Search

The role of microglia in tau accumulation is currently unclear but could provide an important insight into the mechanisms underlying Alzheimer’s disease (AD)1. Here, we measured the microglial marker soluble TREM2 and the disease-associated microglial activation stage 2 markers AXL, MERTK, GAS6, LPL, CST7, SPP1 and CSF1 in nondemented individuals from the Swedish BioFINDER-2 cohort who underwent longitudinal tau-positron emission tomography (PET), amyloid-PET and global cognitive assessment. To assess whether baseline microglial markers had an effect on AD-related changes, we studied three sub-groups of individuals: 121 with evidence of amyloid-PET pathology (A+), 64 with additional evidence of tau-PET pathology (A+T+) and 159 without amyloid- or tau-PET pathology (A−T−). Our results showed that increased levels of TREM2 were associated with slower amyloid accumulation in A+ individuals in addition to slower tau deposition and cognitive decline in A+T+ subjects. Similarly, higher levels of AXL, MERTK, GAS6, LPL, CST7 and CSF1 predicted slower tau accumulation and/or cognitive decline in the A+T+ group. These findings have important implications for future therapeutic strategies aiming to boost microglial protective functions in AD.

The locus coeruleus (LC) is an important noradrenergic nucleus that has recently attracted a lot of attention because of its emerging role in cognitive and psychiatric disorders. Although previous histological studies have shown that the LC has heterogeneous connections and cellular features, no studies have yet assessed its functional topography in vivo, how this heterogeneity changes over aging and whether it is associated with cognition and mood. Here we employ a gradient-based approach to characterize the functional heterogeneity in the organization of the LC over aging using 3T resting-state fMRI in a population-based cohort aged from 18 to 88 years old (Cambridge Centre for Ageing and Neuroscience cohort, n=618). We show that the LC exhibits a rostro-caudal functional gradient along its longitudinal axis, which was replicated in an independent dataset (Human Connectome Project 7T dataset, n=184). Although the main rostro-caudal direction of this gradient was consistent across age groups, its spatial features varied with increasing age, emotional memory and emotion regulation. More specifically, a loss of rostral-like connectivity, more clustered functional topography and greater asymmetry between right and left LC gradients was associated with higher age and worse behavioral performance. Furthermore, participants with higher-than-normal Hospital Anxiety and Depression Scale ratings exhibited alterations in the gradient as well, which manifested in greater asymmetry. These results provide an in vivo account of how the functional topography of the LC changes over aging, and imply that spatial features of this organization are relevant markers of LC-related behavioral measures and psychopathology.

Background Alzheimer’s disease is a neurodegenerative disorder associated with the abnormal deposition of pathological processes, such as amyloid-ß and tau, which produces nonlinear changes in the functional connectivity patterns between different brain regions across the Alzheimer’s disease continuum. However, the mechanisms underlying these nonlinear changes remain largely unknown. Here, we address this question using a novel method based on temporal or delayed correlations and calculate new whole-brain functional networks to tackle these mechanisms. Methods To assess our method, we evaluated 166 individuals from the ADNI database, including amyloid-beta negative and positive cognitively normal subjects, patients with mild cognitive impairment, and patients with Alzheimer’s disease dementia. We used the clustering coefficient and the global efficiency to measure the functional network topology and assessed their relationship with amyloid and tau pathology measured by positron emission tomography, as well as cognitive performance using tests measuring memory and global cognition. Results Our study found nonlinear changes in the global efficiency, but not in the clustering coefficient, showing that the nonlinear changes in functional connectivity are due to an altered ability of brain regions to communicate with each other through direct paths. These changes were most prominent in early disease stages, while later stages of Alzheimer’s disease were associated with more widespread network disruptions characterized by additional clustering changes. Both network measures, i.e., global efficiency and clustering, showed quadratic associations with pathological amyloid and tau burden as well as cognitive decline. Conclusions This study suggests that global efficiency is a more sensitive indicator of network changes in Alzheimer’s disease when compared to clustering coefficient. Both network properties were associated with pathology and cognitive performance, demonstrating their relevance in clinical settings. Our findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization in Alzheimer’s disease, suggesting that it is the lack of direct connections what drives these functional changes.

