Discriminating Alzheimer's disease progression using a new hippocampal marker from T1‐weighted MRI: The local surface roughness

Hippocampal atrophy is one of the main hallmarks of Alzheimer's disease (AD). However, there is still controversy about whether this sign is a robust finding during the early stages of the disease, such as in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Considering this background, we proposed a new marker for assessing hippocampal atrophy: the local surface roughness (LSR). We tested this marker in a sample of 307 subjects (normal control (NC) = 70, SCD = 87, MCI = 137, AD = 13). In addition, 97 patients with MCI were followed‐up over a 3‐year period and classified as stable MCI (sMCI) (n = 61) or progressive MCI (pMCI) (n = 36). We did not find significant differences using traditional markers, such as normalized hippocampal volumes (NHV), between the NC and SCD groups or between the sMCI and pMCI groups. However, with LSR we found significant differences between the sMCI and pMCI groups and a better ability to discriminate between NC and SCD. The classification accuracy of the LSR for NC and SCD was 68.2%, while NHV had a 57.2% accuracy. In addition, the classification accuracy of the LSR for sMCI and pMCI was 74.3%, and NHV had a 68.3% accuracy. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on hippocampal markers and conversion times. The LSR marker showed better prediction of conversion to AD than NHV. These results suggest the relevance of considering the LSR as a new hippocampal marker for the AD continuum.     

Markers of Alzheimer's disease in a population attending a memory clinic

BackgroundNew marker-based criteria for the diagnosis of Alzheimer's disease (AD) were recently proposed. We describe their operational translation in 144 consecutive patients referred to our Memory Clinic.MethodsVisual ratings of hippocampal atrophy and of cortical glucose hypometabolism in magnetic resonance imaging and positron emission tomography, and concentrations of total tau and Aβ1-42 in cerebrospinal fluid were assessed in 12 patients with subjective memory complaints (SMCs) (Mini-Mental State Examination [MMSE] score, 28.0 ± 1.1 [mean ± SD]), 37 with mild cognitive impairment (MCI) (MMSE, 25.1 ± 3.6), 55 with AD (MMSE, 21.1 ± 3.5), and 40 with non-AD dementia (MMSE, 21.6 ± 5.5).ResultsThe sensitivity for AD of each individual biomarker was higher (65% to 87%) than for MCI (18% to 50%). Each biomarker's specificity for SMC and non-AD dementias was good to moderate (83% and 53%). Positivity for at least one marker increased the probability 38 times of belonging to the AD group (P < 0.0001).ConclusionThe new diagnostic criteria can be operationalized in clinical routines, but longitudinal studies of MCI patients will need to assess the criteria's prognostic value.     

Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data

BackgroundThis study examined the predictive value of different classes of markers in the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) over an extended 4-year follow-up in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.MethodsMCI patients were assessed for clinical, cognitive, magnetic resonance imaging (MRI), positron emission tomography–fluorodeoxyglucose (PET-FDG), and cerebrospinal fluid (CSF) markers at baseline and were followed on a yearly basis for 4 years to ascertain progression to AD. Logistic regression models were fitted in clusters, including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF, amyloid-β, and tau).ResultsThe predictive model at 4 years revealed that two cognitive measures, an episodic memory measure and a Clock Drawing screening test, were the best predictors of conversion (area under the curve = 0.78).ConclusionsThis model of prediction is consistent with the previous model at 2 years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers.     

Cerebrospinal fluid Aβ42 is the best predictor of clinical progression in patients with subjective complaints

