Ushering in the study and treatment of preclinical Alzheimer disease

PET Imaging in Alzheimer Disease: Pathology and Research Insights for Technologists

Alzheimer disease (AD) is the sixth leading cause of death in the United States and is projected to affect over 13 million people by the year 2060. Although there is currently no cure for AD, disease-modifying treatments that target amyloid plaques have recently been approved for use. The advent of PET tracers that can reliably detect the presence of cortical amyloid plaques and tau pathologies has allowed researchers and clinicians to identify individuals who have pathologic markers of AD before the onset of cognitive decline. Although these tracers have been widely used in research settings for some time, they are now on the verge of being used to aid clinicians in the differential diagnosis of AD. As the use of these tracers increases, technologists will need to be educated on the best practices and potential problems they may encounter in their clinical populations. This article will review the available tracers for amyloid and tau PET scans and educate technologists about the most important practices and procedures that can be implemented to ensure patient safety and the capture of high-quality scans.

APOE genotype, hippocampal volume, and cognitive reserve predict improvement by cognitive training in older adults without dementia: a randomized controlled trial

Cognitive training (CT) programs aim to improve cognitive performance and impede its decline. Thus, defining the characteristics of individuals who can benefit from these interventions is essential. Our objectives were to assess if the cognitive reserve (CR), APOE genotype (e4 carriers/non-carriers) and/or hippocampal volume might predict the effectiveness of a CT program. Participants were older adults without dementia (n = 226), randomized into parallel experimental and control groups. The assessment consisted of a neuropsychological protocol and additional data regarding total intracranial, gray matter, left/right hippocampus volume; APOE genotype; and Cognitive Reserve (CR). The intervention involved multifactorial CT (30 sessions, 90 min each), with an evaluation pre- and post-training (at six months); the control group simply following the center's routine activities. The primary outcome measures were the change in cognitive performance and the predictors of change. The results show that APOE-e4 non-carriers (79.1%) with a larger left hippocampal volume achieved better gains in semantic verbal fluency (R2 = .19). Subjects with a larger CR and a greater gray matter volume better improved their processing speed (R2 = .18). Age was correlated with the improvement in executive functions, such that older age predicts less improvement (R2 = .07). Subjects with a larger left hippocampal volume achieved more significant gains in general cognitive performance (R2 = .087). In conclusion, besides the program itself, the effectiveness of CT depends on age, biological factors like genotype and brain volume, and CR. Thus, to achieve better results through a CT, it is essential to consider the different characteristics of the participants, including genetic factors.Trial registration: Trial retrospectively registered on January 29th, 2020-(ClinicalTrials.gov -NCT04245579).

LncRNA 51A: A promising diagnostic biomarker for assessing cognitive decline in occupationally exposed aluminum workers

OBJECTIVE

To assess the diagnostic utility of lncRNA 51 A in detecting cognitive decline among aluminum-exposed workers occupationally.

METHODS

921 male workers from an aluminum manufacturing facility underwent cognitive assessments, measurement of plasma aluminum levels and quantification of lncRNA 51 A levels. Receiver Operating Characteristic (ROC) curves were constructed to assess the diagnostic potential of lncRNA 51 A. Bayesian network model was utilized to predict the likelihood of cognitive decline among the study population.

RESULTS

Significant differences in lncRNA 51 A levels, plasma aluminum concentration and MMSE scores were observed between cognitive normal and decline groups. The lncRNA 51 A expression was negatively correlated with MMSE scores. The area under the curve (AUC) was 0.894, with 89.3 % sensitivity and 73.9 % specificity. The Bayesian network model indicated varying probabilities of cognitive decline based on lncRNA 51 A expression levels.

CONCLUSION

Plasma lncRNA 51 A shows potential as an excellent biomarker for cognitive decline diagnosis in aluminum-exposed workers.

Medial Temporal Lobe Atrophy in Older Adults With Subjective Cognitive Impairments Affects Gait Parameters in the Spatial Navigation Task

Both navigation abilities and gait can be affected by the atrophy in the medial temporal cortex. This study aimed to determine whether navigation abilities could differentiate seniors with and without medial temporal lobe atrophy who complained about their cognitive status. The participants, classified to either the medial temporal atrophy group (n = 23) or the control group (n = 22) underwent neuropsychological assessment and performed a spatial navigation task while their gait parameters were recorded. The study showed no significant differences between the two groups in memory, fluency, and semantic knowledge or typical measures of navigating abilities. However, gait parameters, particularly the propulsion index during certain phases of the navigation task, distinguished between seniors with and without medial temporal lobe lesions. These findings suggest that the gait parameters in the navigation task may be a valuable tool for identifying seniors with cognitive complaints and subtle medial temporal atrophy.

Effects of the active amyloid beta immunotherapy CAD106 on PET measurements of amyloid plaque deposition in cognitively unimpaired APOE ε4 homozygotes

INTRODUCTION

Alzheimer's Prevention Initiative Generation Study 1 evaluated amyloid beta (Aβ) active immunotherapy (vaccine) CAD106 and BACE-1 inhibitor umibecestat in cognitively unimpaired 60- to 75-year-old participants at genetic risk for Alzheimer's disease (AD). The study was reduced in size and terminated early. Results from the CAD106 cohort are presented.

METHODS

Sixty-five apolipoprotein E ε4 homozygotes with/without amyloid deposition received intramuscular CAD106 450 μg (n = 42) or placebo (n = 23) at baseline; Weeks 1, 7, 13; and quarterly; 51 of them had follow-up Aβ positron emission tomography (PET) scans at 18 to 24 months.

RESULTS

CAD106 induced measurable serum Aβ immunoglobulin G titers in 41/42 participants, slower rates of Aβ plaque accumulation (mean [standard deviation] annualized change from baseline in amyloid PET Centiloid: -0.91[5.65] for CAD106 versus 8.36 [6.68] for placebo; P < 0.001), and three amyloid-related imaging abnormality cases (one symptomatic).

