Betty M. Tijms

NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

Single-Subject Grey Matter Graphs in Alzheimer's Disease

Coordinated patterns of cortical morphology have been described as structural graphs and previous research has demonstrated that properties of such graphs are altered in Alzheimers 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.

Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer's disease: an EEG study

We investigated whether the functional connectivity and network topology in 69 Alzheimers disease (AD), 48 behavioral variant of frontotemporal dementia (bvFTD) patients, and 64 individuals with subjective cognitive decline are different using resting-state electroencephalography recordings. Functional connectivity between all pairs of electroencephalography channels was assessed using the phase lag index (PLI). We subsequently calculated PLI-weighted networks, from which minimum spanning trees (MSTs) were constructed. Finally, we investigated the hierarchical clustering organization of the MSTs. Functional connectivity analysis showed frequency-dependent results: in the delta band, bvFTD showed highest whole-brain PLI; in the theta band, the whole-brain PLI in AD was higher than that in bvFTD; in the alpha band, AD showed lower whole-brain PLI compared with bvFTD and subjective cognitive decline. The MST results indicate that frontal networks appear to be selectively involved in bvFTD against the background of preserved global efficiency, whereas parietal and occipital loss of network organization in AD is accompanied by global efficiency loss. Our findings suggest different pathophysiological mechanisms in these 2 separate neurodegenerative disorders.

Disruption of structural and functional networks in long‐standing multiple sclerosis

Both gray matter atrophy and disruption of functional networks are important predictors for physical disability and cognitive impairment in multiple sclerosis (MS), yet their relationship is poorly understood. Graph theory provides a modality invariant framework to analyze patterns of gray matter morphology and functional coactivation. We investigated, how gray matter and functional networks were affected within the same MS sample and examined their interrelationship. Magnetic resonance imaging and magnetoencephalography (MEG) were performed in 102 MS patients and 42 healthy controls. Gray matter networks were computed at the group‐level based on cortical thickness correlations between 78 regions across subjects. MEG functional networks were computed at the subject level based on the phase‐lag index between time‐series of regions in source‐space. In MS patients, we found a more regular network organization for structural covariance networks and for functional networks in the theta band, whereas we found a more random network organization for functional networks in the alpha2 band. Correlation analysis revealed a positive association between covariation in thickness and functional connectivity in especially the theta band in MS patients, and these results could not be explained by simple regional gray matter thickness measurements. This study is a first multimodal graph analysis in a sample of MS patients, and our results suggest that a disruption of gray matter network topology is important to understand alterations in functional connectivity in MS as regional gray matter fails to take into account the inherent connectivity structure of the brain. Hum Brain Mapp 35:5946–5961, 2014.

Single-Subject Gray Matter Graph Properties and Their Relationship with Cognitive Impairment in Early- and Late-Onset Alzheimer's Disease

Abstract We investigated the relationships between gray matter graph properties and cognitive impairment in a sample of 215 patients with Alzheimers disease (AD) and also whether age of disease onset modifies such relationships. We expected that more severe cognitive impairment in AD would be related to more random graph topologies. Single-subject gray matter graphs were constructed from T1-weighted magnetic resonance imaging scans. The following global and local graph properties were calculated: betweenness centrality, normalized clustering coefficient γ, and normalized path length λ. Local clustering, path length, and betweenness centrality measures were determined for 90 anatomically defined areas. Regression models with as interaction term age of onset (i.e., early onset when patients were ≤65 years old and late onset when they were >65 years old at the time of diagnosis)×graph property were used to assess the relationships between cognitive functioning in five domains (memory, language, visuospatial, attention, and executive). Worse cognitive impairment was associated with more random graphs, as indicated by low γ, λ, and betweenness centrality values. Three interaction effects for age of onset×global graph property were found: Low γ and λ values more strongly related to memory impairment in early-onset patients; low beta values were significantly related to impaired visuospatial functioning in late-onset patients. For the local graph properties, language impairment showed the strongest relationship with decreased clustering coefficient in the left superior temporal gyrus across the entire sample. Our study shows that single-subject gray matter graph properties are associated with individual differences in cognitive impairment.

Gray matter network disruptions and amyloid beta in cognitively normal adults.

