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In vivo PET imaging studies of young-adult carriers of the apolipoprotein E ε4 allele (APOEε4), the major Alzheimer's disease (AD) susceptibility gene, have demonstrated declines in glucose metabolism in brain areas later vulnerable to AD, such as posterior cingulate cortex, decades before the possible onset of symptoms. We have previously shown in postmortem studies that such metabolic declines in AD are associated with brain regional mitochondrial dysfunction. To determine whether young adult at-risk individuals demonstrate similar mitochondrial functional decline, we histochemically assessed postmortem tissues from the posterior cingulate cortex of young-adult carriers and noncarriers of APOEε4. At-risk ε4 carriers had lower mitochondrial cytochrome oxidase activity than noncarriers in posterior cingulate cortex, particularly within the superficial cortical lamina, a pattern similar to that seen in AD patients. Except for one 34 year-old ε4 homozygote, the ε4 carriers did not have increased soluble amyloid-β, histologic amyloid-β, or tau pathology in this same region. This functional biomarker may prove useful in early detection and tracking of AD and indicates that mitochondrial mechanisms may contribute to the predisposition to AD before any evidence of amyloid or tau pathology. [ABSTRACT FROM AUTHOR]

Background: The APOE epsilon4 allele is associated with the risk of late-onset Alzheimer's disease. The age at which memory decline diverges among persons who are homozygous for the APOE epsilon4 allele, those who are heterozygous for the allele, and noncarriers is unknown.Methods: Using local advertisements, we recruited cognitively normal subjects between the ages of 21 and 97 years, who were grouped according to their APOE epsilon4 status. We then followed the subjects with longitudinal neuropsychological testing. Anyone in whom mild cognitive impairment or dementia developed during follow-up was excluded. We compared the rates of decline in predetermined cognitive measures between carriers and noncarriers of the APOE epsilon4 allele, using a mixed model for longitudinal change with age.Results: We analyzed 815 subjects: 317 APOE epsilon4 carriers (79 who were homozygous for the APOE epsilon4 allele and 238 who were heterozygous) and 498 noncarriers. Carriers, as compared with noncarriers, were generally younger (mean age, 58.0 vs. 61.4 years; P<0.001) and were followed for a longer period (5.3 vs. 4.7 years, P=0.01), with an equivalent duration of formal education (15.4 years) and proportion of women (69%). Longitudinal decline in memory in carriers began before the age of 60 years and showed greater acceleration than in noncarriers (P=0.03), with a possible allele-dose effect (P=0.008). We observed similar although weaker effects on measures of visuospatial awareness and general mental status.Conclusions: Age-related memory decline in APOE epsilon4 carriers diverges from that of noncarriers before the age of 60 years, despite ongoing normal clinical status. [ABSTRACT FROM AUTHOR]

There are no Food and Drug Administration (FDA)-approved medications for the medical management of frontotemporal dementia and its related disorders, so all management recommendations are necessarily off-label and borrowed from experience with Alzheimer's disease, psychiatric disease, and related medical illnesses. Six areas of pharmacotherapeutic consideration are prevention (primary and secondary), intellectual decline, behavioral disorders (such as depression, anxiety, and psychosis), sleep disorders, frequently associated disorders (including motor neuron disease), and abrupt decline. In addition to pharmacotherapy, important lifestyle issues confronting the clinician include driving cessation, securing any weapons maintained at home, assisted living, and caregiver burnout. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to characterize the relationship between whole brain atrophy rates and three levels of genetic risk for Alzheimer's disease in cognitively normal persons. The authors previously found accelerated whole brain atrophy rates in patients with probable Alzheimer's disease by computing changes in brain volume from sequential magnetic resonance images (MRIs).Methods: The authors assessed 36 late-middle-aged persons from three genetic groups: those with two, one, and no copies of the apolipoprotein E (APOE) epsilon4 allele, a common Alzheimer's disease susceptibility gene. The participants had clinical ratings, neuropsychological tests, and volumetric T1-weighted MRIs during a baseline visit and again approximately 2 years later. Two different image-analysis techniques, brain boundary shift integration and iterative principal component analysis, were used to compute whole brain atrophy rates.Results: While there were no baseline, follow-up, or between-visit differences in the clinical ratings or neuropsychological test scores among the three subject groups, whole brain atrophy rates were significantly greater in the epsilon4 homozygote group than in noncarriers and were significantly correlated with epsilon4 gene dose (i.e., the number of epsilon4 alleles in a person's APOE genotype).Conclusion: Since APOE epsilon4 gene dose is associated with an increased risk of Alzheimer's disease and a younger median age at dementia onset, this study suggests an association between the risk of Alzheimer's disease and accelerated brain atrophy rates before the onset of cognitive impairment. [ABSTRACT FROM AUTHOR]

