Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness.

Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease.

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities.

Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease.

Objectives: Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics.

Materials And Methods: We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC).

Cerebrospinal fluid tau levels are associated with abnormal neuronal plasticity markers in Alzheimer's disease.

Background: Increased total tau (t-tau) in cerebrospinal fluid (CSF) is a key characteristic of Alzheimer's disease (AD) and is considered to result from neurodegeneration. T-tau levels, however, can be increased in very early disease stages, when neurodegeneration is limited, and can be normal in advanced disease stages. This suggests that t-tau levels may be driven by other mechanisms as well.

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline.

Individuals with prodromal Alzheimer's disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer's disease.

Tau-related grey matter network breakdown across the Alzheimer's disease continuum.

Background: Changes in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer's disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups.

Disentangling biologically distinct subgroups of Alzheimer's disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.

Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition.

Biomarkers are needed to monitor disease progression in Alzheimer's disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer's disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer's disease cognitive continuum and whether such changes relate to concurrent decline in cognition.

Cerebrospinal fluid total tau levels indicate aberrant neuronal plasticity in Alzheimer's disease.

Alzheimer's disease (AD) is characterised by abnormal amyloid beta and tau processing. Previous studies reported that cerebrospinal fluid (CSF) total tau (t-tau) levels vary between patients. Here we show that CSF t-tau variability is associated with distinct impairments in neuronal plasticity mediated by gene repression factors SUZ12 and REST.

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease.

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers.

Single-subject gray matter networks predict future cortical atrophy in preclinical Alzheimer's disease.

The development of preventive strategies in early-stage Alzheimer's disease (AD) requires measures that can predict future brain atrophy. Gray matter network measures are related to amyloid burden in cognitively normal older individuals and predict clinical progression in preclinical AD. Here, we show that within individuals with preclinical AD, gray matter network measures predict hippocampal atrophy rates, whereas other AD biomarkers (total gray matter volume, cerebrospinal fluid total tau, and Mini-Mental State Examination) do not.

Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship.

Objective: To investigate the relationship between cognitive reserve (CR) and clinical progression across the Alzheimer disease (AD) spectrum.

Methods: We selected 839 β-amyloid (Aβ)-positive participants with normal cognition (NC, n = 175), mild cognitive impairment (MCI, n = 437), or AD dementia (n = 227) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). CR was quantified using standardized residuals (W scores) from a (covariate-adjusted) linear regression with global cognition (13-item Alzheimer's Disease Assessment Scale-cognitive subscale) as an independent variable of interest, and either gray matter volumes or white matter hyperintensity volume as dependent variables.

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease.

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex.

Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease.

Background: Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy.

Impairment in complex activities of daily living is related to neurodegeneration in Alzheimer's disease-specific regions.

Impairment in instrumental activities of daily living (IADL) is an early clinical feature of Alzheimer's disease (AD). The neurobiology underlying IADL disruptions is still unclear. We aimed to investigate the relationship between IADL functioning and cortical atrophy across the AD spectrum.

Atrophy subtypes in prodromal Alzheimer's disease are associated with cognitive decline.

Alzheimer's disease is a heterogeneous disorder. Understanding the biological basis for this heterogeneity is key for developing personalized medicine. We identified atrophy subtypes in Alzheimer's disease dementia and tested whether these subtypes are already present in prodromal Alzheimer's disease and could explain interindividual differences in cognitive decline.

A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline.

Objectives: Grey matter network disruptions in Alzheimer's disease (AD) are associated with worse cognitive impairment cross-sectionally. Our aim was to investigate whether indications of a more random network organization are associated with longitudinal decline in specific cognitive functions in individuals with subjective cognitive decline (SCD).

Experimental Design: We included 231 individuals with SCD who had annually repeated neuropsychological assessment (3 ± 1 years; n = 646 neuropsychological investigations) available from the Amsterdam Dementia Cohort (54% male, age: 63 ± 9, MMSE: 28 ± 2).

Gray matter network measures are associated with cognitive decline in mild cognitive impairment.

Gray matter networks are disrupted in Alzheimer's disease and related to cognitive impairment. However, it is still unclear whether these disruptions are associated with cognitive decline over time. Here, we studied this question in a large sample of patients with mild cognitive impairment with extensive longitudinal neuropsychological assessments.

Neuroanatomical and Neuropsychological Markers of Amnestic MCI: A Three-Year Longitudinal Study in Individuals Unaware of Cognitive Decline.

Structural brain changes underlying mild cognitive impairment (MCI) have been well-researched, but most previous studies required subjective cognitive complaints (SCC) as a diagnostic criterion, diagnosed MCI based on a single screening test or lacked analyses in relation to neuropsychological impairment. This longitudinal voxel-based morphometry study aimed to overcome these limitations: The relationship between regional gray matter (GM) atrophy and behavioral performance was investigated over the course of 3 years in individuals unaware of cognitive decline, identified as amnestic MCI based on an extensive neuropsychological test battery. Region of interest analyses revealed GM atrophy in the left amygdala, hippocampus, and parahippocampus in MCI individuals compared to normally aging participants, which was specifically related to verbal memory impairment and evident already at the first measurement point.