DISCUSSION

Despite early termination, these findings support the potential value of conducting larger prevention trials of Aβ active immunotherapies in individuals at risk for AD.

HIGHLIGHTS

This was the first amyloid-lowering prevention trial in persons at genetic risk of late-onset Alzheimer's disease (AD). Active immunotherapy targeting amyloid (CAD106) was tested in this prevention trial. CAD106 significantly slowed down amyloid plaque deposition in apolipoprotein E homozygotes. CAD106 was generally safe and well tolerated, with only three amyloid-related imaging abnormality cases (one symptomatic). Such an approach deserves further evaluation in larger AD prevention trials.

Cognitive training and brain stimulation in patients with cognitive impairment: a randomized controlled trial

BACKGROUND

Repeated sessions of training and non-invasive brain stimulation have the potential to enhance cognition in patients with cognitive impairment. We hypothesized that combining cognitive training with anodal transcranial direct current stimulation (tDCS) will lead to performance improvement in the trained task and yield transfer to non-trained tasks.

METHODS

In our randomized, sham-controlled, double-blind study, 46 patients with cognitive impairment (60-80 years) were randomly assigned to one of two interventional groups. We administered a 9-session cognitive training (consisting of a letter updating and a Markov decision-making task) over 3 weeks with concurrent 1-mA anodal tDCS over the left dorsolateral prefrontal cortex (20 min in tDCS, 30 s in sham group). Primary outcome was trained task performance (letter updating task) immediately after training. Secondary outcomes included performance in tasks testing working memory (N-back task), decision-making (Wiener Matrices test) and verbal memory (verbal learning and memory test), and resting-state functional connectivity (FC). Tasks were administered at baseline, at post-assessment, and at 1- and 7-month follow-ups (FU). MRI was conducted at baseline and 7-month FU. Thirty-nine participants (85%) successfully completed the intervention. Data analyses are reported on the intention-to-treat (ITT) and the per-protocol (PP) sample.

RESULTS

For the primary outcome, no difference was observed in the ITT (β = 0.1, 95%-CI [- 1.2, 1.3, p = 0.93] or PP sample (β =  - 0.2, 95%-CI [- 1.6, 1.2], p = 0.77). However, secondary analyses in the N-back working memory task showed that, only in the PP sample, the tDCS outperformed the sham group (PP: % correct, β = 5.0, 95%-CI [- 0.1, 10.2], p = 0.06, d-prime β = 0.2, 95%-CI [0.0, 0.4], p = 0.02; ITT: % correct, β = 3.0, 95%-CI [- 3.9, 9.9], p = 0.39, d-prime β = 0.1, 95%-CI [- 0.1, 0.3], p = 0.5). Frontoparietal network FC was increased from baseline to 7-month FU in the tDCS compared to the sham group (pFDR < 0.05). Exploratory analyses showed a correlation between individual memory improvements and higher electric field magnitudes induced by tDCS (ρtDCS = 0.59, p = 0.02). Adverse events did not differ between groups, questionnaires indicated successful blinding (incidence rate ratio, 1.1, 95%-CI [0.5, 2.2]).

CONCLUSIONS

In sum, cognitive training with concurrent brain stimulation, compared to cognitive training with sham stimulation, did not lead to superior performance enhancements in patients with cognitive impairment. However, we observed transferred working memory benefits in patients who underwent the full 3-week intervention. MRI data pointed toward a potential intervention-induced modulation of neural network dynamics. A link between individual performance gains and electric fields suggested dosage-dependent effects of brain stimulation. Together, our findings do not support the immediate benefit of the combined intervention on the trained function, but provide exploratory evidence for transfer effects on working memory in patients with cognitive impairment. Future research needs to explore whether individualized protocols for both training and stimulation parameters might further enhance treatment gains.

TRIAL REGISTRATION

The study is registered on ClinicalTrials.gov (NCT04265378). Registered on 7 February 2020. Retrospectively registered.

Brain 11β-Hydroxysteroid Dehydrogenase Type 1 Occupancy by Xanamem™ Assessed by PET in Alzheimer's Disease and Cognitively Normal Individuals

BACKGROUND

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) regulates intracellular cortisol and its inhibition by the small molecule inhibitor, Xanamem™, may provide a disease-modifying strategy for Alzheimer's disease (AD). Animal models suggest a range of 30-60% enzyme inhibition may suffice to provide neuroprotection.

OBJECTIVE

To determine the regional brain occupancy of 11β-HSD1 by Xanamem™ in cognitively normal participants (CN) and mild cognitive impairment (MCI)/mild AD patients to investigate potential dosing ranges for future efficacy studies.

METHODS

Seventeen MCI/AD and 23 CN were included. Regional brain time-activity curves (TAC), standardized uptake values (SUV40-60) and volume of distribution (VT) from Logan plot with image derived input function from 11C-TARACT positron emission tomography (PET) were used to assess the degree of 11β-HSD1 occupancy by increasing doses of Xanamem™ (5 mg, 10 mg, 20 mg or 30 mg daily for 7 days).

RESULTS

All measures showed high 11β-HSD1 occupancy with Xanamem to similar degree in CN and MCI/AD. The dose-response relationship was relatively flat above 5 mg. Respective median (interquartile range [Q1-Q3]) 11β-HSD1 occupancy in the MCI/AD and CN groups after treatment with 10 mg Xanamem were 80% [79-81%] and 75% [71-76%] in the neocortex, 69% [64-70%] and 61% [52-63%] in the medial temporal lobe, 80% [79-80%] and 73% [68-73%] in the basal ganglia, and 71% [67-75%] and 66% [62-68%] in the cerebellum.

CONCLUSIONS

TAC, SUV40-60, and VT measures indicate Xanamem achieves high target occupancy levels with near saturation at 10 mg daily. These data support exploration of doses of≤10 mg daily in future clinical studies.