Aims: We estimated the prevalence and incidence rates of depressive symptoms and the role of vascular risk factors and sociodemographic variables on the occurrence of depressive symptoms in mild cognitive impairment (MCI). Methods: In the Italian Longitudinal Study on Aging, 2,963 individuals from 5,632 65- to 84-year-old subjects were evaluated at the 1st and 2nd survey, with a 3.5-year follow-up. Dementia and MCI were classified using current criteria. Depressive symptoms were measured with the Geriatric Depression Scale. Results: Among the 2,963 participants, 139 prevalent MCI cases were diagnosed with a depressive symptoms prevalence rate of 63.3%. During the 3.5-year follow-up, we estimated an incidence rate of depressive symptoms of 29.6 per 100 person-years. No sociodemographic variables or vascular risk factors modified the incidence of depressive symptoms in cognitively stable MCI patients or in MCI patients who reverted to normal cognition. Conclusion: In our population, there was a high prevalence and incidence of depressive symptoms in MCI. Our findings do not provide support for a possible role of vascular risk factors in the development of depressive symptoms in MCI, although these findings were based on relatively few cases of MCI, limiting the capacity to draw definitive conclusions. Copyright (c) 2007 S. Karger AG, Basel. [ABSTRACT FROM AUTHOR]

Objective: Mutations in the progranulin (PGRN) gene were recently described as the cause of ubiquitin positive frontotemporal dementia (FTD). Clinical and pathological overlap between amyotrophic lateral sclerosis (ALS) and FTD prompted us to screen PGRN in patients with ALS and ALS-FTD.Methods: The PGRN gene was sequenced in 272 cases of sporadic ALS, 40 cases of familial ALS and in 49 patients with ALS-FTD.Results: Missense changes were identified in an ALS-FTD patient (p.S120Y) and in a single case of limb onset sporadic ALS (p.T182M), although the pathogenicity of these variants remains unclear.Conclusion: PGRN mutations are not a common cause of ALS phenotypes. [ABSTRACT FROM AUTHOR]

Presents abstracts for articles which appear in the April 1, 1998 issue of 'Cephalalgia.' Effective prophylactic therapy for cyclic vomiting in children using amitriptyline or cyproheptadine; Cerebral venous thrombosis and anticardioliptin antibodies; Giant cell (temporal) arteritis; Therapy of acute migraine attacks and migraine prophylaxis-guidelines of the German Migraine and Headache Society; Therapy of cluster headache guidelines of the German Migraine Headache-Society; Periodic syndrome and migraine in children and adolecents; Others.

Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD)., Patients and Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation. ADRET is a bidirectional encoder representations from transformers style self-supervised learning convolutional neural network pretrained on a large data set of retinal color photographs from UK Biobank. The models' performance to distinguish AD from non-AD was determined using mean accuracy, sensitivity, specificity, and receiving operating curves. The generated attention heatmaps were analyzed for distinctive features., Results: The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P <.001) and our institutional testing data sets (98.90% vs 94.17%; P =.04). No major differences were noted between the original and binary vessel segmentation and between both eyes vs single-eye models. Attention heatmaps obtained from patients with AD highlighted regions surrounding small vascular branches as areas of highest relevance to the model decision making., Conclusion: A bidirectional encoder representations from transformers style self-supervised convolutional neural network pretrained on a large data set of retinal color photographs alone can screen symptomatic AD with high accuracy, better than U-Net-pretrained models. To be translated in clinical practice, this methodology requires further validation in larger and diverse populations and integrated techniques to harmonize fundus photographs and attenuate the imaging-associated noise.

