Aging Effect, Reproducibility, and Test-Retest Reliability of a New Cerebral Amyloid Angiopathy MRI Severity Marker-Cerebrovascular Reactivity to Visual Stimulation.

BACKGROUND: Decreased cerebrovascular reactivity, measured as changes in blood-oxygen-level-dependent (BOLD) signal, is a potential new cerebral amyloid angiopathy (CAA) severity marker. Before clinical application, the effect of aging on BOLD parameters, and reproducibility and test-retest reliability of these parameters should be assessed. PURPOSE: Assess the effect of healthy aging on cerebrovascular reactivity (BOLD amplitude, time to peak, and time to baseline). And determine reproducibility and test-retest reliability of these parameters. STUDY TYPE: Prospective-observational. POPULATION: Eighty-six healthy adults (mean age 56 years, 55% female), 10 presymptomatic D-CAA mutation carriers (mean age 34 years, 70% female), and 10 symptomatic D-CAA mutation carriers (mean age 54 years, 70% female). FIELD STRENGTH/SEQUENCE: 3-T, three-dimensional (3D) T1-weighted MRI and gradient echo BOLD fMRI. ASSESSMENT: To assess test-retest reliability of BOLD parameters, i.e. BOLD amplitude, time to peak, and time to baseline, BOLD fMRI scans were repeated three times immediately after each other, in both controls and mutation carriers. To assess reproducibility, BOLD fMRI scans were repeated with a 3-week interval for each subject. STATISTICAL TESTS: Linear regression analyses and two-way mixed absolute agreement intra-class correlation approach. RESULTS: Healthy aging was associated with decreased BOLD amplitude (ß = -0.711) and prolonged time to baseline (ß = 0.236) in the visual cortex after visual stimulation Reproducibility of BOLD amplitude was excellent (ICC 0.940) in the subgroup of healthy adults. Test-retest reliability for BOLD amplitude was excellent in healthy adults (ICC 0.856-0.910) and presymptomatic D-CAA mutation carriers (ICC 0.959-0.981). In symptomatic D-CAA mutation carriers, test-retest reliability was poor for all parameters (ICCs < 0.5). DATA CONCLUSION: Healthy aging is associated with decreased cerebrovascular reactivity, measured by changes in BOLD response to visual stimulation. The BOLD amplitude appears to be a robust measurement in healthy adults and presymptomatic D-CAA mutation carriers, but not in symptomatic D-CAA mutation carriers.

Effect of NOTCH3 EGFr Group, Sex, and Cardiovascular Risk Factors on CADASIL Clinical and Neuroimaging Outcomes.

BACKGROUND: A retrospective study has shown that EGFr (epidermal growth factor-like repeat) group in the NOTCH3 gene is an important cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) disease modifier of age at first stroke and white matter hyperintensity (WMH) volume. No study has yet assessed the effect of other known CADASIL modifiers, that is, cardiovascular risk factors and sex, in the context of NOTCH3 EGFr group. In this study, we determined the relative disease-modifying effects of NOTCH3 EGFr group, sex and cardiovascular risk factor on disease severity in the first genotype-driven, large prospective CADASIL cohort study, using a comprehensive battery of CADASIL clinical outcomes and neuroimaging markers. METHODS: Patients with CADASIL participated in a single-center, prospective cohort study (DiViNAS [Disease Variability in NOTCH3 Associated Small Vessel Disease]) between 2017 and 2020. The study protocol included a clinical assessment, neuropsychological test battery and brain magnetic resonance imaging on a single research day. Multivariable linear, logistic and Cox regression models were used to cross-sectionally assess the effect of CADASIL modifiers on clinical severity (stroke, disability, processing speed) and neuroimaging markers (WMH volume, peak width of skeletonized mean diffusivity, lacune volume, brain volume, cerebral microbleed count). RESULTS: Two hundred patients with CADASIL participated, of which 103 harbored a NOTCH3 EGFr 1-6 variant and 97 an EGFr 7-34 variant. NOTCH3 EGFr 1-6 group was the most important modifier of age at first stroke (hazard ratio, 2.45 [95% CI, 1.39-4.31]; P=0.002), lacune volume (odds ratio, 4.31 [95% CI, 2.31-8.04]; P=4.0×10-6), WMH volume (B=0.81 [95% CI, 0.60-1.02]; P=1.1×10-12), and peak width of skeletonized mean diffusivity (B=0.65 [95% CI, 0.44-0.87]; P=1.6×10-8). EGFr 1-6 patients had a significantly higher WMH volume in the anterior temporal lobes and superior frontal gyri and a higher burden of enlarged perivascular spaces. After NOTCH3 EGFr group, male sex and hypertension were the next most important modifiers of clinical outcomes and neuroimaging markers. CONCLUSIONS: NOTCH3 EGFr group is the most important CADASIL disease modifier not only for age at first stroke and WMH volume but also strikingly so for a whole battery of clinically relevant disease measures such as lacune volume and peak width of skeletonized mean diffusivity. NOTCH3 EGFr group is followed in importance by sex, hypertension, diabetes, and smoking.

Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain.

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS-signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.

Brain activity and connectivity changes in response to nutritive natural sugars, non-nutritive natural sugar replacements and artificial sweeteners.

INTRODUCTION: The brain plays an important regulatory role in directing energy homeostasis and eating behavior. The increased ingestion of sugars and sweeteners over the last decades makes investigating the effects of these substances on the regulatory function of the brain of particular interest. We investigated whole brain functional response to the ingestion of nutrient shakes sweetened with either the nutritive natural sugars glucose and fructose, the low- nutritive natural sugar replacement allulose or the non-nutritive artificial sweetener sucralose. METHODS: Twenty healthy, normal weight, adult males underwent functional MRI on four separate visits. In a double-blind randomized study setup, participants received shakes sweetened with glucose, fructose, allulose or sucralose. Resting state functional MRI was performed before and after ingestion. Changes in Blood Oxygen Level Dependent (BOLD) signal, functional network connectivity and voxel based connectivity by Eigenvector Centrality Mapping (ECM) were measured. RESULTS: Glucose and fructose led to significant decreased BOLD signal in the cingulate cortex, insula and the basal ganglia. Glucose led to a significant increase in eigen vector centrality throughout the brain and a significant decrease in eigen vector centrality in the midbrain. Sucralose and allulose had no effect on BOLD signal or network connectivity but sucralose did lead to a significant increase in eigen vector centrality values in the cingulate cortex, central gyri and temporal lobe. DISCUSSION: Taken together our findings show that even in a shake containing fat and protein, the type of sweetener can affect brain responses and might thus affect reward and satiety responses and feeding behavior. The sweet taste without the corresponding energy content of the non-nutritive sweeteners appeared to have only small effects on the brain. Indicating that the while ingestion of nutritive sugars could have a strong effect on feeding behavior, both in a satiety aspect as well as rewarding aspects, non-nutritive sweeteners appear to not have these effects. TRIAL REGISTRATION: This study is registered at clinicaltrials.gov under number NCT02745730.

Structural and functional changes of the visual cortex in early Huntington's disease.

Huntington's disease (HD) is an autosomal-dominant inherited neurodegenerative disorder characterized by motor disturbances, psychiatric disturbances, and cognitive impairment. Visual cognitive deficits and atrophy of the posterior cerebral cortex are additionally present in early disease stages. This study aimed to assess the extent of structural and functional brain alterations of the visual cortex in HD gene carriers using different neuroimaging modalities. Structural and functional magnetic resonance imaging data were acquired from 18 healthy controls, 21 premanifest, and 20 manifest HD gene carriers. Voxel-based morphometry (VBM) analysis and cortical thickness measurements were performed to assess structural changes in the visual cortex. Brain function was measured by assessing neuronal connectivity changes in response to visual stimulation and at rest in visual resting-state networks. Multiple linear regression analyses were performed to examine the relationship between visual cognitive function and structural imaging measures. Compared to controls, pronounced atrophy and decreased neuronal function at rest were present in associative visual cortices in manifest HD. The primary visual cortex did not show group differences in cortical thickness and in vascular activity after visual stimulation. Thinning of the associative visual cortex was related to worse visual perceptual function. Premanifest HD gene carriers did not show any differences in brain structure or function compared to controls. This study improves the knowledge on posterior brain changes in HD, as our findings suggest that the primary visual cortex remains preserved, both structurally and functionally, while atrophy of associative visual cortices is present in early HD and linked to clinical visual deficits.

Combining multiple anatomical MRI measures improves Alzheimer's disease classification.

Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. © 2016 Wiley Periodicals, Inc.

Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease.

Disease-specific patterns of gray matter atrophy in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) overlap with distinct structural covariance networks (SCNs) in cognitively healthy controls. This suggests that both types of dementia target specific structural networks. Here, we study SCNs in AD and bvFTD. We used structural magnetic resonance imaging data of 31 AD patients, 24 bvFTD patients, and 30 controls from two centers specialized in dementia. Ten SCNs were defined based on structural covariance of gray matter density using independent component analysis. We studied group differences in SCNs using F-tests, with Bonferroni corrected t-tests, adjusted for age, gender, and study center. Associations with cognitive performance were studied using linear regression analyses. Cross-sectional group differences were found in three SCNs (all P < 0.0025). In bvFTD, we observed decreased anterior cingulate network integrity compared with AD and controls. Patients with AD showed decreased precuneal network integrity compared with bvFTD and controls, and decreased hippocampal network and anterior cingulate network integrity compared with controls. In AD, we found an association between precuneal network integrity and global cognitive performance (P = 0.0043). Our findings show that AD and bvFTD target different SCNs. The comparison of both types of dementia showed decreased precuneal (i.e., default mode) network integrity in AD and decreased anterior cingulate (i.e., salience) network integrity in bvFTD. This confirms the hypothesis that AD and bvFTD have distinct anatomical networks of degeneration and shows that structural covariance gives valuable insights in the understanding of network pathology in dementia.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

Imaging the default mode network in aging and dementia.

Although in the last decade brain activation in healthy aging and dementia was mainly studied using task-activation fMRI, there is increasing interest in task-induced decreases in brain activity, termed deactivations. These deactivations occur in the so-called default mode network (DMN). In parallel a growing number of studies focused on spontaneous, ongoing 'baseline' activity in the DMN. These resting state fMRI studies explored the functional connectivity in the DMN. Here we review whether normal aging and dementia affect task-induced deactivation and functional connectivity in the DMN. The majority of studies show a decreased DMN functional connectivity and task-induced DMN deactivations along a continuum from normal aging to mild cognitive impairment and to Alzheimer's disease (AD). Even subjects at risk for developing AD, either in terms of having amyloid plaques or carrying the APOE4 allele, showed disruptions in the DMN. While fMRI is a useful tool for detecting changes in DMN functional connectivity and deactivation, more work needs to be conducted to conclude whether these measures will become useful as a clinical diagnostic tool in AD. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Neurovascular coupling in early stage dementia - A case-control study.

Cerebral amyloid angiopathy (CAA) is frequently found post mortem in Alzheimer's dementia, but often undetected during life especially since in vivo hallmarks of CAA and its vascular damage become overt relatively late in the disease process. Decreased neurovascular coupling to visual stimulation has been put forward as an early MRI marker for CAA disease severity. The current study investigates the role of neurovascular coupling in AD related dementia and its early stages. We included 25 subjective cognitive impairment, 33 mild cognitive impairment and 17 dementia patients and 44 controls. All participants underwent magnetic resonance imaging of the brain and neuropsychological assessment. Univariate general linear modeling analyses were used to assess neurovascular coupling between patient groups and controls. Moreover, linear regression analyses was used to assess the associations between neurovascular coupling and cognition. Our data show that BOLD amplitude is lower in dementia (mean 0.8 ± 0.2, p = 0.001) and MCI patients (mean 0.9 ± 0.3, p = 0.004) compared with controls (mean 1.1 ± 0.2). A low BOLD amplitude was associated with low scores in multiple cognitive domains. We conclude that cerebrovascular dysfunction, most likely due CAA, is an important comorbidity in early stages of dementia and has an independent effect on cognition.

Regional Structural Hippocampal Differences Between Dementia with Lewy Bodies and Parkinson's Disease.

BACKGROUND: Dementia with Lewy bodies (DLB) and Parkinson's disease (PD) are considered subtypes of the α-synucleinopathy continuum that show similar and dissimilar clinical and morphological features. OBJECTIVE: To further our understanding of brain abnormalities that might differentiate both disorders more clearly, we performed quantitative magnetic resonance (MR) imaging of the subcortical and cortical grey matter. METHODS: Three-dimensional T1 weighted 3 tesla MR images of 14 DLB and 62 age- and gender-matched PD patients were examined to study cortical and subcortical grey matter structure. We used volumetric measurements to study total grey matter, and volumes of the pallidum, amygdala, putamen, caudate nucleus, thalamus and hippocampus. Whole-brain and structural network-based methods were used to identify local differences in grey matter and vertex-based shape analysis was used to assess focal hippocampal changes. RESULTS: Volumetric, whole-brain and network-based analyses showed reduced hippocampal (p = 0.008) and right parahippocampal region volumes (p = 0.030) in DLB compared to PD patients. Shape analysis showed atrophy in the head and body of the right (p = 0.040) and in the head of the left (p = 0.030) hippocampus of DLB patients. CONCLUSION: DLB patients showed atrophy of the hippocampus and parahippocampal gyrus compared to PD patients with a differential involvement of the head and body of the hippocampus. Further studies should examine if these group-based findings can be used to differentiate both disorders on an individual level.

Age- and disease-related cerebral white matter changes in patients with Parkinson's disease.

As age and Parkinson's disease (PD) both play a role in the degeneration of brain white matter, we aimed to investigate a possible interaction effect of age and disease presence on white matter integrity in patients with PD. We studied white matter hyperintensity volume, global fractional anisotropy, mean diffusivity and mean magnetization transfer ratio of normal appearing white matter in 163 patients with PD and 218 age- and gender-matched healthy control subjects. We investigated the relationship between age and these parameters in both groups, and interaction between age and disease presence. Patients with PD had a higher load of white matter hyperintensities with a preferential periventricular and anterior distribution as compared with healthy control subjects. Visuospatial functioning was related to total and postural instability and gait difficulty was related to periventricular white matter hyperintensity volume in patients with PD. The age-related decline of white matter integrity was similar for both groups. Global microstructural integrity of the normal appearing white matter did not differ between patients and healthy control subjects, suggesting that PD-specific changes do not exceed normal age-associated change in white matter without lesions.

Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison.

Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients (n = 107) with control subjects (n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

BACKGROUND/OBJECTIVE: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease.

BACKGROUND/OBJECTIVE: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. We applied longitudinal resting state functional magnetic resonance imaging (fMRI) to delineate functional brain connections relevant for disease progression and diagnostic accuracy. METHODS: We used two-center resting state fMRI data of 20 AD patients (65.1±8.0 years), 12 bvFTD patients (64.7±5.4 years), and 22 control subjects (63.8±5.0 years) at baseline and 1.8-year follow-up. We used whole-network and voxel-based network-to-region analyses to study group differences in functional connectivity at baseline and follow-up, and longitudinal changes in connectivity within and between groups. RESULTS: At baseline, connectivity between paracingulate gyrus and executive control network, between cuneal cortex and medial visual network, and between paracingulate gyrus and salience network was higher in AD compared with controls. These differences were also present after 1.8 years. At follow-up, connectivity between angular gyrus and right frontoparietal network, and between paracingulate gyrus and default mode network was lower in bvFTD compared with controls, and lower compared with AD between anterior cingulate gyrus and executive control network, and between lateral occipital cortex and medial visual network. Over time, connectivity decreased in AD between precuneus and right frontoparietal network and in bvFTD between inferior frontal gyrus and left frontoparietal network. Longitudinal changes in connectivity between supramarginal gyrus and right frontoparietal network differ between both patient groups and controls. CONCLUSION: We found disease-specific brain regions with longitudinal connectivity changes. This suggests the potential of longitudinal resting state fMRI to delineate regions relevant for disease progression and for diagnostic accuracy, although no group differences in longitudinal changes in the direct comparison of AD and bvFTD were found.

Early grey matter changes in structural covariance networks in Huntington's disease.

BACKGROUND: Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. OBJECTIVES: We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. METHODS: Structural magnetic resonance imaging data of premanifest HD (n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. RESULTS: Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p < 0.001, in pre-HD p = 0.003). One other network contained the hippocampus, premotor, sensorimotor, and insular cortices (in HD p < 0.001, in pre-HD p = 0.023). Additionally, in HD patients only, decreased network integrity was observed in a network including the lingual gyrus, intracalcarine, cuneal, and lateral occipital cortices (p = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions. CONCLUSION: Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease.

We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical thinning per year. Decrease of thickness over time was highest in AD and generalized throughout the whole brain, most pronounced posteriorly, whereas bvFTD patients had a more selective loss in frontal cortex and in anterior parts of the temporal lobes. In a direct comparison, AD patients showed faster cortical thinning in the insula, temporal, and parietal regions, whereas bvFTD patients only showed faster cortical thinning in the orbitofrontal gyrus. Decline of cognitive performances was in line with cortical thinning and deteriorated the most in AD patients.

Cerebral blood flow in presymptomatic MAPT and GRN mutation carriers: A longitudinal arterial spin labeling study.

OBJECTIVE: Frontotemporal dementia (FTD) is characterized by behavioral disturbances and language problems. Familial forms can be caused by genetic defects in microtubule-associated protein tau (MAPT), progranulin (GRN), and C9orf72. In light of upcoming clinical trials with potential disease-modifying agents, the development of sensitive biomarkers to evaluate such agents in the earliest stage of FTD is crucial. In the current longitudinal study we used arterial spin labeling MRI (ASL) in presymptomatic carriers of MAPT and GRN mutations to investigate early changes in cerebral blood flow (CBF). METHODS: Healthy first-degree relatives of patients with a MAPT or GRN mutation underwent ASL at baseline and follow-up after two years. We investigated cross-sectional and longitudinal differences in CBF between mutation carriers (n = 34) and controls without a mutation (n = 31). RESULTS: GRN mutation carriers showed significant frontoparietal hypoperfusion compared with controls at follow-up, whereas we found no cross-sectional group differences in the total study group or the MAPT subgroup. Longitudinal analyses revealed a significantly stronger decrease in CBF in frontal, temporal, parietal, and subcortical areas in the total group of mutation carriers and the GRN subgroup, with the strongest decrease in two mutation carriers who converted to clinical FTD during follow-up. INTERPRETATION: We demonstrated longitudinal alterations in CBF in presymptomatic FTD independent of grey matter atrophy, with the strongest decrease in individuals that developed symptoms during follow-up. Therefore, ASL could have the potential to serve as a sensitive biomarker of disease progression in the presymptomatic stage of FTD in future clinical trials.