Morphometric and Nanomechanical Screening of Peripheral Blood Cells with Atomic Force Microscopy for Label-Free Assessment of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today's society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs-Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.

Multi-modal cross-attention network for Alzheimer's disease diagnosis with multi-modality data

Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.

Transcranial direct current stimulation in combination with cognitive training in individuals with mild cognitive impairment: a controlled 3-parallel-arm study

OBJECTIVE

Several studies showed that transcranial direct current stimulation (tDCS) enhances cognition in patients with mild cognitive impairment (MCI), however, whether tDCS leads to additional gains when combined with cognitive training remains unclear. This study aims to compare the effects of a concurrent tDCS-cognitive training intervention with either tDCS or cognitive training alone on a group of patients with MCI.

METHODS

The study was a 3-parallel-arm study. The intervention consisted of 20 daily sessions of 20 minutes each. Patients (n = 62) received anodal tDCS to the left dorsolateral prefrontal cortex, cognitive training on 5 cognitive domains (orientation, attention, memory, language, and executive functions), or both. To examine intervention gains, we examined global cognitive functioning, verbal short-term memory, visuospatial memory, and verbal fluency pre- and post-intervention.

RESULTS

All outcome measures improved after the intervention in the 3 groups. The improvement in global cognitive functioning and verbal fluency was significantly larger in patients who received the combined intervention. Instead, the intervention gain in verbal short-term memory and visuospatial memory was similar across the 3 groups.

DISCUSSION

tDCS, regardless of the practicalities, could be an efficacious treatment in combination with cognitive training given the increased effectiveness of the combined treatment.

CONCLUSIONS

Future studies will need to consider individual differences at baseline, including genetic factors and anatomical differences that impact the electric field generated by tDCS and should also consider the feasibility of at-home treatments consisting of the application of tDCS with cognitive training.

Meta-analysis of Controlled Oral Word Association Test (COWAT) FAS performance in amnestic mild cognitive impairment and cognitively unimpaired older adults

Findings from several studies have shown that amnestic mild cognitive impairment (aMCI) older adults have significantly lower performance on phonemic fluency tasks relative to cognitively unimpaired (CU) older adults. These findings suggest that nonmemory domains, such as executive function, are impacted in aMCI. As Alzheimer's disease (AD) research has shifted toward identifying and characterizing preclinical AD, there is a need to identify subtle but significant cognitive changes that are below the threshold for clinical impairment. The aim of this meta-analysis was to examine phonemic fluency differences between aMCI and CU older adults. Data from 18 studies were included in this analysis that found that aMCI individuals' phonemic fluency performance was approximately seven points lower than CU individuals (Δ = -7.31, 95% CI [-9.10, -5.52], z = -8.01, p < 0.001), which represents a medium effect size of (g = 0.61, 95% CI [0.46, 0.76], z = 7.90, p < 0.001). Normative conversion of the aMCI groups' raw scores showed that all were in the normal range of performance. The findings of this meta-analysis demonstrate that significant subclinical deficits in phonemic fluency can be present in aMCI. This should prompt greater use of phonemic fluency tasks in outcome measures for observational and intervention studies.

Meta-Analysis of Animal Fluency Performance in Amnestic Mild Cognitive Impairment and Cognitively Unimpaired Older Adults

Animal fluency is a commonly used neuropsychological measure that is used in the diagnosis of amnestic mild cognitive impairment (aMCI) and Alzheimer disease. Although most individuals with aMCI have clinically normal scores on this test, several studies have shown that aMCI individuals' performance is significantly lower than that of cognitively unimpaired (CU) individuals. The aim of this meta-analysis was to characterize the effect size of animal fluency performance differences between aMCI and CU individuals. Literature search with search terms used were: "animal fluency and mild cognitive impairment," "semantic fluency and mild cognitive impairment," "category fluency and mild cognitive impairment." Both the standardized mean difference and the raw mean difference were derived from random effects analyses. Demographically adjusted z-scores for animal fluency performance for the aMCI groups were obtained to determine normative performance. Nineteen studies were included in the analysis. The standardized mean difference for animal fluency performance between CU and aMCI was 0.89 (95% confidence interval: [0.73; 1.04], P <0.001), I2 =70.3% [52.7%; 81.4%], which reflects a large effect size with moderate heterogeneity. The raw mean difference was -4.08 [-4.75; -3.38], P <0.001. The mean animal fluency z-score for aMCI groups was in the Low Average range (z=-0.77). This study found a substantial difference in animal fluency performance between aMCI and CU individuals. The aMCI groups' normative performance did not fall into the impaired range, indicating that there are important subclinical differences in animal fluency performance that may inform the design of cognitive end points for Alzheimer's disease prevention trials.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

OBJECTIVE

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD.

METHODS

We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy.

RESULTS

We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set.

CONCLUSION

The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

A public resource of baseline data from the Alzheimer's Prevention Initiative Autosomal-Dominant Alzheimer's Disease Trial

INTRODUCTION

The Alzheimer's Prevention Initiative Autosomal-Dominant Alzheimer's Disease (API ADAD) Trial evaluated the anti-oligomeric amyloid beta (Aβ) antibody therapy crenezumab in cognitively unimpaired members of the Colombian presenilin 1 (PSEN1) E280A kindred. We report availability, methods employed to protect confidentiality and anonymity of participants, and process for requesting and accessing baseline data.

METHODS

We developed mechanisms to share baseline data from the API ADAD Trial in consultation with experts and other groups sharing data from Alzheimer's disease (AD) prevention trials, balancing the need to protect anonymity and trial integrity with making data broadly available to accelerate progress in the field. We pressure-tested deliberate and inadvertent potential threats under specific assumptions, employed a system to suppress or mask both direct and indirect identifying variables, limited and firewalled data managers, and put forth specific principles requisite to receive data.