Objective: To evaluate the performance of Alzheimer disease (AD) cerebrospinal fluid (CSF) biomarkers in a tertiary neurology clinic setting with high frequency of non-AD cases, including normal pressure hydrocephalus (NPH)., Methods: There were 534 patients who underwent AD CSF biomarkers (Roche Elecsys Aβ42, p-Tau181, total-Tau) from April 1, 2020, through April 23, 2021. A behavioral neurologist blinded to CSF results assigned a clinical diagnosis retrospectively on the basis of consensus criteria, and a neuroradiologist blinded to the diagnosis and CSF studies graded brain magnetic resonance images for indicators of CSF dynamics disorders. Associations between biomarkers, diagnoses, and imaging were assessed by χ 2 , analysis of covariance, and linear regression methods., Results: Median age at time of testing was 67 years (range, 19 to 96 years), median symptom duration was 2 years (range, 0.4 to 28 years), and median Short Test of Mental Status score was 30 (range, 0 to 38). Clinical diagnoses significantly correlated with different CSF biomarker values (χ 2 =208.3; P=10e-4). p-Tau181/Aβ42 ratios above 0.023 positively correlated with Alzheimer dementia (more than individual measures). This ratio also had the best performance for differentiating Alzheimer dementia from NPH (area under the curve, 0.869). Imaging markers supportive of CSF dynamics disorders correlated with low Aβ42, p-Tau181, and total-Tau., Conclusion: In a heterogeneous clinical population, abnormal p-Tau181/Aβ42 ratios (>0.023) have the strongest association with Alzheimer dementia and probably represent a comorbid AD pathologic component in persons clearly matching non-AD neurodegenerative syndromes. Altered CSF dynamics were associated with lower concentrations of AD CSF biomarkers regardless of clinical diagnosis, but the ratio compensates for these changes. In the appropriate clinical setting, an isolated abnormal Aβ42 should prompt consideration of NPH., (Copyright © 2024 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 cases and 3,153 controls, and meta-analysis with the Dementia-seq cohort, compiled from 26 institutions/brain banks in the United States, Europe and Australia. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyses, we further identify for the first time genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified VIPR1 , RBPJL , and L3MBTL1 as novel subtype specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signalling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.

Introduction: Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia., Methods: In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined., Results: Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions., Discussion: The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bergamino, Burke, Sabbagh, Caselli, Baxter and Stokes.)

Background: Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques., Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio., Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score., Results: Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors., Conclusions: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage., Competing Interests: None of the authors has any potential conflict of interest related to this article. Data used in this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative database (https://adni.loni.usc.edu).

Introduction: Resting-state functional magnetic resonance imaging (fMRI) graph theory may help detect subtle functional connectivity changes affecting memory prior to impairment., Methods: Cognitively normal apolipoprotein E (APOE) ε4 carriers/noncarriers underwent longitudinal cognitive assessment and one-time MRI. The relationship of left/right hippocampal connectivity and memory trajectory were compared between carriers/noncarriers., Results: Steepness of verbal memory decline correlated with decreased connectivity in the left hippocampus, only among APOE ε4 carriers. Right hippocampal metrics were not correlated with memory and there were no significant correlations in the noncarriers. Verbal memory decline correlated with left hippocampal volume loss for both carriers and noncarriers, with no other significant volumetric findings., Discussion: Findings support early hippocampal dysfunction in intact carriers, the AD disconnection hypothesis, and left hippocampal dysfunction earlier than the right. Combining lateralized graph theoretical metrics with a sensitive measure of memory trajectory allowed for detection of early-stage changes in APOE ε4 carriers before symptoms of mild cognitive impairment are present., Highlights: Graph theory connectivity detects preclinical hippocampal changes in APOE ε4 carriers. The AD disconnection hypothesis was supported in unimpaired APOE ε4 carriers. Hippocampal dysfunction starts asymmetrically on the left., (© 2023 the Alzheimer's Association.)

