It is important to study apathy in Alzheimer's disease (AD) to better understand its underlying neurobiology and develop effective interventions. In the current study, we sought to examine the relationships between longitudinal apathy and regional tau burden in cognitively impaired older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Cigarette smoking in adults is associated with abnormalities in brain neurobiology. Smoking-induced central nervous system oxidative stress (OxS) is a potential mechanism associated with these abnormalities. The goal of this study was to compare cognitively-normal elders on cerebrospinal fluid (CSF) levels of F2-isoprostane biomarkers of OxS.

Progranulin plays an important role in neuroinflammation in Alzheimer's disease (AD) pathophysiology, being upregulated by activated microglia. This study assessed whether cerebrospinal fluid levels of progranulin correlated with structural neuroimaging measures and cognition in 122 cognitively normal individuals, 81 mild cognitive impairment, and 70 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Cognitively normal subjects were classified into 3 groups using the AT(N) system, whereas all mild cognitive impairment and AD patients were A+/TN+. Correlations between progranulin with neuroanatomical measures and cognitive decline were performed within each group. Progranulin was associated with cortical thickening in parietal, occipital, and frontal regions in cognitively normal individuals with amyloid pathology. These subjects also showed cortical thickening compared with A-/TN- subjects, an effect that was partially mediated by progranulin. In addition, higher progranulin correlated with longitudinal cognitive decline. The association between progranulin and cortical thickening, together with regional "brain swelling" in A+/TN- subjects, suggests progranulin contributes to the neuroinflammatory structural changes in preclinical AD.

Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of κw = 0.74/0.72 (MTA left/right), κw = 0.62 (GCA-F) and κw = 0.74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.

Rs9296559 within CD2-associated protein (CD2AP) has been identified as a susceptibility locus for Alzheimer's disease (AD). Recent studies indicated that CD2AP functioned as a regulator of endocytic trafficking to modulate the β-amyloid (Aβ) generation in neurons. Moreover, knockdown of cindr, the Drosophila ortholog of CD2AP, enhanced tau-induced neurodegeneration, implying CD2AP also participated in tau pathology. However, the role of rs9296559 in regulating Aβ and tau metabolism in AD was still unclear.

In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer's disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.

The aggregation and deposition of amyloid-β (Aβ) peptides into plaques is an early event in Alzheimer's disease (AD), which is followed by different aspects of neurodegeneration that can be measured in the cerebrospinal fluid (CSF) or plasma using neurofilament light (NFL), neurogranin (Ng), total Tau (T-Tau), and phosphorylated tau (P-Tau) levels. The relationship between these biomarkers and regional brain atrophy across the different stages of AD remains largely unexplored. In this study, we assessed whether NFL, Ng, T-Tau, and P-Tau levels in CSF and NFL in plasma are associated with cortical thinning and subcortical volume loss in cognitively normal, mild cognitive impairment, and AD subjects with and without Aβ pathology. Our main findings showed that CSF NFL levels were associated with brain atrophy in all groups, but plasma NFL only correlated with atrophy in symptomatic cases. In contrast, Ng was associated with regional brain atrophy only in individuals with Aβ pathology. Altogether, our main findings suggest that Ng is strongly associated with Aβ pathology, whereas NFL is more unspecific.

Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear.

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

Brain complexity, which reflects the ability of the brain to adapt to a changing environment, has been found to be significantly changed with age. However, there is less evidence on the alterations of brain complexity in neurodegenerative disorders such as Alzheimer's disease (AD). Here we investigated the altered complexity of resting-state blood oxygen level-dependent signals in AD-related neurodegeneration using multiscale entropy (MSE) analysis. All participants were recruited from the Alzheimer's Disease Neuroimaging Initiative, including healthy controls (HC, n=62), amnestic mild cognitive impairment (aMCI, n =81) patients, and Alzheimer's disease (AD, n=25) patients. Our results showed time scale-dependent MSE differences across the three groups. In scale=1, significantly changed MSE patterns (HC>aMCI>AD) were found in four brain regions, including the hippocampus, middle frontal gyrus, intraparietal lobe, and superior frontal gyrus. In scale=4, reversed MSE patterns (HC

Several studies support the relation between leptin and Alzheimer's disease (AD). We show that leptin levels in CSF are unchanged as subjects progress to AD. However, in AD hippocampus, leptin signalling was decreased and leptin localization was shifted, being more abundant in reactive astrocytes and less in neurons. Similar translocation of leptin was found in brains from Tg2576 and apoE4 mice. Moreover, an enhancement of leptin receptors was found in hippocampus of young Tg2576 mice and in primary astrocytes and neurons treated with Aβ₁₋₄₂. In contrast, old Tg2576 mice showed decreased leptin receptors levels. Similar findings to those seen in Tg2576 mice were found in apoE4, but not in apoE3 mice. These results suggest that leptin levels are intact, but leptin signalling is impaired in AD. Thus, Aβ accumulation and apoE4 genotype result in a transient enhancement of leptin signalling that might lead to a leptin resistance state over time.