RESULTS

Baseline demographic, PSEN1 E280A and apolipoprotein E genotypes, florbetapir and fluorodeoxyglucose positron emission tomography, magnetic resonance imaging, clinical, and cognitive data can now be requested by interested researchers.

DISCUSSION

Baseline data are publicly available; treatment data and biological samples, including baseline and treatment-related blood-based biomarker data will become available in accordance with our original trial agreement and subsequently developed Collaboration for Alzheimer's Prevention principles. Sharing of these data will allow exploration of important questions including the differential effects of initiating an investigational AD prevention therapy both before as well as after measurable Aβ plaque deposition.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

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.

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

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.

Alzheimer's disease amyloid-β pathology in the lens of the eye

Neuropathological hallmarks of Alzheimer's disease (AD) include pathogenic accumulation of amyloid-β (Aβ) peptides and age-dependent formation of amyloid plaques in the brain. AD-associated Aβ neuropathology begins decades before onset of cognitive symptoms and slowly progresses over the course of the disease. We previously reported discovery of Aβ deposition, β-amyloidopathy, and co-localizing supranuclear cataracts (SNC) in lenses from people with AD, but not other neurodegenerative disorders or normal aging. We confirmed AD-associated Aβ molecular pathology in the lens by immunohistopathology, amyloid histochemistry, immunoblot analysis, epitope mapping, immunogold electron microscopy, quantitative immunoassays, and tryptic digest mass spectrometry peptide sequencing. Ultrastructural analysis revealed that AD-associated Aβ deposits in AD lenses localize as electron-dense microaggregates in the cytoplasm of supranuclear (deep cortex) fiber cells. These Aβ microaggregates also contain αB-crystallin and scatter light, thus linking Aβ pathology and SNC phenotype expression in the lenses of people with AD. Subsequent research identified Aβ lens pathology as the molecular origin of the distinctive cataracts associated with Down syndrome (DS, trisomy 21), a chromosomal disorder invariantly associated with early-onset Aβ accumulation and Aβ amyloidopathy in the brain. Investigation of 1249 participants in the Framingham Eye Study found that AD-associated quantitative traits in brain and lens are co-heritable. Moreover, AD-associated lens traits preceded MRI brain traits and cognitive deficits by a decade or more and predicted future AD. A genome-wide association study of bivariate outcomes in the same subjects identified a new AD risk factor locus in the CTNND2 gene encoding δ-catenin, a protein that modulates Aβ production in brain and lens. Here we report identification of AD-related human Aβ (hAβ) lens pathology and age-dependent SNC phenotype expression in the Tg2576 transgenic mouse model of AD. Tg2576 mice express Swedish mutant human amyloid precursor protein (APP-Swe), accumulate hAβ peptides and amyloid pathology in the brain, and exhibit cognitive deficits that slowly progress with increasing age. We found that Tg2576 trangenic (Tg⁺) mice, but not non-transgenic (Tg^-) control mice, also express human APP, accumulate hAβ peptides, and develop hAβ molecular and ultrastructural pathologies in the lens. Tg2576 Tg⁺ mice exhibit age-dependent Aβ supranuclear lens opacification that recapitulates lens pathology and SNC phenotype expression in human AD. In addition, we detected hAβ in conditioned medium from lens explant cultures prepared from Tg⁺ mice, but not Tg^- control mice, a finding consistent with constitutive hAβ generation in the lens. In vitro studies showed that hAβ promoted mouse lens protein aggregation detected by quasi-elastic light scattering (QLS) spectroscopy. These results support mechanistic (genotype-phenotype) linkage between Aβ pathology and AD-related phenotypes in lens and brain. Collectively, our findings identify Aβ pathology as the shared molecular etiology of two age-dependent AD-related cataracts associated with two human diseases (AD, DS) and homologous murine cataracts in the Tg2576 transgenic mouse model of AD. These results represent the first evidence of AD-related Aβ pathology outside the brain and point to lens Aβ as an optically-accessible AD biomarker for early detection and longitudinal monitoring of this devastating neurodegenerative disease.

Brain structural alterations and clinical features of cognitive frailty in Japanese community-dwelling older adults: the Arao study (JPSC-AD)

Cognitive frailty (CF) is a clinical condition defined by the presence of both mild cognitive impairment (MCI) and physical frailty (PF). Elderly with CF are at greater risk of dementia than those with MCI or PF alone, but there are few known clinical or neuroimaging features to reliably distinguish CF from PF or MCI. We therefore conducted a population-based cross-sectional study of community elderly combining physical, cognitive, neuropsychiatric, and multisequence magnetic resonance imaging (MRI) evaluations. The MRI evaluation parameters included white matter (WM) lesion volumes, perivascular and deep subcortical WM lesion grades, lacunar infarct prevalence, microbleed number, and regional medial temporal lobe (MTL) volumes. Participants were divided into 4 groups according to the presence or absence of MCI and PF-(1) no MCI, PF (n = 27); (2) no PF, MCI (n = 119); (3) CF (MCI + PF) (n = 21), (4) normal controls (n = 716). Unique features of CF included shorter one-leg standing time; severe depressive symptoms; and MRI signs of significantly more WM lesions, lacunar infarcts, small-vessel disease lesions, microbleeds, and reduced MTL volumes. These unique deficits suggest that interventions for CF prevention and treatment should focus on motor skills, depressive symptoms, and vascular disease risk factor control.

Limitations of clinical trial sample size estimate by subtraction of two measurements

In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.