Cerebral white matter rarefaction (CWMR) was considered by Binswanger and Alzheimer to be due to cerebral arteriolosclerosis. Renewed attention came with CT and MR brain imaging, and neuropathological studies finding a high rate of CWMR in Alzheimer disease (AD). The relative contributions of cerebrovascular disease and AD to CWMR are still uncertain. In 1181 autopsies by the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND), large-format brain sections were used to grade CWMR and determine its vascular and neurodegenerative correlates. Almost all neurodegenerative diseases had more severe CWMR than the normal control group. Multivariable logistic regression models indicated that Braak neurofibrillary stage was the strongest predictor of CWMR, with additional independently significant predictors including age, cortical and diencephalic lacunar and microinfarcts, body mass index, and female sex. It appears that while AD and cerebrovascular pathology may be additive in causing CWMR, both may be solely capable of this. The typical periventricular pattern suggests that CWMR is primarily a distal axonopathy caused by dysfunction of the cell bodies of long-association corticocortical projection neurons. A consequence of these findings is that CWMR should not be viewed simply as "small vessel disease" or as a pathognomonic indicator of vascular cognitive impairment or vascular dementia., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Association of Neuropathologists, Inc. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Plasma neurofilament light (NfL) is an indicator of neurodegeneration and/or neuroaxonal injury in persons with Alzheimer's disease (AD) and a wide range of other neurological disorders. Here, we characterized and compared plasma NfL concentrations in cognitively unimpaired (CU) late-middle-aged and older adults with two, one, or no copies of the APOE ε4 allele, the major genetic risk factor for AD. We then assessed plasma NfL associations with brain imaging measurements of AD-related neurodegeneration (hippocampal atrophy and a hypometabolic convergence index [HCI]), brain imaging measurements of amyloid-β plaque burden, tau tangle burden and white matter hyperintensity volume (WMHV), and delayed and total recall memory scores., Methods: Plasma NfL concentrations were measured in 543 CU 69 ± 9 year-old participants in the Arizona APOE Cohort Study, including 66 APOE ε4 homozygotes (HM), 165 heterozygotes (HT), and 312 non-carriers (NC). Robust regression models were used to characterize plasma NfL associations with APOE ε4 allelic dose before and after adjustment for age, sex, and education. They were also used to characterize plasma NfL associations with MRI-based hippocampal volume and WMHV measurements, an FDG PET-based HCI, mean cortical PiB PET measurements of amyloid-β plaque burden and meta-region-of-interest (meta-ROI) flortaucipir PET measurements of tau tangle burden, and Auditory Verbal Learning Test (AVLT) Delayed and Total Recall Memory scores., Results: After the adjustments noted above, plasma NfL levels were significantly greater in APOE ε4 homozygotes and heterozygotes than non-carriers and significantly associated with smaller hippocampal volumes (r =  - 0.43), greater tangle burden in the entorhinal cortex and inferior temporal lobes (r = 0.49, r = 0.52, respectively), and lower delayed (r =  - 0.27), and total (r =  - 0.27) recall memory scores (p < 0.001). NfL levels were not significantly associated with PET measurements of amyloid-β plaque or total tangle burden., Conclusions: Plasma NfL concentrations are associated with the APOE ε4 allele, brain imaging biomarkers of neurodegeneration, and less good recall memory in CU late-middle-aged and older adults, supporting its value as an indicator of neurodegeneration in the preclinical study of AD., (© 2023. The Author(s).)

Background: Older age is a major risk factor for severe COVID-19 disease which has been associated with a variety of neurologic complications, both acutely and chronically., Objective: We sought to determine whether milder COVID-19 disease in older vulnerable individuals is also associated with cognitive and behavioral sequelae., Methods: Neuropsychological, behavioral, and clinical outcomes before and after contracting COVID-19 disease, were compared in members of two ongoing longitudinal studies, the Arizona APOE Cohort and the national Alzheimer's Disease Research Center (ADRC)., Results: 152 APOE and 852 ADRC cohort members, mean age overall roughly 70 years, responded to a survey that indicated 21 APOE and 57 ADRC members had contracted COVID-19 before their ensuing (post-COVID) study visit. The mean interval between test sessions that preceded and followed COVID was 2.2 years and 1.2 years respectively for the APOE and ADRC cohorts. The magnitude of change between the pre and post COVID test sessions did not differ on any neuropsychological measure in either cohort. There was, however, a greater increase in informant (but not self) reported cognitive change in the APOE cohort (p = 0.018), but this became nonsignificant after correcting for multiple comparisons., Conclusion: Overall members of both cohorts recovered well despite their greater age-related vulnerability to more severe disease.

Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development)., Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements., Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction., Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics., Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

Background: Alzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans., Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline., Methods: Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry., Results: We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort., Conclusion: Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

Blood-based biomarkers are needed for the early diagnosis of Alzheimer's disease (AD). We analyzed longitudinal human plasma samples from AD and control cases to identify biomarkers for the early diagnosis of AD. Plasma samples were grouped based on clinical diagnosis at the time of collection: AD, mild cognitive impairment (MCI), and pre-symptomatic (preMCI). Samples were analyzed by ELISA using a panel of reagents against nine different AD-related amyloid-β (Aβ), tau, or TDP-43 variants. Receiver operating characteristic (ROC) curves of different biomarker panels for different diagnostic sample groups were determined. Analysis of all of the samples gave a sensitivity of 92% and specificity of 76% for the diagnosis of AD. Early-stage diagnosis of AD, utilizing only the preMCI and MCI samples, identified 88% of AD cases. Using sex-biased biomarker panels, early diagnosis of AD cases improved to 96%. Using the sex-biased panels, we also identified 6 of the 25 control group cases as being at high risk of AD, which is consistent with what is expected given the advanced age of the control cases. Specific AD-associated protein variants are effective blood-based biomarkers for the early diagnosis of AD. Notably, significant differences were observed in biomarker profiles for the early detection of male and female AD cases.

Background: Alzheimer's disease (AD) biomarkers have provided a unique opportunity to understand AD pathogenesis and monitor treatment responses. However, exercise trials show mixed effects on imagining and cerebrospinal fluid biomarkers of AD. The feasibility and effects of exercise on plasma biomarkers remain unknown. The primary objective of this study was to examine the feasibility of recruitment, retention, and blood sample collection in community-dwelling older adults with mild-to-moderate AD dementia. Secondarily, it estimated the preliminary effects of 6-month aerobic and stretching exercise on plasma amyloid-β 42 and Aβ 40  (Aβ 42/40 ) ratio, phosphorylated tau (p-tau) 181, and total tau (t-tau)., Methods: This pilot study was implemented in year 2 of the 2-parallel group FIT-AD trial that randomized 96 participants on a 2:1 allocation ratio to moderate-intensity cycling or low-intensity stretching for 20-50 min, 3 times/week for 6 months with 6-month follow-up. Investigators (except for the statistician) and data collectors were blinded to group assignment. Fasting blood samples were collected from 26 participants at baseline and 3 and 6 months. Plasma Aβ 42 , Aβ 40 , p-tau181, and t-tau were measured using Simoa™ assays. Data were analyzed using intention-to-treat, Cohen's d, and linear mixed models., Resultss: The sample averaged 77.6±6.99 years old and 15.4±3.00 years of education with 65% being male and 96.2% being apolipoprotein epsilon 4 gene carriers. The recruitment rate was 76.5%. The retention rate was 100% at 3 months and 96.2% at 6 months. The rate of blood collection was 88.5% at 3 months and 96.2% at 6 months. Means (standard deviation) of within-group 6-month difference in the stretching and cycling group were 0.001 (0.012) and -0.001 (0.010) for Aβ 42/40 ratio, 0.609 (1.417) pg/mL and 0.101(1.579) pg/mL for p-tau181, and -0.020 (0.279) pg/mL and -0.075 (0.215) pg/mL for t-tau. Effect sizes for within-group 6-month difference were observed for p-tau181 in stretching (d=0.43 [-0.33, 1.19]) and t-tau in cycling (-0.35 [-0.87, 0.17])., Conclusions: Blood collections with fasting were well received by participants and feasible with high recruitment and retention rates. Plasma biomarkers of AD may be modifiable by exercise intervention. Important design considerations are provided for future Phase III trials., Trials Registration: ClinicalTrials.gov Identifier: NCT01954550 and posted on October 1, 2013., (© 2022. The Author(s).)

