During the past decade, a conceptual shift occurred in the field of Alzheimer's disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this "silent" stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

BackgroundWhite matter hyperintensities (WMH), lacunes and microbleeds are regarded as typical MRI expressions of cerebral small vessel disease (SVD) and they are highly prevalent in the elderly. However, clinical expression of MRI defined SVD is generally moderate and heterogeneous. By reviewing studies that directly correlated postmortem MRI and histopathology, this paper aimed to characterise the pathological substrates of SVD in order to create more understanding as to its heterogeneous clinical manifestation.SummaryPostmortem studies showed that WMH are also heterogeneous in terms of histopathology. Damage to the tissue ranges from slight disentanglement of the matrix to varying degrees of myelin and axonal loss. Glial cell responses include astrocytic reactions—for example, astrogliosis and clasmatodendrosis—as well as loss of oligodendrocytes and distinct microglial responses. Lipohyalinosis, arteriosclerosis, vessel wall leakage and collagen deposition in venular walls are recognised microvascular changes. Suggested pathogenetic mechanisms are ischaemia/hypoxia, hypoperfusion due to altered cerebrovascular autoregulation, blood–brain barrier leakage, inflammation, degeneration and amyloid angiopathy. Only a few postmortem MRI studies have addressed lacunes and microbleeds to date. Cortical microinfarcts and changes in the normal appearing white matter are ‘invisible’ on conventional MRI but are nevertheless expected to contribute substantially to clinical symptoms.ConclusionPathological substrates of WMH are heterogeneous in nature and severity, which may partly explain the weak clinicoradiological associations found in SVD. Lacunes and microbleeds have been relatively understudied and need to be further investigated. Future studies should also take into account ‘MRI invisible’ SVD features and consider the use of, for example, quantitative MRI techniques, to increase the sensitivity of MRI for these abnormalities and study their effects on clinical functioning.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.information flow | phase transfer entropy | resting-state networks | magnetoencephalography | atlas-based beamforming T he brain is an extremely complex system (1-3) containing, at the macroscopic scale, interconnected functional units (4) with more-or-less specific information processing capabilities (5). However, cognitive functions require the coordinated activity of these spatially separated units, where the oscillatory nature of neuronal activity may provide a possible mechanism (6-9). A complete description of these interactions, in terms of both strength and directionality, is therefore necessary for the understanding of both normal and abnormal brain functioning.Functional interactions may be inferred from statistical dependencies between the time series of neuronal activity at different sites, so-called functional connectivity (10). Indeed, interactions in large-scale functional networks have been observed using Electroencephalography, Magnetoencephalography (EEG/MEG) and functional Magnetic Resonance Imaging (fMRI) (e.g., refs. 11-14). However, as yet, little is known about the directionality of these interactions in large-scale functional networks during the resting state. Estimating directionality from fMRI is challenging due to its limited temporal resolution and indirect relation to neuronal activity (15, 16). In contrast, EEG studies in healthy controls have revealed a front-to-back pattern of directed connectivity, particularly in the alpha band (17-22), consistent with modeling studies that have shown that such patterns may arise due to differences in the number of anatomical connections (the degree) of anterior and posterior regions (22, 23). However, modeled patterns of information flow depend on the a...

on behalf of the LADIS Study GroupBackground and Purpose-We studied the natural course of white matter hyperintensities (WMH) and lacunes, the main MRI representatives of small vessel disease, over time and evaluated possible predictors for their development. Methods-Baseline and repeat MRI (3-year follow-up) were collected within the multicenter, multinational Leukoaraiosis and Disability study (nϭ396). Baseline WMH were scored on MRI by the Fazekas scale and the Scheltens scale. WMH progression was assessed using the modified Rotterdam Progression scale (absence/presence of progression in 9 brain regions). Baseline and new lacunes were counted per region. WMH and lacunes at baseline and vascular risk factors were evaluated as predictors of WMH progression and new lacunes. Results-WMH progressed (meanϮSDϭ1.9Ϯ1.8) mostly in the subcortical white matter, where WMH was also most prevalent at baseline. The majority of new lacunes, which were found in 19% of the subjects (maximumϭ9), also appeared in the subcortical white matter, mainly of the frontal lobes, whereas most baseline lacunes were located in the basal ganglia. Baseline WMH and lacunes predicted both WMH progression and new lacunes. Furthermore, previous stroke, diabetes, and blood glucose were risk factors for WMH progression. Male sex, hypertension, systolic blood pressure, previous stroke, body mass index, high-density lipoprotein, and triglyceride levels were risk factors for new lacunes. Conclusion-WMH and lacunes progressed over time, predominantly in the subcortical white matter. Progression was observed especially in subjects with considerable WMH and lacunes at baseline. Moreover, the presence of vascular risk factors at baseline predicted WMH progression and new lacunes over a 3-year period.