MicroRNA-4722-5p and microRNA-615-3p serve as potential biomarkers for Alzheimer's disease

The aim of the present study was to investigate the expression levels of microRNA(miR)-4722-5p and miR-615-3p in Alzheimer's disease (AD) and their diagnostic value. Blood samples were collected from 33 patients with AD and 33 healthy controls, and an β-amyloid (Aβ)25-35-induced PC12 cell model was also established. The relative mRNA expression levels of miR-4722-5p and miR-615-3p were detected using reverse transcription-quantitative PCR. The correlations between the mRNA expression levels of the two miRNAs and the mini-mental state examination (MMSE) scores were analyzed, and the receiver operating characteristic curve was used to assess the diagnostic value of miR-4722-5p and miR-615-3p in AD. Functional enrichment analysis of the miRNA target genes was performed using The Database for Annotation, Visualization and Integrated Discovery database and the R language analysis package. The mRNA expression levels of miR-4722-5p and miR-615-3p were increased in patients with AD and the Aβ25-35-induced PC12 cell model. The mRNA expression levels of miR-4722-5p and miR-615-3p were negatively correlated with MMSE scores, and the combination of the two miRNAs for AD had an improved diagnostic value than that of each miRNA alone. The results of Gene Ontology (GO) enrichment analysis showed that the target genes of miR-4722-5p were found in the cytoplasm and cytosol, and were mainly involved in protein folding and cell division. The molecular functions included protein binding and GTPase activator activity. The results of Kyoto Encyclopedia of Genes and Genomes analysis showed that miR-4722-5p was associated with the regulation of dopaminergic synapses and mTOR signaling pathways. GO enrichment analysis also revealed that the target genes of miR-615-3p were located in the nucleus and cytoplasm, were involved in the regulation of transcription and protein phosphorylation, and were associated with protein binding, metal ion binding and transcription factor activity. The target genes of miR-615-3p played important roles in the regulation of the Ras and FoxO signaling pathways. In conclusion, miR-4722-5p and miR-615-3p may be potential biomarkers in the early diagnosis of AD.

Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

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.

Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative

Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.

Glucagon-like peptide-1 attenuated carboxymethyl lysine induced neuronal apoptosis via peroxisome proliferation activated receptor-γ

Backgrounds and aims: The role of peroxisome proliferator activated receptor-γ (PPAR-γ) in neuronal apoptosis remains unclear. We aim to investigate the role of PPAR-γ in glucagon-like peptide-1 (GLP-1) alleviated neuronal apoptosis induced by carboxymethyl-lysine (CML).

Materials and Methods: In vitro, PC12 cells were treated by CML/GLP-1. Moreover. the function of PPAR-γ was blocked by GW9662. In vivo, streptozotocin (STZ) was used to induce diabetic rats with neuronal apoptosis. The cognitive function of rats was observed by Morris water maze. Apoptosis was detected by TUNEL assay. Bcl2, Bax, PPAR-γ and receptor of GLP-1 (GLP-1R) were measured by western blotting or immunofluorescence.

Results: In vitro experiment, CML triggered apoptosis, down-regulated GLP-1R and PPAR-γ. Moreover, GLP-1 not only alleviated the apoptosis, but also increased levels of PPAR-γ. GW9662 abolished the neuroprotective effect of GLP-1 on PC12 cells from apoptosis. Furthermore, GLP-1R promoter sequences were detected in the PPAR-γ antibody pulled mixture. GPL-1 levels decreased, while CML levels increased in diabetic rats, compared with control rats. Additionally, we observed elevated bax, decreased bcl2, GLP-1R and PPAR-γ in diabetic rats.

Conclusions: GLP-1 could attenuate neuronal apoptosis induced by CML. Additionally, PPAR-γ involves in this process.

Nanomedicine against Alzheimer's and Parkinson's Disease

Alzheimer's and Parkinson's are the two most rampant neurodegenerative disorders worldwide. Existing treatments have a limited effect on the pathophysiology but are unable to fully arrest the progression of the disease. This is due to the inability of these therapeutic molecules to efficiently cross the blood-brain barrier. We discuss how nanotechnology has enabled researchers to develop novel and efficient nano-therapeutics against these diseases. The development of nanotized drug delivery systems has permitted an efficient, site-targeted, and controlled release of drugs in the brain, thereby presenting a revolutionary therapeutic approach. Nanoparticles are also being thoroughly studied and exploited for their role in the efficient and precise diagnosis of neurodegenerative conditions. We summarize the role of different nano-carriers and RNAi-conjugated nanoparticle-based therapeutics for their efficacy in pre-clinical studies. We also discuss the challenges underlying the use of nanomedicine with a focus on their route of administration, concentration, metabolism, and any toxic effects for successful therapeutics in these diseases.

A Sex Perspective in Neurodegenerative Diseases: microRNAs as Possible Peripheral Biomarkers

Sex is a significant variable in the prevalence and incidence of neurological disorders. Sex differences exist in neurodegenerative disorders (NDs), where sex dimorphisms play important roles in the development and progression of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the last few years, some sex specific biomarkers for the identification of NDs have been described and recent studies have suggested that microRNA (miRNA) could be included among these, as influenced by the hormonal and genetic background. Failing to consider the possible differences between males and females in miRNA evaluation could introduce a sex bias in studies by not considering some of these sex-related biomarkers. In this review, we recapitulate what is known about the sex-specific differences in peripheral miRNA levels in neurodegenerative diseases. Several studies have reported sex-linked disparities, and from the literature analysis miR-206 particularly has been shown to have a sex-specific involvement. Hopefully, in the near future, patient stratification will provide important additional clues in diagnosis, prognosis, and tailoring of the best therapeutic approaches for each patient. Sex-specific biomarkers, such as miRNAs, could represent a useful tool for characterizing subgroups of patients.