Background: Advanced diffusion-based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline., Purpose: To assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups., Study Type: Prospective/longitudinal., Population: Twelve CI patients (75% female) and 13 HC subjects (69% female)., Field Strength/sequence: 3 T; Spin-Echo-IVIM-DWI., Assessment: Two MRI scans were performed with a 12-month interval. ADC and IVIM-DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel-based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters., Statistical Tests: Analysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial-η 2 and Hedges' g were used for effect-size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant., Results: Significant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM-D, and IVIM-f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08 . 10 -3 vs. 1.09 ± 0.07 . 10 -3  mm 2 /sec; IVIM-D: 0.82 ± 0.01 . 10 -3 vs. 0.73 ± 0.01 . 10 -3  mm 2 /sec; and IVIM-f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM-D, and IVIM-f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05 . 10 -3 vs. 1.50 ± 0.07 . 10 -3  mm 2 /sec; IVIM-D: 0.87 ± 0.01 . 10 -3 vs. 0.94 ± 0.02 . 10 -3  mm 2 /sec; and IVIM-f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM-D, and IVIM-f negatively correlated with ΔMoCA score (ρ = -0.49, -0.51, and -0.50, respectively)., Data Conclusion: These findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM-based metrics., Level of Evidence: 2 TECHNICAL EFFICACY STAGE: 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. Tau tangle is the specific protein pathological hallmark of AD and plays a crucial role in leading to dementia-related structural deformations observed in magnetic resonance imaging (MRI) scans. The volume loss of hippocampus is mainly related to the development of AD. Besides, apolipoprotein E ( APOE ) also has significant effects on the risk of developing AD. However, few studies focus on integrating genotypes, MRI, and tau deposition to infer multimodal relationships. In this paper, we proposed a federated chow test model to study the synergistic effects of APOE and tau on hippocampal morphometry. Our experimental results demonstrate our model can detect the difference of tau deposition and hippocampal atrophy among the cohorts with different genotypes and subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal tau in the homozygote cohort. Our model will provide novel insight into the neural mechanisms about the individual impact of APOE and tau deposition on brain imaging.

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wu, Chen, Wang, Caselli, Thompson, Wang and Wang.)

Background: A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes., Objective: We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61)., Methods: Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs., Results: The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC., Conclusion: The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wu, Dong, Zhang, Su, Wu, Caselli, Reiman, Ye, Lepore, Chen, Thompson and Wang.)

Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).

Biomarker assisted preclinical/early detection and intervention in Alzheimer's disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wu, Dong, Gui, Zhang, Su, Chen, Thompson, Caselli, Reiman, Ye and Wang.)

An effective presymptomatic diagnosis and treatment of Alzheimer's disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention.

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies., Competing Interests: Declaration of Competing Interest All authors claim no conflict of interest with the current research., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease. It is presently only accurately diagnosed at an advanced stage by a series of motor deficits, which are predated by a litany of non-motor symptoms manifesting over years or decades. Aberrant epigenetic modifications exist across a range of diseases and are non-invasively detectable in blood as potential markers of disease. We performed comparative analyses of the methylome and transcriptome in blood from PD patients and matched controls. Our aim was to characterize DNA methylation and gene expression patterns in whole blood from PD patients as a foundational step toward the future goal of identifying molecular markers that could predict, accurately diagnose, or track the progression of PD. We found that differentially expressed genes (DEGs) were involved in the processes of transcription and mitochondrial function and that PD methylation profiles were readily distinguishable from healthy controls, even in whole-blood DNA samples. Differentially methylated regions (DMRs) were functionally varied, including near transcription factor nuclear transcription factor Y subunit alpha ( NFYA ), receptor tyrosine kinase DDR1 , RING finger ubiquitin ligase ( RNF5 ), acetyltransferase AGPAT1 , and vault RNA VTRNA2-1 . Expression quantitative trait methylation sites were found at long non-coding RNA PAX8-AS1 and transcription regulator ZFP57 among others. Functional epigenetic modules were highlighted by IL18R1 , PTPRC , and ITGB2 . We identified patterns of altered disease-specific DNA methylation and associated gene expression in whole blood. Our combined analyses extended what we learned from the DEG or DMR results alone. These studies provide a foundation to support the characterization of larger sample cohorts, with the goal of building a thorough, accurate, and non-invasive molecular PD biomarker., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Henderson, Wang, Meechoovet, Siniard, Naymik, De Both, Huentelman, Caselli, Driver-Dunckley and Dunckley.)