White matter hyperintensities (WMH) are frequently seen on T(2)-weighted MRI scans of elderly subjects with and without Alzheimer's disease. WMH are only weakly and inconsistently associated with cognitive decline, which may be explained by heterogeneity of the underlying neuropathological substrates. The use of quantitative MRI could increase specificity for these neuropathological changes. We assessed whether post-mortem quantitative MRI is able to reflect differences in neuropathological correlates of WMH in tissue samples obtained post-mortem from Alzheimer's disease patients and from non-demented elderly. Thirty-three formalin-fixed, coronal brain slices from 11 Alzheimer's disease patients (mean age: 83 +/- 10 years, eight females) and 15 slices from seven non-demented controls (mean age: 78 +/- 10 years, four females) with WMH were scanned at 1.5 T using qualitative (fluid-attenuated inversion recovery, FLAIR) and quantitative MRI [diffusion tensor imaging (DTI) including estimation of apparent diffusion coefficient (ADC) and fractional anisotropy (FA), and T(1)-relaxation time mapping based on flip-angle array). A total of 104 regions of interest were defined on FLAIR images in WMH and normal appearing white matter (NAWM). Neuropathological examination included (semi-)quantitative assessment of axonal density (Bodian), myelin density (LFB), astrogliosis (GFAP) and microglial activation (HLA-DR). Patient groups (Alzheimer's disease versus controls) and tissue types (WMH versus NAWM) were compared with respect to QMRI and neuropathological measures. Overall, Alzheimer's disease patients had significantly lower FA (P < 0.01) and higher T(1)-values than controls (P = 0.04). WMH showed lower FA (P < 0.01) and higher T(1)-values (P < 0.001) than NAWM in both patient groups. A significant interaction between patient group and tissue type was found for the T(1) measurements, indicating that the difference in T(1)-relaxation time between NAWM and WMH was larger in Alzheimer's disease patients than in non-demented controls. All neuropathological measures showed differences between WMH and NAWM, although the difference in microglial activation was specific for Alzheimer's disease. Multivariate regression models revealed that in Alzheimer's disease, axonal density was an independent determinant of FA, whereas T(1) was independently determined by axonal and myelin density and microglial activation. Quantitative MRI techniques reveal differences in WMH between Alzheimer's disease and non-demented elderly, and are able to reflect the severity of the neuropathological changes involved.

Incident lacunes on MRI parallel a steeper rate of decline in executive functions and psychomotor speed. Accordingly, in addition to WML, lacunes determine longitudinal cognitive impairment in small vessel disease. Although the individual contribution of lacunes on cognition was modest, they cannot be considered benign findings, but indicate a risk of progressive cognitive impairment.

Background: Although heart failure (HF) is a common cardiovascular disorder, to date little research has been conducted into possible associations between HF and structural abnormalities of the brain. Aims: To determine the frequency and pattern of magnetic resonance imaging (MRI) abnormalities in outpatients with chronic HF, and to identify any demographic and clinical correlates. Methods: Brain MRI scans were compared between a sample of 58 HF patients, 48 controls diagnosed with cardiovascular disease uncomplicated by HF (cardiac controls) and 42 healthy controls. Deep, periventricular and total white matter hyperintensities (WMH), lacunar and cortical infarcts, global and medial temporal lobe atrophy (MTA) were investigated. Results: Compared to cardiac and healthy controls, HF patients had significantly more WMH, lacunar infarcts and MTA, whereas cardiac controls only had more MTA, compared to healthy controls. Age and left ventricular ejection fraction (LVEF) were independently associated with total WMH. Age and systolic hypotension were associated with MTA in HF patients and cardiac controls. Conclusion: Our results suggest that cardiac dysfunction contributes independently to the development of cerebral MRI abnormalities in patients with HF. Age and low LVEF are the principal predictors of cerebral WMH in patients with HF and in cardiac controls.

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-β42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease.