Sofie Adriaanse

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.

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.

Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort.

BACKGROUND Two approaches are available for measuring Alzheimers disease (AD) pathology in vivo. Biomarkers in cerebrospinal fluid (CSF) include amyloid-β1-42 (Aβ42) and tau. Furthermore, amyloid deposition can be visualized using positron emission tomography (PET) and [11C]Pittsburgh compound-B ([11C]PIB). OBJECTIVE We investigated concordance between CSF biomarkers and [11C]PIB PET as markers for AD pathology in a memory clinic cohort. METHODS We included 64 AD patients, 34 non-AD dementia patients, 22 patients with mild cognitive impairment (MCI), and 16 controls. [11C]PIB scans were visually rated as positive or negative. CSF biomarkers were considered abnormal based on Aβ42 alone (<550 ng/L), a more lenient Aβ42 cut-off (<640 ng/L) or a combination of both Aβ42 and tau ((373 + 0.82 tau)/Aβ42 > 1). Concordance between CSF biomarkers and [11C]PIB PET was determined. RESULTS Overall, concordance between [11C]PIB PET and CSF Aβ42 (<550 ng/L) was 84%. In discordant cases, [11C]PIB PET was more often AD-positive than Aβ42. When a more lenient Aβ42 cut-point (<640 ng/L) or a combination of Aβ42 and tau was used, concordance with [11C]PIB PET appeared to be even higher (90% and 89%). This difference is explained by a subgroup of mostly MCI and AD patients with Aβ42 levels just above cut-off. Now, in discordant cases, CSF was more often AD-positive than [11C]PIB PET. CONCLUSION Concordance between CSF Aβ42 and [11C]PIB PET was good in all diagnostic groups. Discordance was mostly seen in MCI and AD patients close to the cut-point. These results provide convergent validity for the use of both types of biomarkers as measures of AD pathology.

Long-term effects of amyloid, hypometabolism, and atrophy on neuropsychological functions.

Objective: To assess how amyloid deposition, glucose hypometabolism, and cerebral atrophy affect neuropsychological performance in patients with Alzheimer disease (AD) dementia, patients with mild cognitive impairment (MCI), and controls over time. Methods: A total of 41 patients with AD dementia, 28 patients with MCI, and 19 controls underwent [11C]–Pittsburgh compound B (11C-PiB) and [18F]-2-fluoro-2-deoxy-d-glucose (18F-FDG)–PET and MRI scans at baseline. We extracted global binding potential for 11C-PiB, the number of abnormal voxels for 18F-FDG, and gray matter volumes using SIENAX for MRI as measures of amyloid, hypometabolism, and atrophy. In addition, repeat neuropsychological testing was performed, including memory, attention, language, and executive tasks (mean follow-up 2.2 ± 0.7 years). Cross-sectional and longitudinal relationships between imaging markers and cognition were assessed using linear mixed models, including terms for the imaging markers, time, sex, age, diagnosis, and interactions for imaging marker × time and imaging marker × time × diagnosis. Results: Linear mixed models showed that baseline hypometabolism and atrophy were associated with poorer baseline performance on attention and executive functions (p < 0.05), whereas amyloid was not related to baseline cognition. Hypometabolism and amyloid were strongly associated with longitudinal decline in essentially all cognitive domains (pinteraction < 0.05), whereas atrophy was related specifically to future decline in Mini-Mental State Examination and memory (pinteraction < 0.05). Conclusion: Glucose hypometabolism and brain atrophy were associated with concurrent cognitive function, whereas brain amyloid was not. Amyloid deposition and glucose hypometabolism were predictors for decline of a wide variety of cognitive functions, while brain atrophy specifically predicted memory deterioration.

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.

