Alle Meije Wink

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

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 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.

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.

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

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

Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis

Objective: This study investigates whether changes in functional connectivity, diffusivity, and volume of the thalamus can explain different severities of cognitive impairment in multiple sclerosis (MS). Methods: An inception cohort of 157 patients with MS (104 women, mean age 41 years), 6 years postdiagnosis, was divided into 3 groups: cognitively preserved (CP, n = 108), mildly cognitively impaired (MCI, n = 22), and more severely cognitively impaired (SCI, n = 27). These groups were matched to 47 healthy controls (HC, 28 women, mean age 41 years). Thalamic volume, thalamic skeleton diffusivity (fractional anisotropy [FA] and mean diffusivity [MD]), and thalamic resting-state functional connectivity (FC) were compared between groups. Results: Thalamic volume was significantly lower in all patient groups compared to controls, with lowest volumes in patients with SCI, and no difference between CP and MCI. Thalamic skeleton FA was decreased in SCI compared to HC only; MD was increased in SCI compared to all other groups. Thalamic FC was increased in SCI with a total of 15 regions, mainly sensorimotor, frontal, and occipital parts of the brain. Thalamic volume, FC, and MD remained independent predictors in a linear regression model (R2 = 0.46), together with male sex and a lower level of education. Lesion and whole-brain volumes were not significant predictors. Conclusions: These findings indicate that thalamic changes in structure and function are highly informative regarding overall cognitive performance in MS. Increased thalamic FC only became apparent in SCI, possibly as a sign of maladaption.

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

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

Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis

Background: Cognitive dysfunction in multiple sclerosis (MS) has a large impact on the quality of life and is poorly understood. Objective: The aim of this study was to investigate functional network integrity in MS, and relate this to cognitive dysfunction and physical disability. Methods: Resting state fMRI scans were included of 128 MS patients and 50 controls. Eigenvector centrality mapping (ECM) was applied, a graph analysis technique that ranks the importance of brain regions based on their connectivity patterns. Significant ECM changes were related to physical disability and cognitive dysfunction. Results: In MS patients, ECM values were increased in bilateral thalamus and posterior cingulate (PCC) areas, and decreased in sensorimotor and ventral stream areas. Sensorimotor ECM decreases were related to higher EDSS (rho = −0.24, p = 0.007), while ventral stream decreases were related to poorer average cognition (rho = 0.23, p = 0.009). The thalamus displayed increased connectivity to sensorimotor and ventral stream areas. Conclusion: In MS, areas in the ventral stream and sensorimotor cortex appear to become less central in the entire functional network of the brain, which is associated with clinico-cognitive dysfunction. The thalamus, however, displays increased connectivity with these areas. These findings may aid in further elucidating the function of functional reorganization processes in MS.

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.

Amyloid and its association with default network integrity in Alzheimer's disease

The purpose of this study was to investigate the association between functional connectivity and β‐amyloid depositions in the default mode network (DMN) in Alzheimers disease (AD), patients with mild cognitive impairment (MCI), and healthy elderly. Twenty‐five patients with AD, 12 patients with MCI, and 18 healthy controls were included in the study. Resting‐state functional magnetic resonance imaging was used to assess functional connectivity in the DMN. In parallel, amyloid burden was measured in the same subjects using positron emission tomography with carbon‐11‐labeled Pittsburgh Compound‐B as amyloid tracer. Functional connectivity of the DMN and amyloid deposition within the DMN were not associated across all subjects or within diagnostic groups. Longitudinal studies are needed to examine if amyloid depositions precede aberrant functional connectivity in the DMN. Hum Brain Mapp 35:779–791, 2014.

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.

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.