The ability to pool data from multiple MRI scanners is becoming increasingly important with the influx in multi-site research studies. Fast spin echo (FSE) dual spin echo sequences are often chosen for such studies based principally on their short acquisition time and the clinically useful contrasts they provide for assessing gross pathology. The practicality of measuring FSE-T2 relaxation properties has rarely been assessed. Here, FSE-T2 relaxation properties are examined across the three main scanner vendors (General Electric (GE), Philips, and Siemens). The American College of Radiology (ACR) phantom was scanned on four 1.5T platforms (two GE, one Philips, and one Siemens) to determine if the dual echo pulse sequence is susceptible to vendor-based variance. In addition, data from 85 subjects spanning the spectrum of normal aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD) was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to affirm the presence of any phantom based between vendor variance and determine the relationship between this variance and disease. FSE-T2 relaxation properties, including peak FSE-T2 and histogram width, were calculated for each phantom and human subject. Direct correspondence was found between the phantom and human subject data. Peak FSE-T2 of Siemens scanners was consistently at least 20ms prolonged compared to GE and Philips. Siemens scanners showed broader FSE-T2 histograms than the other scanners. Greater variance was observed across GE scanners than either Philips or Siemens. FSE-T2 differences were much greater with scanner vendor than between diagnostic groups, as no significant changes in peak FSE-T2 or histogram width between normal aged, MCI, and AD subject groups were observed. These results indicate that whole brain histogram measures are not sensitive enough to detect FSE-T2 changes between normal aging, MCI, and AD and that FSE-T2 is highly variable across scanner vendors.

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

The human cortical regions for processing high-level visual (HLV) functions of different categories remain ambiguous, especially in terms of their conjunctions and specifications. Moreover, the neurobiology of declined HLV functions in patients with Alzheimer's disease (AD) has not been fully investigated. This study provides a functionally sorted overview of HLV cortices for processing "what" and "where" visual perceptions and it investigates their atrophy in AD and MCI patients. Based upon activation likelihood estimation (ALE), brain regions responsible for processing five categories of visual perceptions included in "what" and "where" visions (i.e., object, face, word, motion, and spatial visions) were analyzed, and subsequent contrast analyses were performed to show regions with conjunctive and specific activations for processing these visual functions. Next, based on the resulting ALE maps, the atrophy of HLV cortices in AD and MCI patients was evaluated using voxel-based morphometry. Our ALE results showed brain regions for processing visual perception across the five categories, as well as areas of conjunction and specification. Our comparisons of gray matter (GM) volume demonstrated atrophy of three "where" visual cortices in late MCI group and extensive atrophy of HLV cortices (25 regions in both "what" and "where" visual cortices) in AD group. In addition, the GM volume of atrophied visual cortices in AD and MCI subjects was found to be correlated to the deterioration of overall cognitive status and to the cognitive performances related to memory, execution, and object recognition functions. In summary, these findings may add to our understanding of HLV network organization and of the evolution of visual perceptual dysfunction in AD as the disease progresses.

The goal of this study was to compare regional brain atrophy patterns in cognitively unimpaired (CU) older adults with and without brain accumulation of amyloid-β (Aβ) to elucidate contributions of Aβ, age, and other variables to atrophy rates. In 80 CU participants from the Alzheimer's Disease Neuroimaging Initiative, we determined effects of Aβ and age on longitudinal, regional atrophy rates, while accounting for confounding variables including sex, APOE ε4 genotype, white matter lesions, and cerebrospinal fluid total and phosphorylated tau levels. We not only found overlapping patterns of atrophy in Aβ+ versus Aβ- participants but also identified regions where atrophy pattern differed between the 2 groups. Higher Aβ load was associated with increased longitudinal atrophy in the entorhinal cortex, amygdala, and hippocampus, even when accounting for age and other variables. Age was associated with atrophy in insula, fusiform gyrus, and isthmus cingulate, even when accounting for Aβ. We found age by Aβ interactions in the postcentral gyrus and lateral orbitofrontal cortex. These results elucidate the separate and related effects of age, Aβ, and other important variables on longitudinal brain atrophy rates in CU older adults.

We investigated the effect of baseline Aβ, sex, and APOE on longitudinal tau accumulation in cerebrospinal fluid (CSF) in clinically normal older adults. Two hundred thirty-nine participants (aged 56-89 years, clinical dementia rating = 0) underwent serial CSF collection for Aβ1-42, total-tau (t-tau) and phospho-tau181P (p-tau). We used preprocessed data from fully automated Roche Elecsys immunoassays. A series of linear regressions were used to examine cross-sectional effects of Aβ1-42, sex, and APOEε4 on baseline CSF tau and linear mixed models for longitudinal changes in CSF tau. Cross-sectionally, CSF t-tau and p-tau were associated with abnormal Aβ1-42 and APOEε4 but not with sex. Longitudinally, low baseline CSF Aβ1-42 levels, but not APOEε4 or sex, predicted faster p-tau accumulation. The relationship between baseline CSF Aβ1-42 and tau accumulation was strongest in APOEε4 carriers, and particularly female carriers, relative to other groups. The current findings support an association between baseline CSF Aβ1-42 and changes in CSF tau. Elevated risk in females, apparent only in carriers, reinforces findings of sex-related vulnerability in those with genetic predisposition for Alzheimer's disease.

Insulin resistance and type 2 diabetes are associated with cognitive decline and increased risk for Alzheimer's disease (AD). Relatively few studies have assessed the impact of metabolic dysfunction on conversion to AD in mild cognitive impairment (MCI), and it is unclear whether glycemic status is associated with clinically relevant measures of cognitive decline and brain structure in MCI. This study used the Alzheimer's Disease Neuroimaging Initiative database to examine the relationship of baseline glycemia with conversion to AD and longitudinal clinical, cognitive, and imaging measures of decline. Subjects with MCI (n = 264) with baseline and 2-year Clinical Dementia Rating data available were classified according to American Diabetes Association criteria for fasting glucose at baseline. The groups were normoglycemic (fasting glucose, <100 mg/dL; n = 167) or impaired glycemia (fasting glucose, ≥ 100 mg/dL, n = 97). The impaired glycemia group included individuals with fasting glucose that either reached the American Diabetes Association cut point for impaired fasting glucose or individuals with diagnosed diabetes. Two-year change in Clinical Dementia Rating-Sum of Boxes, cognitive performance testing (global cognition), brain volume (whole-brain and hippocampal volume), fluorodeoxyglucose-positron emission tomography, and conversion to AD were assessed. Subjects with normoglycemia at baseline had less functional (Clinical Dementia Rating-Sum of Boxes) and global cognitive decline over 2 years than subjects with impaired glycemia. Subjects with normoglycemia also lost less whole-brain volume and exhibited lower conversion from MCI to AD. There was no difference in hippocampal volume change or fluorodeoxyglucose-positron emission tomography between groups. These results suggest that baseline glycemia is related to cognitive decline and progression to AD.

Biomarkers associated with Alzheimer's disease (AD)-like brain atrophy in healthy individuals may identify mechanisms involved in early stage AD. Aside from cerebrospinal fluid (CSF) β-amyloid42 (Aβ42) and tau, no studies have tested associations between CSF proteins and AD-like brain atrophy. We studied 90 healthy elders, who underwent lumbar puncture at baseline, and serial magnetic resonance imaging scans for up to 4 years. We tested statistical effects of baseline CSF proteins (N = 70 proteins related to Aβ42-metabolism, microglial activity, and synaptic/neuronal function) on atrophy rates in 7 AD-related regions. Besides the effects of Aβ42 and phosphorylated tau (P-tau) that were seen in several regions, novel CSF proteins were found to have effects in inferior and middle temporal cortex (including apolipoprotein CIII, apolipoprotein D, and apolipoprotein H). Several proteins (including S100β and matrix metalloproteinase-3) had effects that depended on the presence of brain Aβ pathology, as measured by CSF Aβ42. Other proteins (including P-tau and apolipoprotein D) had effects even after adjusting for CSF Aβ42. The statistical effects in this exploratory study were mild and not significant after correction for multiple comparisons, but some of the identified proteins may be associated with brain atrophy in healthy persons. Proteins interacting with CSF Aβ42 may be related to Aβ brain pathology, whereas proteins associated with atrophy even after adjusting for CSF Aβ42 may be related to Aβ-independent mechanisms.

Mild cognitive impairment (MCI) of the amnestic type is considered to be a transitionary stage between healthy aging and Alzheimer's disease (AD). Previous studies have demonstrated that intrinsic functional connectivity of the default network (DN) is altered in normal aging and AD and impacts both within- and between-network connectivity. Although changes within the DN have been reported in MCI, it remains uncertain how interactions with other large-scale brain networks are altered in this prodromal stage of AD. We investigated within- and between-network connectivity in healthy older adults (HOAs) and older adults with MCI across 3 canonical brain networks: DN, dorsal attention network, and frontoparietal control network. We also assessed how patterns of functional connectivity among the 3 networks predicted cognitive status and age using multivariate partial least squares. A total of 91 MCI and 71 HOA resting-state scans were analyzed from the Alzheimer's Disease Neuroimaging Initiative. There were 3 key findings. First, a circumscribed pattern of greater between-network and interhemispheric connectivity was associated with higher cognitive status in HOAs. Second, for individuals with MCI, cognitive status was positively associated with a more distributed, less-differentiated pattern of intrinsic functional connectivity across the 3 networks. Finally, greater within-network functional connectivity was positively associated with cognitive status for HOAs irrespective of age; however, this compensation-like effect diminished with increasing age for participants with MCI. Although reliable differences between healthy aging and MCI in the intrinsic network architecture of the brain are apparent, these differences emerge as shifting associations between network interactivity, cognitive functioning, and age.