Background: The majority of individuals with dementia will suffer from behavioral and psychological symptoms of dementia (BPSD). These symptoms contribute to functional impairment and caregiver burden. Objective: To characterize BPSD in Alzheimer’s disease (AD), vascular dementia (VaD), mixed (Mixed) dementia, Parkinson’s disease dementia (PDD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and unspecified dementia in individuals residing in long-term care facilities. Methods: We included 10,405 individuals with dementia living in long-term care facilities from the Swedish registry for cognitive/dementia disorders (SveDem) and the Swedish BPSD registry. BPSD was assessed with the Neuropsychiatric Inventory - Nursing Home Version (NPI-NH). Multivariate logistic regression models were used to evaluate the associations between dementia diagnoses and different BPSDs. Results: The most common symptoms were aberrant motor behavior, agitation, and irritability. Compared to AD, we found a lower risk of delusions (in FTD, unspecified dementia), hallucinations (FTD), agitation (VaD, PDD, unspecified dementia), elation/euphoria (DLB), anxiety (Mixed, VaD, unspecified dementia), disinhibition (in PDD), irritability (in DLB, FTD, unspecified dementia), aberrant motor behavior (Mixed, VaD, unspecified dementia), and sleep and night-time behavior changes (unspecified dementia). Higher risk of delusions (DLB), hallucinations (DLB, PDD), apathy (VaD, FTD), disinhibition (FTD), and appetite and eating abnormalities (FTD) were also found in comparison to AD. Conclusion: Although individuals in our sample were diagnosed with different dementia disorders, they all exhibited aberrant motor behavior, agitation, and irritability. This suggests common underlying psychosocial or biological mechanisms. We recommend prioritizing these symptoms while planning interventions in long-term care facilities.

White matter (WM) alterations are commonly found across different stages of Alzheimer’s disease (AD). However, the association between these changes with underlying AD pathology such as amyloid-β (Aβ) and tau deposition is still poorly understood. Hitherto, most studies have assessed WM alterations in AD using diffusion tensor imaging (DTI). Nonetheless, DTI has methodological shortcomings that limit an accurate biological interpretation. To address this limitation, here we applied fixel-based analysis (FBA) to disentangle microscopic differences in fiber density (FD) from macroscopic morphological changes in fiber cross-section (FC) in early stages of AD. We further investigated the associations of FBA metrics with AD pathology and cognitive performance. Additionally, we compared FBA results with other commonly used WM metrics derived from DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). To achieve these goals, we included 224 Aβ-negative and 91 Aβ-positive cognitively unimpaired individuals as well as 78 Aβ-positive patients with mild cognitive impairment (MCI) with diffusion-weighted MRI (dMRI), Aβ-PET and tau-PET scans from the Swedish BioFINDER-2 study. We found that tau-PET uptake in medial temporal regions was associated with macrostructural alterations reflected by reduced FC mainly in the parahippocampal part of the cingulum bundle in Aβ-positive individuals. This tau-related WM alteration was also associated with impaired memory. Interestingly, only FBA metrics were able to capture the association between tau-PET uptake and white matter degeneration. No association was found between global amyloid load and any dMRI metrics. Compared to both cognitively unimpaired groups, MCI patients showed a decrease in all FBA metrics in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations. Metrics derived from DKI, and FW-DTI revealed a similar pattern of alterations whereas the spatial extent of WM abnormalities detected by DTI was more widespread. Altogether, our results indicate that early WM alterations in AD are mainly due to macrostructural changes identified by FBA metrics, being more closely associated with tau than Aβ pathology. These findings suggest that future studies assessing the effects of AD pathology in white matter tracts should consider using FBA metrics.