BackgroundThe need to recognize Alzheimer’s disease (AD) as early as possible led us to evaluate the predictive value of amyloid β(1-42) (Aβ42), total tau (tau), and phosphorylated tau (ptau) in cerebrospinal fluid (CSF) for clinical progression in patients with subjective complaints.MethodsWe recruited nondemented patients with subjective complaints (i.e., criteria for mild cognitive impairment [MCI] not fulfilled) from our memory clinic. We assessed the predictive value of CSF Aβ42, tau, and ptau for clinical progression using Cox proportional hazards models adjusted for age, gender, and baseline findings on the Mini-Mental State Examination (MMSE). Clinical progression was defined as progression to MCI or AD.ResultsWe included 127 patients with subjective complaints (age 60 ± 10 years, 61 [48%] females, MMSE 29 ± 1). At baseline, Aβ42 and tau were abnormal in 20 patients (both 16%), and ptau in 32 patients (25%). Thirteen patients (10%) progressed to MCI (n = 11) or AD (n = 2). Aβ42 was the strongest predictor of progression to MCI or AD with an adjusted hazard ratio (HR) of 16.0 (3.8–66.4). The adjusted HR associated with tau was 2.8 (0.9–9.2) and with ptau 2.6 (0.8–8.2). Combinations of biomarkers had a lower predictive value than Aβ42 alone.ConclusionLow Aβ42 was the strongest predictor of clinical progression in patients with subjective complaints. These results are in line with the hypothesis that the cascade of pathologic events starts with deposition of Aβ42, whereas neuronal degeneration and hyperphosphorylation of tau are more downstream events, closer to clinical manifestation of AD.     

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

IntroductionIt is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.MethodsA deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.ResultsThe deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures.DiscussionOur method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.     

The new Alzheimer’s criteria in a naturalistic series of patients with mild cognitive impairment

To test the validity of the new diagnostic criteria for Alzheimer’s disease (AD) in a naturalistic series of patients with mild cognitive impairment (MCI). Ninety consecutive MCI patients were enrolled in a longitudinal study on the natural history of cognitive impairment. Medial temporal (MT) atrophy on MRI was defined as hippocampal volume below the fifth percentile of the distribution in healthy elders, abnormal CSF was based on Sjogren’s cutoffs for Abeta42 and tau, and temporoparietal hypometabolism on 18F-FDG PET based on Herholz’s t sum score. Patients were followed clinically to detect conversion to AD (MCI-AD), non-AD dementia (MCI-nAD), or no conversion (MCI-NC). The 24 MCI-AD and 15 MCI-nAD patients had sociodemographic, clinical, and neuropsychological baseline features similar to the 51 MCI-NC patients. All MCI patients with MT atrophy converted to AD, as did all those with abnormal CSF, but only 48 and 35% of those without MT atrophy or abnormal CSF converted (p on logrank test = 0.0007 and 0.001). Prediction of AD conversion was enhanced when positivity to either MT atrophy or abnormal CSF was considered, with only 15% of those MCI patients negative on both converting to AD (p < 0.0005). Markers were not predictive of non-AD dementia conversion. The accuracy of either MT atrophy or abnormal CSF in discriminating MCI-AD from MCI-NC was good (AUC 0.82, 95% CI 0.70–0.95). MT atrophy and abnormal CSF are the single most robust predictors of conversion to AD in MCI patients, and their combination enhances prediction. AD markers are not predictive of conversion to non-AD dementia.     

The cerebrospinal fluid “Alzheimer profile”: Easily said,but what does it mean?

BackgroundWe aimed to identify the most useful definition of the “cerebrospinal fluid Alzheimer profile,” based on amyloid-ß_1-42 (Aβ₄₂), total tau, and phosphorylated tau (p-tau), for diagnosis and prognosis of Alzheimer's disease (AD).MethodsWe constructed eight Alzheimer profiles with previously published combinations, including regression formulas and simple ratios. We compared their diagnostic accuracy and ability to predict dementia due to AD in 1385 patients from the Amsterdam Dementia Cohort. Results were validated in an independent cohort (n = 1442).ResultsCombinations outperformed individual biomarkers. Based on the sensitivity of the best performing regression formulas, cutoffs were chosen at 0.52 for the tau/Aβ₄₂ ratio and 0.08 for the p-tau/Aβ₄₂ ratio. Ratios performed similar to formulas (sensitivity, 91%–93%; specificity, 81%–84%). The same combinations best predicted cognitive decline in mild cognitive impairment patients. Validation confirmed these results, especially regarding the tau/Aβ₄₂ ratio.ConclusionsA tau/Aβ₄₂ ratio of >0.52 constitutes a robust cerebrospinal fluid Alzheimer profile. We recommend using this ratio to combine biomarkers.     

Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease

BackgroundIn the earliest clinical stages of Alzheimer’s disease (AD) when symptoms are mild, clinical diagnosis can be difficult. AD pathology most likely precedes symptoms. Biomarkers can serve as early diagnostic indicators or as markers of preclinical pathologic change. Candidate biomarkers derived from structural and functional neuroimaging and those measured in cerebrospinal fluid (CSF) and plasma show the greatest promise. Unbiased exploratory approaches, eg, proteomics or cortical thickness analysis, could yield novel biomarkers. The objective of this article was to review recent progress in selected imaging and neurochemical biomarkers for early diagnosis, classification, progression, and prediction of AD.MethodsWe performed a survey of recent research, focusing on core biomarker candidates in AD.ResultsA number of in vivo neurochemistry and neuroimaging techniques, which can reliably assess aspects of physiology, pathology, chemistry, and neuroanatomy, hold promise as biomarkers. These neurobiologic measures appear to relate closely to pathophysiologic, neuropathologic, and clinical data, such as hyperphosphorylation of tau, amyloid beta (Aβ) metabolism, lipid peroxidation, pattern and rate of atrophy, loss of neuronal integrity, functional and cognitive decline, as well as risk of future decline. Current advances in the neuroimaging of mediotemporal, neocortical, and subcortical areas of the brain of mild cognitive impairment (MCI) and AD subjects are presented. CSF levels of Aβ42, tau, and hyperphosphorylated tau protein (p-tau) can distinguish subjects with MCI who are likely to progress to AD. They also show preclinical alterations that predict later development of early AD symptoms. Studies on plasma Aβ are not entirely consistent, but recent findings suggest that decreased plasma Aβ42 relative to Aβ40 might increase the risk of AD. Increased production of Aβ in aging is suggested by elevation of BACE1 protein and enzyme activity in the brain and CSF of subjects with MCI. CSF tau and p-tau are increased in MCI as well and show predictive value. Other biomarkers might indicate components of a cascade initiated by Aβ, such as oxidative stress or inflammation. These merit further study in MCI and earlier.ConclusionsA number of neuroimaging candidate markers are promising, such as hippocampus and entorhinal cortex volumes, basal forebrain nuclei, cortical thickness, deformation-based and voxel-based morphometry, structural and effective connectivity by using diffusion tensor imaging, tractography, and functional magnetic resonance imaging. CSF Aβ42, BACE1, total tau, and p-tau are substantially altered in MCI and clinical AD. Other interesting novel marker candidates derived from blood are being currently proposed (phase I). Biomarker discovery through proteomic approaches requires further research. Large-scale international controlled multicenter trials (such as the U.S., European, Australian, and Japanese Alzheimer’s Disease Neuroimaging Initiative and the German Dementia Network) are engaged in phase III development of the core feasible imaging and CSF biomarker candidates in AD. Biomarkers are in the process of implementation as primary outcome variables into regulatory guideline documents regarding study design and approval for compounds claiming disease modification.     

Spatial correlation maps of the hippocampus with cerebrospinal fluid biomarkers and cognition in Alzheimer's disease: A longitudinal study

This study is an observational study that takes the existing longitudinal data from Alzheimer''s disease Neuroimaging Initiative to examine the spatial correlation map of hippocampal subfield atrophy with CSF biomarkers and cognitive decline in the course of AD. This study included 421 healthy controls (HC), 557 patients of stable mild cognitive impairment (s‐MCI), 304 Alzheimer''s Disease (AD) patients, and 241 subjects who converted to be AD from MCI (c‐MCI), and 6,525 MRI scans in a period from 2004 to 2019. Our findings revealed that all the hippocampal subfields showed their accelerated atrophy rate from cognitively normal aging to stable MCI and AD. The presubiculum, dentate gyrus, and fimbria showed greater atrophy beyond the whole hippocampus in the HC, s‐MCI, and AD groups and corresponded to a greater decline of memory and attention in the s‐MCI group. Moreover, the higher atrophy rates of the subiculum and CA2/3, CA4 were also associated with a greater decline in attention in the s‐MCI group. Interestingly, patients with c‐MCI showed that the presubiculum atrophy was associated with CSF tau levels and corresponded to the onset age of AD and a decline in attention in patients with c‐MCI. These spatial correlation findings of the hippocampus suggested that the hippocampal subfields may not be equally impacted by normal aging, MCI, and AD, and their atrophy was selectively associated with declines in specific cognitive domains. The presubiculum atrophy was highlighted as a surrogate marker for the AD prognosis along with tau pathology and attention decline.     

A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer's disease

BackgroundOur objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD).MethodsSubjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model.ResultsThe final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale—cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points).ConclusionsA point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.     

Biological heterogeneity in ADNI amnestic mild cognitive impairment

BackgroundPrevious work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.MethodsADNI subjects diagnosed with amnestic MCI (n = 138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.ResultsFour clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.ConclusionsSubjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.     

Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects

Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1-42 and total tau (T-tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F-18 fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.     

Discriminatory and predictive capabilities of enzyme-linked immunosorbent assay and multiplex platforms in a longitudinal Alzheimer’s disease study

BackgroundMultiplex assays such as xMAP have been proposed for the assessment of Alzheimer’s disease (AD) biomarkers amyloid β 42 (Aβ₄₂), tau (Tau), and phosphorylated tau (pTau) in cerebrospinal fluid (CSF). Here, we compared the traditional enzyme-linked immunosorbent assay (ELISA) and xMAP with respect to their: (1) absolute biomarker concentration, (2) ability to distinguish AD from nondemented subjects, (3) ability to monitor AD longitudinally, and (4) ability to predict progression from mild cognitive impairment (MCI) to AD.MethodsWe selected 68 AD, 62 MCI, and 24 nondemented subjects, performed clinical examinations, and obtained CSF at baseline and 2 years later. Aβ₄₂, Tau, and pTau were measured with both ELISA and xMAP.ResultsBiomarker levels differed considerably between the two assays, and the differences were concentration dependent. No differences were observed in ability to distinguish nondemented subjects from AD patients between ELISA (area under curve of 0.84 for Aβ₄₂, 0.79 for Tau, and 0.75 for pTau) and xMAP (area under curve of 0.82 for Aβ₄₂, 0.75 for Tau, and 0.73 for pTau), all P < .05. Increased Aβ₄₂ levels of AD patients at follow-up compared with baseline were detected with ELISA, whereas increased Tau levels for nondemented subjects and MCI patients were only detected with xMAP. The hazard ratios for progression from MCI to AD did not differ between the assays.ConclusionBoth ELISA and multiplex assays can be used to measure AD biomarker levels in CSF to support clinical diagnosis and predict progression from MCI to AD with similar accuracy. Importantly, the assays’ output in absolute biomarker concentrations is remarkably different, and this discrepancy cannot be reconciled with simple correction factors.     

White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy

This study investigates relationships between white matter hyperintensity (WMH) volume, cerebrospinal fluid (CSF) Alzheimer's disease (AD) pathology markers, and brain and hippocampal volume loss. Subjects included 198 controls, 345 mild cognitive impairment (MCI), and 154 AD subjects with serial volumetric 1.5‐T MRI. CSF Aβ₄₂ and total tau were measured (n = 353). Brain and hippocampal loss were quantified from serial MRI using the boundary shift integral (BSI). Multiple linear regression models assessed the relationships between WMHs and hippocampal and brain atrophy rates. Models were refitted adjusting for (a) concurrent brain/hippocampal atrophy rates and (b) CSF Aβ₄₂ and tau in subjects with CSF data. WMH burden was positively associated with hippocampal atrophy rate in controls (P = 0.002) and MCI subjects (P = 0.03), and with brain atrophy rate in controls (P = 0.03). The associations with hippocampal atrophy rate remained following adjustment for concurrent brain atrophy rate in controls and MCIs, and for CSF biomarkers in controls (P = 0.007). These novel results suggest that vascular damage alongside AD pathology is associated with disproportionately greater hippocampal atrophy in nondemented older adults. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.     

The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium

BackgroundIn North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI).MethodsSeven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales.ResultsRecruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects.ConclusionsAcademic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.     