Hypometabolism of the posterior cingulate cortex is not restricted to Alzheimer's disease

pathology) and [F]FDG (glucose metabolism) PET. Methods: PET scans were performed in 5 posterior cortical atrophy (PCA), 4 logopenic variant primary progressive aphasia (lvPPA), 1 early-onset AD and 2 (memory-predominant) late-onset AD patients (all PIB+) and in 19 cognitively normal controls (Table 1). We created SUVr images for [F]AV1451 (80-100 minutes, gray matter cerebellum as reference region) and [F]FDG (3060 minutes, pons-normalized), and DVR images for [C]PIB (090 minutes, gray matter cerebellum as reference region). We visually assessed [F]AV1451, [C]PIB and [F]FDG uptake patterns in 3 distinct AD variants, and performed voxel-wise contrasts (in SPM) between PCA patients and controls. Results: Figure 1 shows asymmetric [F]AV1451 uptake in parietal, temporal and frontal regions (left>right) in a patient with lvPPA, a classical temporoparietal pattern in a patient with memory-predominant AD, and mainly occipitotemporal and occipitoparietal involvement in a PCA patient. [F]AV1451 and [F]FDG appeared strikingly as mirror images, with regions of high [F] AV1451 uptake corresponding to low [F]FDG uptake and vice versa, while [C]PIB binding was observed throughout the association neocortex. Voxelwise contrasts with [F] AV1451 and [F]FDG showed that PCA patients significantly differed from controls in clinically affected posterior brain regions, while [C]PIB binding was greater in both posterior regions and in clinically less affected anterior regions (Figure 2). Conclusions: [F]AV1451 was specifically retained in brain regions closely related to the clinical presentation across distinct AD variants and overlapped substantially with hypometabolic regions in PCA, while [C]PIB binding was more diffuse and showed less overlap with [F]FDG uptake. This provides preliminary in-vivo evidence that hypometabolism and symptomatology are more closely linked to tau than to Ab pathology.

Concordance between CSF biomarkers and [11C]PiB-PET in a memory clinic population

Background:Microbleeds (MBs) and superficial siderosis (SS) are thought to be related to cerebral amyloid angiopathy (CAA). To assess the relation between these CAA markers and amyloid load in vivo and blood-brain barrier (BBB) permeability we used quantitative [11 C]PIB-PET in Alzheimer disease (AD) patients.Methods: We included eight AD patients with MBs or SS (17 MBs and 5 SS areas (CAA+ group)) and five AD patients without (CAAgroup) on 1.5T SWI. Dynamic 90minute [11 C]PIB-PET scans were acquired on the HR+ PET camera with arterial blood sampling to generate metabolite corrected input curves. Data were analysed with a validated two tissue compartment model. We assessed the non-displaceable binding potential (BP ND), reflecting specific amyloid binding, and the K1/k2 ratio, reflecting BBB permeability, on three levels; (i) local volumes of interest (VOIs) drawn manually around MBs/SS and copied contra-laterally serving as control regions, (ii) regional VOIs and (iii) global cortical VOIs.Results: Locally, BP ND did not differ, but K1/k2 ratios were higher in grey matter CAA+ VOIs than in contra-lateral control VOIs (median (range) 2.30(1.33.3) versus 2.13(1.3-2.9); p<0.05), suggesting locally increased BBB permeability. Regional or global VOIs however, showed no differences in BP ND (global: CAA+1⁄42.29(1.3-8.4), CAA-1⁄42.65(1.3-5.1); p>0.05) or K1/ k2 ratio (global: CAA+1⁄42.22(1.3-2.8) and CAA-1⁄42.19(1.7-2.7); p>0.05). Conclusions: Instead of BP ND,K1/k2 ratios surrounding MBs and SS were higher, suggesting that MRI signs of CAA in AD may relate to locally increased BBB permeability, rather than increased amyloid deposition. This has important implications for the use of [11 C]PIB scans in the diagnosis of CAA.

CONCORDANCE OF [18F]FLUTEMETAMOL AMYLOID DEPOSITION IN COGNITIVELY HEALTHY ELDERLY MONOZYGOTIC TWIN PAIRS

Elles Konijnenberg, Anouk den Braber, Mara ten Kate, Sofie Adriaanse, Maqsood M. Yaqub, Dorret I. Boomsma, Philip Scheltens, Bart N.M. van Berckel, Pieter Jelle Visser, Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands; 2 Department of Biological Psychology, VU University, Amsterdam, Netherlands; Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands; VU University Medical Center, Amsterdam, Netherlands; 5 Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands. Contact e-mail: e.konijnenberg@vumc.nl