Early prevention of cognitive impairment in the community population: The Beijing Aging Brain Rejuvenation Initiative

Facing considerable challenges associated with aging and dementia, China urgently needs an evidence-based health-care system for prevention and management of dementia. The Beijing Aging Brain Rejuvenation Initiative (BABRI) is a community-based cohort study initiated in 2008 that focuses on asymptomatic stages of dementia, aims to develop community-based prevention strategies for cognitive impairment, and provides a platform for scientific research and clinical trials. Thus far, BABRI has recruited 10,255 participants (aged 50 and over, 60.3% female), 2021 of whom have been followed up at least once at a 2- or 3-year interval. This article presents aims and study design of BABRI; summarizes preliminary behavioral and neuroimaging findings on mild cognitive impairment (MCI) and results of clinical trials on MCI; and discusses issues concerning early prevention in community, MCI diagnosis methods, and applications of database of aging and dementia. BABRI is proposed to build a systematic framework on brain health in old age.

Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition

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.

Physical Therapy for Gait, Balance, and Cognition in Individuals with Cognitive Impairment: A Retrospective Analysis

Objectives

The purpose of this study was to determine if a pragmatic physical therapy (PT) program was associated with improved cognition, gait, and balance in individuals with cognitive impairment. This study investigated these associations for individuals with Alzheimer disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), and mild cognitive impairment (MCI) in order to better characterize outcomes to PT for each diagnostic group.

Methods

Data before and after one month of physical therapy were extracted from patient records (67 with AD, 34 with VaD, 35 with DLB, and 37 with MCI). The mean number of PT sessions over a month was 3.4 (±1.8). Outcomes covered the domains of gait, balance, and cognition with multiple outcomes used to measure different constructs within the balance and gait domains.

Results

All groups showed improvements in balance and at least one gait outcome measure. Those with MCI improved in every measure of gait and balance performance. Lastly, cognition as measured by Montreal Cognitive Assessment improved in individuals in the AD, VaD, and MCI groups.

Conclusion

While this retrospective analysis is not appropriate for causal inference, results of one month of physical therapy were associated with decreases in gait, balance, and cognitive impairment in individuals with AD, VaD, DLB<, and MCI. Clinical Implications. While physical therapy is not typically a primary treatment strategy for individuals with cognitive impairment, the results of this study are consistent with the literature that demonstrates improvement from physical therapy for other neurodegenerative diseases. Further clinical and research exploration for physical therapy as a primary treatment strategy in these populations is warranted.

A typology of clinical conditions

In the philosophy of medicine, great attention has been paid to defining disease, yet less attention has been paid to the classification of clinical conditions. These include conditions that look like diseases but are not; conditions that are diseases but that (currently) have no diagnostic criteria; and other types, including those relating to risk for disease. I present a typology of clinical conditions by examining factors important for characterizing clinical conditions. By attending to the types of clinical conditions possible on the basis of these key factors (symptomaticity, dysfunction, and the meeting of diagnostic criteria), I draw attention to how diseases and other clinical conditions as currently classified can be better categorized, highlighting the issues pertaining to certain typology categories. Through detailed analysis of a wide variety of clinical examples, including Alzheimer disease as a test case, I show how nosology, research, and decisions about diagnostic criteria should include normative as well as naturalistically describable factors.

Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study

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.

Abnormal Serum Bilirubin/Albumin Concentrations in Dementia Patients With Aβ Deposition and the Benefit of Intravenous Albumin Infusion for Alzheimer's Disease Treatment

Background

Our previous study in animal models revealed that bilirubin could induce Aβ formation and deposition. Bilirubin may be important in neurodegenerative dementia with Aβ deposition. Hence, lowering the concentration of the free bilirubin capable of crossing the blood brain-barrier may benefit the treatment of Alzheimer's disease (AD).

Objectives

The objectives of this study were to examine the change in the serum bilirubin and albumin concentrations of dementia patients with Aβ deposition, and to determine the effects of intravenous administration of albumin in the treatment of AD.

Methods

Bilirubin and albumin concentrations in dementia patients with Aβ deposition were examined. Cell viability and apoptosis were determined in dopaminergic neuron-like cells MN9D treated with bilirubin in the presence of diverse concentrations of serum. Human albumin at a dose of 10 g every 2 weeks for 24 weeks was administered intravenously to AD patients to examine the effect of albumin on AD symptoms.

Results

Significantly higher indirect bilirubin (IBIL) concentrations, lower albumin concentrations, and higher ratio of IBIL to albumin (IBIL/ALB) were observed in dementia patients with Aβ deposition, including AD, dementia with Lewy bodies, and general paresis of insane. In vitro assays showed that bilirubin-induced injury in cultured dopaminergic neuron-like cells negatively depends on the concentration of serum in the culture medium. General linear model with repeated measures analysis indicated a main effect of group on the change in albumin concentrations and Alzheimer's Disease Cooperative Study Activities of Daily Living Inventory scale (ADCS-ADL) scores, and the main effect of time and group, and group-by-time interaction on the change of Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) scores. Analysis of the combined data of the entire 28 weeks of assessment period using the area under curve convincingly showed significantly improvements in the change of albumin concentrations, ADCS-ADL scores, and CDR-SB scores.

Conclusion

IBIL and the IBIL/ALB ratio are significantly higher in dementia patients with Aβ deposition, and intravenous administration of albumin is beneficial to AD treatment.

Trial Registration

The intervention study was registered at http://www.chictr.org.cn (ChiCTR-IOR-17011539). Date of registration: June 1, 2017.

Unsupervised assessment of cognition in the Healthy Brain Project: Implications for web-based registries of individuals at risk for Alzheimer's disease

INTRODUCTION

Web-based platforms are used increasingly to assess cognitive function in unsupervised settings. The utility of cognitive data arising from unsupervised assessments remains unclear. We examined the acceptability, usability, and validity of unsupervised cognitive testing in middle-aged adults enrolled in the Healthy Brain Project.

METHODS

A total of 1594 participants completed unsupervised assessments of the Cogstate Brief Battery. Acceptability was defined by the amount of missing data, and usability by examining error of test performance and the time taken to read task instructions and complete tests (learnability).

RESULTS

Overall, we observed high acceptability (98% complete data) and high usability (95% met criteria for low error rates and high learnability). Test validity was confirmed by observation of expected inverse relationships between performance and increasing test difficulty and age.