Background The medial parietal cortex is an early site of pathological protein deposition in Alzheimer’s disease (AD). Previous studies have identified different subregions within this area; however, these subregions are often heterogeneous and disregard individual differences or subtle pathological alterations in the underlying functional architecture. To address this limitation, here we measured the continuous connectivity gradients of the medial parietal cortex and assessed their relationship with cerebrospinal fluid (CSF) biomarkers, ApoE ε4 carriership and memory in asymptomatic individuals at risk to develop AD. Methods Two hundred sixty-three cognitively normal participants with a family history of sporadic AD who underwent resting-state and task-based functional MRI using encoding and retrieval tasks were included from the PREVENT-AD cohort. A novel method for characterizing spatially continuous patterns of functional connectivity was applied to estimate functional gradients in the medial parietal cortex during the resting-state and task-based conditions. This resulted in a set of nine parameters that described the appearance of the gradient across different spatial directions. We performed correlation analyses to assess whether these parameters were associated with CSF biomarkers of phosphorylated tau181 (p-tau), total tau (t-tau), and amyloid-ß1-42 (Aß). Then, we compared the spatial parameters between ApoE ε4 carriers and noncarriers, and evaluated the relationship between these parameters and memory. Results Alterations involving the superior part of the medial parietal cortex, which was connected to regions of the default mode network, were associated with higher p-tau, t-tau levels as well as lower Aß/p-tau levels during the resting-state condition (p p p = 0.001). No results were found when using conventional connectivity measures. Conclusions Functional alterations in the medial parietal gradients are associated with CSF AD biomarkers, ApoE ε4 carriership, and lower memory in an asymptomatic cohort with a family history of sporadic AD, suggesting that functional gradients are sensitive to subtle changes associated with early AD stages.

BackgroundExisting bio fluid and imaging biomarkers used in research and clinical diagnostics of neurodegenerative diseases are often expensive or invasive and are mainly available in specialised care centres. CT is an affordable and widely available imaging modality predominantly used to evaluate structural abnormalities, but not for the volumetric quantification of neurodegeneration. Previously, we developed a deep learning model trained on MRI segmentations from individuals with paired CT and MR scans, which achieved high accuracy and robust tissue classification based on brain CT images.PurposeTo explore the diagnostic utility of deep-learning-derived CT-based atrophy measures and study their association with relevant cognitive, biochemical and other imaging markers of neurodegenerative diseases.Materials and methodsIn this retrospective study, we analysed 917 CT and 744 MR scans from cognitively healthy participants of the Gothenburg H70 Birth Cohort (70.4 ± 2.6 years) and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore (73 Alzheimer’s disease, 20 vascular dementia, 22 cognitively normal; 74.0 ± 8.2 years). We tested associations between six CT-derived volumetric measures with clinical diagnosis, fluid and imaging biomarkers and cognition.ResultsIn the Memory Clinic Cohort, deep-learning-derived CT-based atrophy measures differentiated cognitively healthy individuals from Alzheimer’s disease (AUC 0.88; 95% CI: 0.79-0.96) and vascular dementia (AUC 0.91; 95% CI: 0.81-1.00) patients with high accuracy levels comparable to MR-derived measures. Additionally, CT-based measures distinguished early, prodromal Alzheimer’s disease (AUC= 0.73, 95% CI: 0.62, 0.85) and prodromal vascular dementia patients from healthy individuals (CT-GM: AUC= 0.7, 95% CI: 0.51, 0.81). CT-derived volumes were significantly associated with measures of cognition and biochemical markers of neurodegeneration, notably plasma-derived neurofilament light (ρ=-0.43, pConclusionOur findings provide strong evidence for the potential of deep-learning-derived CT-based atrophy measures in aiding neurodegenerative disease diagnostics in primary care settings.