Gray matter networks are disrupted in Alzheimers disease (AD). It is unclear when these disruptions start during the development of AD. Amyloid beta 1-42 (Aβ42) is among the earliest changes in AD. We studied, in cognitively healthy adults, the relationship between Aβ42 levels in cerebrospinal fluid (CSF) and single-subject cortical gray matter network measures. Single-subject gray matter networks were extracted from structural magnetic resonance imaging scans in a sample of cognitively healthy adults (N = 185; age range 39-79, mini-mental state examination >25, N = 12 showed abnormal Aβ42 < 550 pg/mL). Degree, clustering coefficient, and path length were computed at whole brain level and for 90 anatomical areas. Associations between continuous Aβ42 CSF levels and single-subject cortical gray matter network measures were tested. Smoothing splines were used to determine whether a linear or nonlinear relationship gave a better fit to the data. Lower Aβ42 CSF levels were linearly associated at whole brain level with lower connectivity density, and nonlinearly with lower clustering values and higher path length values, which is indicative of a less-efficient network organization. These relationships were specific to medial temporal areas, precuneus, and the middle frontal gyrus (all p < 0.05). These results suggest that mostly within the normal spectrum of amyloid, lower Aβ42 levels can be related to gray matter networks disruptions.

Differential diagnosis of neurodegenerative diseases using structural MRI data.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimers disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimers disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimers disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

Widespread Disruption of Functional Brain Organization in Early-Onset Alzheimer's Disease

Early-onset Alzheimer’s disease (AD) patients present a different clinical profile than late-onset AD patients. This can be partially explained by cortical atrophy, although brain organization might provide more insight. The aim of this study was to examine functional connectivity in early-onset and late-onset AD patients. Resting-state fMRI scans of 20 early-onset (<65 years old), 28 late-onset (≥65 years old) AD patients and 15 “young” (<65 years old) and 31 “old” (≥65 years old) age-matched controls were available. Resting-state network-masks were used to create subject-specific maps. Group differences were examined using a non-parametric permutation test, accounting for gray-matter. Performance on five cognitive domains were used in a correlation analysis with functional connectivity in AD patients. Functional connectivity was not different in any of the RSNs when comparing the two control groups (young vs. old controls), which implies that there is no general effect of aging on functional connectivity. Functional connectivity in early-onset AD was lower in all networks compared to age-matched controls, where late-onset AD showed lower functional connectivity in the default-mode network. Functional connectivity was lower in early-onset compared to late-onset AD in auditory-, sensory-motor, dorsal-visual systems and the default mode network. Across patients, an association of functional connectivity of the default mode network was found with visuoconstruction. Functional connectivity of the right dorsal visual system was associated with attention across patients. In late-onset AD patients alone, higher functional connectivity of the sensory-motor system was associated with poorer memory performance. Functional brain organization was more widely disrupted in early-onset AD when compared to late-onset AD. This could possibly explain different clinical profiles, although more research into the relationship of functional connectivity and cognitive performance is needed.

Regional atrophy is associated with impairment in distinct cognitive domains in Alzheimer's disease.

In Alzheimers disease (AD), some patients present with cognitive impairment other than episodic memory disturbances. We evaluated whether occurrence of posterior atrophy (PA) and medial temporal lobe atrophy (MTA) could account for differences in cognitive domains affected.

The Association of Glucose Metabolism and Eigenvector Centrality in Alzheimer's Disease

Both fluorine-18-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography, examining glucose metabolism, and resting-state functional magnetic resonance imaging (rs-fMRI), using covarying blood oxygen levels, can be used to explore neuronal dysfunction in Alzheimers disease (AD). Both measures are reported to identify similar brain regions affected in AD patients. The spatial overlap and association of [(18)F]FDG with rs-fMRI in AD patients and controls were examined to investigate whether these two measures are associated, and if so, to what extent. For 24 AD patients and 18 controls, [(18)F]FDG and rs-fMRI data were available. [(18)F]FDG standardized uptake value ratios (SUVr), with cerebellar gray matter (GM) as reference tissue, were calculated. Eigenvector centrality (EC) mapping was used to spatially analyze the functional brain network. Group differences were calculated for [(18)F]FDG and eigenvector centrality mapping (ECM) values in four cortical regions (occipital, parietal, frontal, and temporal) and across voxels, with age, gender, and GM as covariates. Correlation of [(18)F]FDG with ECM was calculated within groups. Both lowered [(18)F]FDG SUVr and EC values were seen in the parietal and occipital cortex of AD patients. However, [(18)F]FDG yielded more robust and widespread brain areas affected in AD patients; hypometabolism was also observed in the temporal cortex and regions within frontal brain areas. Poor spatial overlap of both measures was observed. No associations were found between local [(18)F]FDG SUVr and ECM. In conclusion, agreement of [(18)F]FDG and ECM in AD patients seems moderate at best. [(18)F]FDG was most accurate in distinguishing AD patients from controls.