CSF Abeta42, tau and phosphorylated tau correlate with medial temporal lobe atrophy

Cerebrospinal fluid (CSF) biomarkers and medial temporal lobe (MTL) atrophy predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). We investigated the association between the CSF biomarkers and MTL atrophy and the ability of these measures to predict AD in MCI patients in the same study population. The study included 21 MCI patients of whom eight progressed to AD during the study. CSF biomarkers were measured by using ELISA method and volumes of MTL structures were assessed by magnetic resonance imaging (MRI). Abeta42 levels were lower and tau and phospho-tau levels were higher in progressive subjects. The progressive subjects had lower volumes in all MRI measures. Tau and phospho-tau correlated inversely with hippocampal volumes and left entorhinal cortex volume in the whole study group. In the stable group, tau correlated with hippocampal volumes. Abeta42 had a negative correlation whereas phospho-tau exhibited a positive correlation with left hippocampal volume in the progressive group. These results indicate that both measures may reflect the ongoing neurodegenerative process in the progressive MCI patients. However, the order of the changes in the CSF biomarkers and MTL atrophy remain unclear due to a small number of studied subjects and study design.     

Chronic depressive symptomatology and CSF amyloid beta and tau levels in mild cognitive impairment

Objectives

To investigate the association between chronic subsyndromal symptoms of depression (SSD), cerebrospinal fluid (CSF) biomarkers, and neuropsychological performance in individuals with mild cognitive impairment (MCI).

Methods

Participants included 238 older adults diagnosed with MCI from the Alzheimer's Disease Neuroimaging Initiative repository with cognitive and CSF amyloid beta (Aβ_1–42), total tau (t‐tau), and phosphorylated tau (p‐tau) data. The Neuropsychiatric Inventory identified individuals with chronic endorsement (SSD group N = 80) or no endorsement (non‐SSD group N = 158) of depressive symptoms across timepoints. CSF biomarker and cognitive performance were evaluated with linear regression models adjusting for age, education, gender, APOE genotype, global cognitive status, and SSD group.

Results

As compared to the non‐SSD group, the SSD group displayed lower CSF Aβ_1–42 levels (β = ?24.293, S.E. = 6.345, P < 0.001). No group differences were observed for CSF t‐tau (P = 0.497) or p‐tau levels (P = 0.392). Lower CSF Aβ_1–42 levels were associated with poorer performance on learning (β = 0.041, S.E. = 0.018, P = 0.021) and memory (β = ?0.012, S.E. = 0.005, P = 0.031) measures, whereas higher CSF t‐tau levels were associated with poorer performance on measures of global cognition (β = 0.022, S.E = 0.008, P = 0.007) and language (β = ?0.010, S.E = 0.004, P = 0.019). SSD was independently associated with diminished global cognition, learning and memory, language, and executive function performance over and above the effects of CSF biomarkers (all P < 0.05).

Conclusions

MCI participants with SSD displayed diminished CSF Aβ_1–42 levels but did not differ from non‐SSD controls in CSF tau levels. Additionally, CSF biomarkers and SSD independently accounted for variance in cognitive performance, suggesting that these factors may uniquely confer cognitive risk in MCI.     

Diffusion imaging in dementia with Lewy bodies: Associations with amyloid burden,atrophy, vascular factors and clinical features

IntroductionWhite matter disruption in dementia has been linked to a variety of factors including vascular disease and cortical pathology. We aimed to examine the relationship between white matter changes on diffusion tensor imaging (DTI) in DLB and factors including vascular disease, structural atrophy and amyloid burden.MethodsParticipants with DLB (n = 29), Alzheimer's disease (AD, n = 17) and healthy controls (n = 20) had clinical and neuropsychological assessments followed by structural and diffusion tensor 3T MRI and ¹⁸F-Florbetapir PET-CT imaging. Voxelwise statistical analysis of white matter fractional anisotropy (FA) and mean diffusivity (MD) was carried out using Tract-Based Spatial Statistics with family-wise error correction (p < 0.05).ResultsDLB and AD groups demonstrated widespread increased MD and decreased FA when compared with controls. There were no differences between the DLB and AD groups. In DLB, increased MD and decreased FA correlated with decreased grey matter and hippocampal volumes as well as vascular disease. There was no correlation with cortical florbetapir SUVR. The relationship between DTI changes and grey matter/hippocampal volumes remained after including Cumulative Illness Rating Scale-Geriatric vascular score as a covariate.ConclusionsWidespread disruption of white matter tracts is present in DLB and is associated with vascular disease, reduced hippocampal volume and reduced grey matter volume, but not with cortical amyloid deposition. The mechanism behind the correlation observed between hippocampal volume and white matter tract disruption should be investigated in future cohorts using tau imaging, as hippocampal atrophy has been shown to correlate with tau deposition in DLB.     