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

It is currently unclear whether plasma biomarkers can be used as independent prognostic tools to predict changes associated with early Alzheimer’s disease. In this study, we sought to address this question by assessing whether plasma biomarkers can predict changes in amyloid load, tau accumulation, brain atrophy and cognition in non-demented individuals. To achieve this, plasma amyloid-β 42/40 (Aβ42/40), phosphorylated-tau181, phosphorylated-tau217 and neurofilament light were determined in 159 non-demented individuals, 123 patients with Alzheimer’s disease dementia and 35 patients with a non-Alzheimer’s dementia from the Swedish BioFINDER-2 study, who underwent longitudinal amyloid (18F-flutemetamol) and tau (18F-RO948) PET, structural MRI (T1-weighted) and cognitive testing. Our univariate linear mixed effect models showed there were several significant associations between the plasma biomarkers with imaging and cognitive measures. However, when all biomarkers were included in the same multivariate linear mixed effect models, we found that increased longitudinal amyloid-PET signals were independently predicted by low baseline plasma Aβ42/40 (P = 0.012), whereas increased tau-PET signals, brain atrophy and worse cognition were independently predicted by high plasma phosphorylated-tau217 (P, Pereira et al. show that plasma Aβ42/40 is an independent predictor of future amyloid accumulation, whereas plasma P-tau217 is an independent predictor of tau accumulation, brain atrophy and cognitive decline. Both measures could thus serve as prognostic markers to predict future Alzheimer’s disease pathology.

The association between cerebrospinal fluid (CSF) amyloid beta (Aβ) Aβ38 or Aβ40 and brain grey- and white matter integrity is poorly understood. We studied this in 213 cognitively normal 70-year-olds, and in subgroups defined by presence/absence of the APOE ε4 allele and Aβ pathology: Aβ-/APOE-, Aβ+/APOE-, Aβ-/APOE+ and Aβ+/APOE+. CSF Aβ was quantified using ELISA and genotyping for APOE was performed. Low CSF Aβ42 defined Aβ plaque pathology. Brain volumes were assessed using Freesurfer-5.3, and white matter integrity using tract-based statistics in FSL. Aβ38 and Aβ40 were positively correlated with cortical thickness, some subcortical volumes and white matter integrity in the total sample, and in 3 of the subgroups: Aβ-/APOE-, Aβ+/APOE- and Aβ-/APOE+. In Aβ+/APOE+ subjects, higher Aβ38 and Aβ40 were linked to reduced cortical thickness and subcortical volumes. We hypothesize that production of all Aβ species decrease in brain regions with atrophy. In Aβ+/APOE+, Aβ-dysregulation may be linked to cortical atrophy in which high Aβ levels is causing pathological changes in the gray matter of the brain.

Elucidating compensatory mechanisms underpinning phonemic fluency (PF) may help to minimize its decline due to normal aging or neurodegenerative diseases. We investigated cortical brain networks potentially underpinning compensation of age-related differences in PF. Using graph theory, we constructed networks from measures of thickness for PF, semantic, and executive–visuospatial cortical networks. A total of 267 cognitively healthy individuals were divided into younger age (YA, 38–58 years) and older age (OA, 59–79 years) groups with low performance (LP) and high performance (HP) in PF: YA-LP, YA-HP, OA-LP, OA-HP. We found that the same pattern of reduced efficiency and increased transitivity was associated with both HP (compensation) and OA (aberrant network organization) in the PF and semantic cortical networks. When compared with the OA-LP group, the higher PF performance in the OA-HP group was associated with more segregated PF and semantic cortical networks, greater participation of frontal nodes, and stronger correlations within the PF cortical network. We conclude that more segregated cortical networks with strong involvement of frontal nodes seemed to allow older adults to maintain their high PF performance. Nodal analyses and measures of strength were helpful to disentangle compensation from the aberrant network organization associated with OA.

Background: Cognitive deficits arising in the course of Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and Parkinson’s disease with dementia (PDD) are directly linked to synaptic loss. Postmortem studies suggest that zinc transporter protein 3 (ZnT3), AMPA glutamate receptor 3 (GluA3), and Dynamin1 are associated with cognitive decline in AD and Lewy body dementia patients. Objective: We aimed to evaluate the diagnostic value of ZnT3, GluA3, and Dynamin 1 in the cerebrospinal fluid (CSF) of patients with dementia due to AD, DLB, and PDD compared to cognitively normal subjective cognitive decline (SCD) patients in a retrospective study. In addition, we assessed the relationship between synaptic markers and age, sex, cognitive impairment, and depressive symptoms as well as CSF amyloid, phosphorylated tau (p-tau), and total tau (T-tau). Methods: Commercially available ELISA immunoassay was used to measure the levels of proteins in a total of 97 CSF samples from AD (N = 24), PDD (N = 18), DLB (N = 27), and SCD (N = 28) patients. Cognitive impairment was assessed using the Mini-Mental State Examination (MMSE). Results: We found a significant increase in the concentrations of ZnT3, GluA3, and Dynamin1 in AD (p = 0.002) and of ZnT3 and Dynamin 1 in DLB (p = 0.001, p = 0.002) when compared to SCD patients. Changes in ZnT3 concentrations correlated with MMSE scores in AD (p = 0.011), and with depressive symptoms in SCD (p = 0.041). Conclusion: We found alteration of CSF levels of synaptic proteins in AD, PDD, and DLB. Our results reveal distinct changes in CSF concentrations of ZnT3 that could reflect cognitive impairment in AD with implications for future prognostic and diagnostic marker development.

