz and 30-45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks. In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01). With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.BMC Neuroscience 08/2009; 10:101. · 3.04 Impact Factor -
Article: Abolitionism and the Politics of ‘Bad Conscience’
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ABSTRACT: Image and reality of criminal justice in The Netherlands differ to a considerable degree. The causes for the relatively mild penal climate in Holland have to be taken into account, if a potential successful strategy to preserve that climate is to be developed. The discussion about the merits of abolitionism, currently held within the Dutch League for Penal Reform, is dealt with in detail.The Howard Journal of Criminal Justice 01/2009; 26(1):15 - 32. -
Article: Resting-state oscillatory brain dynamics in Alzheimer disease.
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ABSTRACT: 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.Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society 09/2008; 25(4):187-93. · 1.47 Impact Factor
Top co-authors
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Institutions
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2008–2013
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VU medisch centrum
- Department of Clinical Neurophysiology
Amsterdam, North Holland, Netherlands
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2009
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VU University Amsterdam
Amsterdam, North Holland, Netherlands
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