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer's disease (AD) have the potential to inform a cognitively unimpaired (CU) person's likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures., Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI)., Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years ("progressors") and 80 matched adults who remained CU for at least 4 years ("nonprogressors"). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI., Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume., Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes., Purpose: To characterize differences in voxel-based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics., Study Type: Prospective/cross-sectional., Population: In all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC)., Field Strength/sequence: 3T/magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and IVIM-DWI ASSESSMENT: All participants completed a cognitive screening battery prior to MRI. IVIM-DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T 1 -weighted MP-RAGE structural images. Group-wise templates were used to compare across groups., Statistical Tests: Analysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post-hoc comparisons using Bonferroni correction) for group comparisons. Partial-η 2 and Hedges' g were used for effect-size analysis. Spearman's correlations (false discovery rate [FDR]-corrected) for the relationship between cognitive scores and imaging., Results: Clusters of significant group-wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM-D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel-based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR)., Data Conclusion: These findings suggest that IVIM-DWI metrics may be earlier biomarkers for AD-related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes., Level of Evidence: 2 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1811-1826., (© 2020 International Society for Magnetic Resonance in Medicine.)

Objective: To reassess the role of amyloid beta (Aβ) and the amyloid precursor protein (APP) system in the pathogenesis of Alzheimer's disease (AD)., Background: APP is a cell adhesion molecule that has been highly conserved over the course of phylogeny that has critical roles in brain development, synaptic plasticity, and the brain's intrinsic immune system. The amyloid cascade hypothesis describes a relatively linear, deterministic sequence of events triggered by a gain of Aβ peptide fragment toxicity that results in neurodegeneration and cognitive loss, yet well designed immunotherapy and beta secretase inhibitor trials that have successfully targeted Aβ have failed to have any consistent effects on the steady decline of cognition., New/updated Hypothesis: Mutations of the APP and presenilin genes not only alter the ratio of longer to shorter Aβ fragments (resulting in a gain of Aβ toxicity), but also disrupt the normal homeostatic roles of their respective proteins. The evolutionary history, physiological importance, and complexity of the APP and presenilin systems, as well as other critical components including tau and apolipoprotein E (APOE) imply that altered function of such systems could have severe consequences that include but need not be limited to a gain of Aβ toxicity and would more generally result in altered homeostasis of APP-related functions., Major Challenges Addressed by Our Hypothesis: Challenges that a loss of APP homeostasis addresses better than the more limited gain of Aβ toxicity model include the topographic mismatches between Aβ and tau pathology, the profile and chronology of cognitive and biomarker changes that precede the clinical expression of mild cognitive impairment and dementia, and the disappointments of Aβ targeted therapeutics among others., Linkage to Other Major Theories: The importance of APP, α- and β-secretases, the presenilins and γ-secretase, as well as tau was recognized by the authors of the amyloid cascade hypothesis, and has since led multiple investigators to propose alternative, more balanced hypotheses including reduced homeostasis and frank loss-of-function of key components that include but go beyond the currently envisioned linear model of Aβ toxicity., (© 2020 the Alzheimer's Association.)

Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton's method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβ- normal controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker.

Objective: To test the hypothesis that ApoE isoforms affect mitochondrial structure and function that are related to cognitive impairment in Alzheimer disease (AD), we systematically investigated the effects of ApoE isoforms on mitochondrial biogenesis and dynamics, oxidative stress, synapses, and cognitive performance in AD., Methods: We obtained postmortem human brain tissues and measured proteins that are responsible for mitochondrial biogenesis (peroxisome proliferator-activated receptor-gamma coactivator-1α [PGC-1α] and sirtuin 3 [SIRT3]), for mitochondrial dynamics (mitofusin 1 [MFN1], mitofusin 2 [MFN2], and dynamin-like protein 1 [DLP1]), for oxidative stress (superoxide dismutase 2 [SOD2] and forkhead-box protein O3a [Foxo3a]), and for synapses (postsynaptic density protein 95 [PSD95] and synapsin1 [Syn1]). A total of 46 cases were enrolled, including ApoE-ɛ4 carriers (n = 21) and noncarriers (n = 25)., Results: Levels of these proteins were compared between ApoE-ɛ4 carriers and noncarriers. ApoE-ɛ4 was associated with impaired mitochondrial structure and function, oxidative stress, and synaptic integrity in the human brain. Correlation analysis revealed that mitochondrial proteins and the synaptic protein were strongly associated with cognitive performance., Conclusion: ApoE isoforms influence mitochondrial structure and function, which likely leads to alteration in oxidative stress, synapses, and cognitive function. These mitochondria-related proteins may be a harbinger of cognitive decline in ApoE-ɛ4 carriers and provide novel therapeutic targets for prevention and treatment of AD., (© 2020 American Academy of Neurology.)