Background The panel of fluid- and imaging-based biomarkers available for neurodegenerative disease research is growing and has the potential to close important gaps in research and the clinic. With this growth and increasing use, appropriate implementation and interpretation are paramount. Various biomarkers feature nuanced differences in strengths, limitations, and biases that must be considered when investigating disease etiology and clinical utility. For example, neuropathological investigations of Alzheimer’s disease pathogenesis can fall in disagreement with conclusions reached by biomarker-based investigations. Considering the varied strengths, limitations, and biases of different research methodologies and approaches may help harmonize disciplines within the neurodegenerative disease field. Purpose of review Along with separate review articles covering fluid and imaging biomarkers in this issue of Alzheimer’s Research and Therapy, we present the result of a discussion from the 2019 Biomarkers in Neurodegenerative Diseases course at the University College London. Here, we discuss themes of biomarker use in neurodegenerative disease research, commenting on appropriate use, interpretation, and considerations for implementation across different neurodegenerative diseases. We also draw attention to areas where biomarker use can be combined with other disciplines to understand issues of pathophysiology and etiology underlying dementia. Lastly, we highlight novel modalities that have been proposed in the landscape of neurodegenerative disease research and care.

Purpose The abnormal deposition of tau begins before the onset of clinical symptoms and seems to target specific brain networks. The aim of this study is to identify the spatial patterns of tau deposition in cognitively normal older adults and assess whether they are related to amyloid-β (Aβ), APOE, sex, and longitudinal cognitive decline. Methods We included 114 older adults with cross-sectional flortaucipir (FTP) and Pittsburgh Compound-B PET in addition to longitudinal cognitive testing. A voxel-wise independent component analysis was applied to FTP images to identify the spatial patterns of tau deposition. We then assessed whether tau within these patterns differed by Aβ status, APOE genotype, and sex. Linear mixed effects models were built to test whether tau in each component predicted cognitive decline. Finally, we ordered the spatial components based on the frequency of high tau deposition to model tau spread. Results We found 10 biologically plausible tau patterns in the whole sample. There was greater tau in medial temporal, occipital, and orbitofrontal components in Aβ-positive compared with Aβ-negative individuals; in the parahippocampal component in ε3ε3 compared with ε2ε3 carriers; and in temporo-parietal and anterior frontal components in women compared with men. Higher tau in temporal and frontal components predicted longitudinal cognitive decline in memory and executive functions, respectively. Tau deposition was most frequently observed in medial temporal and ventral cortical areas, followed by lateral and primary areas. Conclusions These findings suggest that the spatial patterns of tau in asymptomatic individuals are clinically meaningful and are associated with Aβ, APOE ε2ε3, sex and cognitive decline. These patterns could be used to predict the regional spread of tau and perform in vivo tau staging in older adults.

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.