Cortical changes in incipient Alzheimer's disease

Mild cognitive impairment (MCI) is defined by memory impairment with no impact on daily activities. 10 to 15% of MCI convert to Alzheimer's disease (AD) per year. While structural changes in the cortex of AD patients have been extensively investigated, fewer studies analyzed changes in the years preceding conversion. 46 MCI patients and 20 healthy controls underwent structural 1.0T-weighted high-resolution MR scans at baseline and after 1.4 (SD 0.3) years. All subjects were assessed yearly for up to 4 years with a comprehensive neuropsychological battery. Sixteen of the 46 patients converted to AD (cMCI) while 30 remained stable (sMCI). An accurate voxel-based statistical mesh-model technique (cortical pattern matching) with a related region-of-interest analysis based on networks defined from a Brodmann area atlas (BAs) were used to map gray matter changes over time. At baseline, cMCI patients had 10 to 30% less cortical gray matter volume than healthy controls in regions known to be affected by AD pathology (entorhinal, temporoparietal, posterior cingulate, and orbitofrontal cortex, p=0.0001). Over time, cMCI patients lost more gray matter than sMCI in all brain areas but mainly in the olfactory and in the polysynaptic hippocampal network (more than 8% gray matter loss, p<0.024). sMCI patients had 10 to 20% less volume than controls in the posterior cingulate and orbitofrontal cortex (p<0.008) although their progression over time was significantly slower than cMCI. AD patients in the MCI stage show greater gray matter loss in the olfactory and polysynaptic hippocampal network. These findings are in line with neuropathological knowledge.     

Blood biomarkers in mild cognitive impairment patients: Relationship between analytes and progression to Alzheimer disease dementia

Background and purpose

Blood-based biomarkers are promising tools for the diagnosis of Alzheimer disease (AD) at prodromal stages (mild cognitive impairment [MCI]) and are hoped to be implemented as screening tools for patients with cognitive complaints. In this work, we evaluated the potential of peripheral neurological biomarkers to predict progression to AD dementia and the relation between blood and cerebrospinal fluid (CSF) AD markers in MCI patients referred from a general neurological department.

Methods

A group of 106 MCI patients followed at the Neurology Department of Coimbra University Hospital was included. Data regarding baseline neuropsychological evaluation, CSF levels of amyloid β 42 (Aβ42), Aβ40, total tau (t-Tau), and phosphorylated tau 181 (p-Tau181) were available for all the patients. Aβ42, Aβ40, t-Tau, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels were determined in baseline stored serum and plasma samples by commercial SiMoA (Single Molecule Array) assays. Progression from MCI to AD dementia was assessed at follow-up (mean = 5.8 ± 3.4 years).

Results

At baseline, blood markers NfL, GFAP, and p-Tau181 were significantly increased in patients who progressed to AD at follow-up (p < 0.001). In contrast, plasma Aβ42/40 ratio and t-Tau showed no significant differences between groups. NfL, GFAP, and p-Tau181 demonstrated good diagnostic accuracy to identify progression to AD dementia (area under the curve [AUC] = 0.81, 0.80, and 0.76, respectively), which improved when combined (AUC = 0.89). GFAP and p-Tau181 were correlated with CSF Aβ42. Association of p-Tau181 with NfL was mediated by GFAP, with a significant indirect association of 88% of the total effect.

Conclusions

Our findings highlight the potential of combining blood-based GFAP, NfL, and p-Tau181 to be applied as a prognostic tool in MCI.