Background: Hypertension is an important risk factor for Alzheimer’s disease (AD). The pathophysiological mechanisms underlying the relationship between AD and hypertension are not fully understood, but they most likely involve microvascular dysfunction and cerebrovascular pathology. Although previous studies have assessed the impact of hypertension on different markers of brain integrity, no study has yet provided a comprehensive comparison of cerebrospinal fluid (CSF) biomarkers and structural brain differences between normotensive and hypertensive groups in a single and large cohort of older adults in relationship to cognitive performances.Objective: The aim of the present work was to investigate the differences in cognitive performances, CSF biomarkers and magnetic resonance imaging (MRI) of brain structure between normotensive, controlled hypertensive, uncontrolled hypertensive, and untreated hypertensive older adults from the Gothenburg H70 Birth Cohort Studies.Methods: As an indicator of vascular brain pathology, we measured white matter hyperintensities (WMHs), lacunes, cerebral microbleeds, enlarged perivascular space (epvs), and fractional anisotropy (FA). To assess markers of AD pathology/neurodegeneration, we measured hippocampal volume, temporal cortical thickness on MRI, and amyloid-β42, phosphorylated tau, and neurofilament light protein (NfL) in cerebrospinal fluid. Various neuropsychological tests were used to assess performances in memory, attention/processing speed, executive function, verbal fluency, and visuospatial abilities.Results: We found more white matter pathology in hypertensive compared to normotensive participants, with the highest vascular burden in uncontrolled participants (e.g., lower FA, more WMHs, and epvs). No significant difference was found in any MRI or CSF markers of AD pathology/neurodegeneration when comparing normotensive and hypertensive participants, nor among hypertensive groups. No significant difference was found in most cognitive functions between groups.Conclusion: Our results suggest that good blood pressure control may help prevent cerebrovascular pathology. In addition, hypertension may contribute to cognitive decline through its effect on cerebrovascular pathology rather than AD-related pathology. These findings suggest that hypertension is associated with MRI markers of vascular pathology in the absence of a significant decline in cognitive functions.

Several studies have shown decreased cerebral blood flow (CBF) in Alzheimer’s disease (AD). However, the role of hypoperfusion in the disease pathogenesis remains unclear. Combining arterial spin labeling MRI, PET, and CSF biomarkers, we investigated the associations between gray matter (GM)-CBF and the key mechanisms in AD including amyloid-β (Aβ) and tau pathology, synaptic and axonal degeneration. Further, we applied a disease progression modeling to characterize the temporal sequence of different AD biomarkers. Lower perfusion was observed in temporo-occipito-parietal cortex in the Aβ-positive cognitively impaired compared to both Aβ-negative and Aβ-positive cognitively unimpaired individuals. In participants along the AD spectrum, GM-CBF was associated with tau, synaptic and axonal dysfunction, but not Aβ in similar cortical regions. Axonal degeneration was further associated with hypoperfusion in cognitively unimpaired individuals. Disease progression modeling revealed that GM-CBF disruption Followed the abnormality of biomarkers of Aβ, tau and brain atrophy. These findings indicate that tau tangles and neurodegeneration are more closely connected with GM-CBF changes than Aβ pathology. Although subjected to the sensitivity of the employed neuroimaging techniques and the modeling approach, these findings suggest that hypoperfusion might not be an early event associated with the build-up of Aβ in preclinical phase of AD.

Although recent clinical trials targeting amyloid-β in Alzheimer’s disease have shown promising results, there is increasing evidence suggesting that understanding alternative disease pathways that interact with amyloid-β metabolism and amyloid pathology might be important to halt the clinical deterioration. In particular, there is evidence supporting a critical role of astroglial activation and astrocytosis in Alzheimer’s disease. However, so far, no studies have assessed whether astrocytosis is independently related to either amyloid-β or tau pathology in vivo. To address this question, we determined the levels of the astrocytic marker GFAP in plasma and CSF of 217 amyloid-β-negative cognitively unimpaired individuals, 71 amyloid-β-positive cognitively unimpaired individuals, 78 amyloid-β-positive cognitively impaired individuals, 63 amyloid-β-negative cognitively impaired individuals and 75 patients with a non-Alzheimer’s disease neurodegenerative disorder from the Swedish BioFINDER-2 study. Participants underwent longitudinal amyloid-β (18F-flutemetamol) and tau (18F-RO948) PET as well as cognitive testing. We found that plasma GFAP concentration was significantly increased in all amyloid-β-positive groups compared with participants without amyloid-β pathology (P, Pereira et al. show that plasma GFAP is an early and independent marker of astrocytosis associated with Aβ pathology. Plasma GFAP correlated with cognitive decline and mediated the effect of Aβ-PET on tau-PET burden, suggesting that astrocytosis secondary to Aβ aggregation may promote tau accumulation.

BackgroundChanges 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. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum.MethodsWe included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions.ResultsAcross the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (allpConclusionsOur data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

The organization of the Alzheimer's disease (AD) connectome has been studied using single neuroimaging modalities. However, different neuroimaging modalities are not independent and often interact with each other in the course of AD. Here, we integrate the networks obtained from T1-weighted and 18F-Florbetapir PET to build a multiplex connectome using BRAPH 2.0 and assess how it changes across different AD stages. We assessed the overlapping strength, multilayer communities, overlapping connections, and the multiplex participation and clustering coefficients. There was a reorganization of the communities across the four groups and we found significant changes reflecting a loss of multiplex hubs and overlapping connections in medial frontal and occipital areas in the patients’ groups. These findings indicate that multiplex network changes can be useful to understand the relationship between amyloid pathology and gray matter atrophy occurring over the course of AD.

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we investigate the cognitive variability in older adults by linking the influence of white matter microstructure on the task-related organization of fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual differences in cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal (fMRI and biological markers) brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

The brain is a complex network that relies on the interaction between its various regions, known as the connectome. The human connectome undergoes complex changes with aging and shows differences in many functional network measures between men and women; however, the effects of aging and sex on the brain connectome are not well characterized. In this study, we assess functional connectivity changes in a large cohort of men and women between 45 and 79 years of age using conventional methods as well as a novel approach based on multilayer brain connectivity. Our findings provide a deeper insight into the sex differences that occur in functional connectivity over the course of aging. Moreover, they indicate that multilayer networks provide a natural way to integrate the information from positive and negative functional connections, providing important information on the effects of sex and age on network topology.

A growing number of studies suggest that detection of Alzheimer’s disease can be improved by using information derived from distinct neuroimaging modalities. However, so far it remains unresolved how these modalities can be combined within a deep learning model approach. In this study, we proposed a deep-neural-network model GapNet that can work with incomplete dataset including baseline and longitudinal MR, amyloid-PET, and FDG-PET data. We verified the effectiveness of GapNet by comparing it to the conventional Vanilla neural networks and specifically testing their performance in discriminating between healthy controls and individuals with amyloid changes, which is an important early pathological marker in Alzheimer’s Disease. Results showed that, compared to the Vanilla networks, GapNet achieved higher classification accuracy. In sum, our finding suggested that the GapNet model is a promising deep learning approach for detecting Alzheimer’s disease with multi-modal neuroimaging

Although several studies have shown decreased cerebral blood flow (CBF) in Alzheimers disease (AD), the role of hypoperfusion in the disease pathogenesis remains unclear. Combining arterial spin labeling MRI, positron emission tomography, and biomarkers of cerebrospinal fluid, we investigated the associations between CBF and the key mechanisms in AD including amyloid-{beta} (A{beta}) and tau pathology, synaptic dysfunction and axonal degeneration. Further, we applied a disease progression modeling to characterize the temporal sequence of different AD biomarkers. Lower perfusion was observed in the temporo-occipito-parietal regions in the A{beta}-positive cognitively impaired compared to both the A{beta}-positive and A{beta}-negative cognitively unimpaired individuals. In participants along the AD spectrum (those with A{beta} pathology regardless of their cognitive status), CBF was inversely associated with tau and synaptic dysfunction, but not A{beta}, in similar cortical regions. Moreover, the disease progression modeling revealed that CBF disruption followed the abnormality of biomarkers of A{beta}, tau and brain atrophy. These findings indicate that tau tangles and synaptic degeneration are more closely connected with CBF changes rather than A{beta} pathology. This supports the notion that hypoperfusion is not an early event associated with the build-up of A{beta} during the preclinical phase of AD.

BackgroundThe majority of individuals with dementia will suffer from psychological and behavioral symptoms of dementia (BPSD). There is limited number of studies investigating BPSD in different types of dementia.ObjectivesTo characterize BPSD in Alzheimer’s disease (AD), vascular dementia (VaD), mixed (Mixed) dementia, Parkinson’s disease dementia (PDD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD) and unspecified dementia (UNS) in individuals residing in long-term care facilities.MethodsWe included 10,405 individuals with dementia living in long-term care facilities from the Swedish registry for cognitive/dementia disorders (SveDem) and the Swedish BPSD registry. BPSD was assessed with the Neuropsychiatric Inventory - Nursing Home Version (NPI-NH). Multivariate logistic regression models were used to evaluate the associations between dementia diagnoses and different BPSDs.ResultsThe most common symptoms were aberrant motor behavior, agitation and irritability. Compared to AD, we found a lower risk of delusions (in FTD, unspecified dementia), hallucinations (in FTD), agitation (VaD, in PDD, unspecified dementia), elation/euphoria (in DLB), anxiety (Mixed, VaD, unspecified dementia), disinhibition (in PDD); irritability (in DLB, FTD, unspecified dementia), aberrant motor behavior (in Mixed, VaD, unspecified dementia), sleep and night-time behavior changes (in unspecified dementia). Higher risk of delusions (in DLB), hallucinations (in DLB, PDD), apathy (VaD, FTD), disinhibition (in FTD) and appetite and eating abnormalities (in FTD) were also found in comparison to AD.ConclusionsBPSD was common in all types of dementia, with the most common symptoms being aberrant motor behavior, agitation, and irritability. Additionally, presentation of BPSD differ in different dementia types.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a deterioration of neuronal connectivity. The pathological accumulation of tau in neurons is one of the hallmarks of AD and has been connected to the loss of dendritic spines of pyramidal cells, which are the major targets of cortical excitatory synapses and key elements in memory storage. However, the detailed mechanisms underlying the loss of dendritic spines in individuals with AD are still unclear. Here, we used graph-theory approaches to compare the distribution of dendritic spines from neurons with and without tau pathology of AD individuals. We found that the presence of tau pathology determines the loss of dendritic spines in clusters, ruling out alternative models where spine loss occurs at random locations. Since memory storage has been associated with synaptic clusters, the present results provide a new insight into the mechanisms by which tau drives synaptic damage in AD, paving the way to memory deficits through alterations of spine organization., This work was supported by grants from the Spanish Ministerio de Ciencia, Innovación y Universidades (Grant IJCI-2016-27658 to PMS, Grant PGC2018-094307-B-I00), the Cajal Blue Brain Project (the Spanish partner of the Blue Brain Project initiative from EPFL, Switzerland) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED, CB06/05/0066, Spain). JBP is currently supported by Grants from the Swedish Research Council (#2018-02201), Hjärnfonden (#FO2019-0289), Alzheimerfonden (#AF-930827) and the Strategic Research Programme in Neuroscience at Karolinska Institutet (Stratneuro Startup Grant).

Parkinson’s disease (PD) patients frequently develop cognitive impairment. There is a need for brief clinical assessments identifying PD patients at high risk of progressing to dementia. In this study, we look into predicting dementia in PD and underlying structural and functional correlates to cognitive decline in PD. We included 175 patients with PD, 30 with PD dementia, 51 neurologically healthy controls and 121 patients with Alzheimer’s disease (AD) from Skane University Hospital, BIOFINDER cohorts. All underwent cognitive tests, including MMSE, 10-word list delayed recall (ADAS-cog), A Quick Test of cognitive speed (AQT), Letter S fluency, Clock Drawing Test (CDT) and pentagon copying. In non-demented patients with PD, abnormal AQT and CDT results predicted an increased risk of subsequent development of dementia (hazard ratio 2.2 for both). When comparing the cognitive profile between PD and AD, decreased performance on AQT, which measures attention and processing speed, was more typical in PD. Lastly, we investigated the underlying structural and functional correlates for the PD-specific test AQT with magnetic resonance imaging. In PD patients, decreased performance on AQT was associated with i) cortical thinning in temporoparietal regions, ii) changes in diffusion MRI, especially in the cingulum tract, and iii) decreased functional connectivity in posterior brain networks.