CONCLUSION

Consideration of test design paired with acceptability and usability criteria can provide valid indices of cognition in the unsupervised settings used to develop registries of individuals at risk for Alzheimer's disease.

An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine

Alzheimer's disease (AD) is the most common cause of dementia and a progressive neurodegenerative condition, characterized by a decline in cognitive function. Symptoms usually appear gradually and worsen over time, becoming severe enough to interfere with individual daily tasks. Thus, the accurate diagnosis of both AD and the prodromal stage (i.e., mild cognitive impairment (MCI)) is crucial for timely treatment. As AD is inherently dynamic, the relationship between AD indicators is unclear and varies over time. To address this issue, we first aimed at investigating differences in atrophic patterns between individuals with AD and MCI and healthy controls (HCs). Then we utilized multiple biomarkers, along with filter- and wrapper-based feature selection and an extreme learning machine- (ELM-) based approach, with 10-fold cross-validation for classification. Increasing efforts are focusing on the use of multiple biomarkers, which can be useful for the diagnosis of AD and MCI. However, optimum combinations have yet to be identified and most multimodal analyses use only volumetric measures obtained from magnetic resonance imaging (MRI). Anatomical structural MRI (sMRI) measures have also so far mostly been used separately. The full possibilities of using anatomical MRI for AD detection have thus yet to be explored. In this study, three measures (cortical thickness, surface area, and gray matter volume), obtained from sMRI through preprocessing for brain atrophy measurements; cerebrospinal fluid (CSF), for quantification of specific proteins; cognitive score, as a measure of cognitive performance; and APOE ε4 allele status were utilized. Our results show that a combination of specific biomarkers performs well, with accuracies of 97.31% for classifying AD vs. HC, 91.72% for MCI vs. HC, 87.91% for MCI vs. AD, and 83.38% for MCIs vs. MCIc, respectively, when evaluated using the proposed algorithm. Meanwhile, the areas under the curve (AUC) from the receiver operating characteristic (ROC) curves combining multiple biomarkers provided better classification performance. The proposed features combination and selection algorithm effectively classified AD and MCI, and MCIs vs. MCIc, the most challenging classification task, and therefore could increase the accuracy of AD classification in clinical practice. Furthermore, we compared the performance of the proposed method with SVM classifiers, using a cross-validation method with Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images

BACKGROUND

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

OBJECTIVE

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

METHODS

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog).

RESULTS

We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods.

CONCLUSION

Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Whole-Blood Levels of MicroRNA-9 Are Decreased in Patients With Late-Onset Alzheimer Disease

Recent evidence suggests changes in circulating microRNA levels may be promising biomarkers for the clinical diagnosis of Alzheimer disease (AD). We hypothesized that whole-blood microRNAs may be useful to identify individuals with established AD. For this purpose, a sample of community-dwelling women (≥55 years old) carrying the ApoE ∊4 allele were clinically evaluated using the American Psychiatric Association/Diagnostic and Statistical Manual of Mental Disorders, Fourth edition and the Alzheimer Disease Assessment Scale-Cognitive Subscale criteria to diagnose probable AD, and the Clinical Dementia Rating scale to stage the dementia. A set of 25 mature microRNAs was rationally selected for evaluation based on experimental evidence of interaction with genes linked to the late-onset AD neuropathology. Whole-blood concentrations were determined by quantitative real-time polymerase chain reaction. Compared to patients without dementia, a median 3-fold decrease in miR-9 levels was found among patients with AD (P = .001). Our findings support blood-borne miR-9 as a candidate biomarker for probable AD, embodied by evidence from the literature of its implication in amyloidogenesis.

Braak Stage, Cerebral Amyloid Angiopathy, and Cognitive Decline in Early Alzheimer's Disease

The aim of this study was to determine the interaction between cerebral amyloid angiopathy (CAA) and Braak staging on cognition in the elderly. The study used a total of 141 subjects consisting of 72 non-cognitively impaired (NCI), 33 mild cognitive impairment (MCI), 36 Alzheimer's disease (AD) cases displaying Braak stages 0-II and III from the Rush Religious Order Study cohort. The association between Braak stage and CAA status and cognition was evaluated using a series of regression models that adjusted for age at death, sex, education, APOEɛ4 status, and Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropathological diagnosis. Individuals with CAA were more likely to be classified as Braak stage III relative to those without CAA [OR = 2.33, 95% CI (1.06, 5.14), p = 0.04]. A significant interaction was found between Braak stage and CAA status on a global cognitive score (β = -0.58, SE = 0.25, p = 0.02). Episodic memory also showed a significant association between Braak stage and CAA (β= -0.75, SE = 0.35, p = 0.03). These data suggest that there is a significant interaction between tau pathology and cerebrovascular lesions on cognition within the AD clinical spectrum.

A novel kit for early diagnosis of Alzheimer's disease using a fluorescent nanoparticle imaging

Alzheimer's disease (AD) is a progressive neurodegenerative disease and chronic illness with long preclinical phases and a long clinical duration. Until recently, a lack of potential therapeutic agents against AD was the primary focus of research, which resulted in less effort directed towards developing useful diagnostic approaches. In this study, we developed a WO2002/088706 kit that is composed of fluorescent nanoparticles for the early detection of AD. We provided a fluorescent nanoparticle for detecting markers and a kit for the early diagnosis of AD. The kit consists of a probe molecule comprising an oligonucleotide capable of detecting one or more AD-specific microRNAs (miRNAs) and biomarkers related to AD. Through screening, we selected miR-106b, miR-146b, miR-181a, miR-200a, miR-34a, miR-124b, miR-153, miR-155, Aβ_1-42 monomer (mAβ), Aβ_1-42 oligomer (oAβ), UCHL1, NLRP3, Tau, STAT3, SORL1, Clusterin, APOE3, APOE4, Nogo-A, IL-13, and Visfatin to serve as AD- and inflammation-related markers. For detection of kit-binding properties, we checked the expression levels of amyloid beta (Aβ), tau protein, and inflammatory mediators in APP/PS/ApoE knockdown (KD) mice and a control group using co-localisation analysis conducted with a confocal microscope. Using a similar approach, we checked the expression levels of miRNAs in HT22 cells. Finally, we used the plasma from AD patients to confirm that our fluorescent nanoparticles and the WO2002/088706 kit will provide a possible early diagnosis to serve as an AD detector that can be further improved for future studies on targeting AD.

Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique

Supplemental Digital Content is available in the text.

Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which diffusion tensor image segmentation technique (DSEG; which characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure.

Neuroimaging of Alzheimer's disease: focus on amyloid and tau PET

Although the diagnosis of dementia is still largely a clinical one, based on history and disease course, neuroimaging has dramatically increased our ability to accurately diagnose it. Neuroimaging modalities now play a wider role in dementia beyond their traditional role of excluding neurosurgical lesions and are recommended in most clinical guidelines for dementia. In addition, new neuroimaging methods facilitate the diagnosis of most neurodegenerative conditions after symptom onset and show diagnostic promise even in the very early or presymptomatic phases of some diseases. In the case of Alzheimer's disease (AD), extracellular amyloid-β (Aβ) aggregates and intracellular tau neurofibrillary tangles are the two neuropathological hallmarks of the disease. Recent molecular imaging techniques using amyloid and tau PET ligands have led to preclinical diagnosis and improved differential diagnosis as well as narrowed subject selection and treatment monitoring in clinical trials aimed at delaying or preventing the symptomatic phase of AD. This review discusses the recent progress in amyloid and tau PET imaging and the key findings achieved by the use of this molecular imaging modality related to the respective roles of Aβ and tau in AD, as well as its specific limitations.

Long Non-coding RNA BACE1-AS May Serve as an Alzheimer's Disease Blood-Based Biomarker

Circulating long noncoding RNAs (lncRNAs) might serve as biomarkers for different pathological conditions. BACE1-AS lncRNA upregulates in the brain of people with Alzheimer's disease (AD) and might be detected in the bloodstream. To reveal if lncRNA BACE1-AS may serve as a blood-based biomarker for AD, we compared its levels in plasma and plasma-derived exosomes between AD (n = 45) and healthy people (n = 36). Exosomes were purified from plasma by Invitrogen™ Total Exosome Isolation Kit and characterized by scanning electron microscopy (SEM) and dynamic light scattering (DLS). Total RNA was extracted from whole plasma, and plasma-derived exosomes using TRIzol® LS or TRIzol® Reagents respectively were then reverse transcribed to the cDNA using PrimeScript II cDNA synthesis kit. The BACE1-AS levels were quantified by real-time PCR, and their biomarker potencies were evaluated using ROC curve analysis. Results obtained verified the presence of BACE1-AS in the plasma samples of both AD and healthy controls. We did not observe any significant differences between the levels of BACE1-AS in the plasma or plasma-derived exosomes of AD and control people. However, there were significant differences between AD subgroups and control in the whole plasma samples. The BACE1-AS level was low in pre-AD subgroup but it was high in full-AD people compared to the healthy controls. Moreover, ROC curve analysis revealed that lncRNA BACE1-AS may discriminate pre-AD and healthy control (75% sensitivity and 100% specificity), full-AD and healthy control (68% sensitivity and 100% specificity), and pre-AD and full-AD subgroups (78% sensitivity and 100% specificity), highlighting its potential as a biomarker for AD development. In conclusion, plasma BACE1-AS level may serve as a potent blood-based biomarker for Alzheimer's disease.

The Alzheimer's Prevention Initiative Generation Program: Study design of two randomized controlled trials for individuals at risk for clinical onset of Alzheimer's disease

Introduction

Alzheimer's disease (AD) pathology, including the accumulation of amyloid beta (Aβ) species and tau pathology, begins decades before the onset of cognitive impairment. This long preclinical period provides an opportunity for clinical trials designed to prevent or delay the onset of cognitive impairment due to AD. Under the umbrella of the Alzheimer's Prevention Initiative Generation Program, therapies targeting Aβ, including CNP520 (umibecestat), a β-site-amyloid precursor protein cleaving enzyme-1 (BACE-1) inhibitor, and CAD106, an active Aβ immunotherapy, are in clinical development in preclinical AD.

Methods

The Alzheimer's Prevention Initiative Generation Program comprises two pivotal (phase 2/3) studies that assess the efficacy and safety of umibecestat and CAD106 in cognitively unimpaired individuals with high risk for developing symptoms of AD based on their age (60-75 years), APOE4 genotype, and, for heterozygotes (APOE ε2/ε4 or ε3/ε4), elevated brain amyloid. Approximately, 3500 individuals will be enrolled in either Generation Study 1 (randomized to cohort 1 [CAD106 injection or placebo, 5:3] or cohort 2 [oral umibecestat 50 mg or placebo, 3:2]) or Generation Study 2 (randomized to oral umibecestat 50 mg and 15 mg, or placebo [2:1:2]). Participants receive treatment for at least 60 months and up to a maximum of 96 months. Primary outcomes include time to event, with event defined as diagnosis of mild cognitive impairment due to AD and/or dementia due to AD, and the Alzheimer's Prevention Initiative preclinical composite cognitive test battery. Secondary endpoints include the Clinical Dementia Rating Sum of Boxes, Repeatable Battery for the Assessment of Neuropsychological Status total score, Everyday Cognition Scale, biomarkers, and brain imaging.

Discussion

The Generation Program is designed to assess the efficacy, safety, and biomarker effects of the two treatments in individuals at high risk for AD. It may also provide a plausible test of the amyloid hypothesis and further accelerate the evaluation of AD prevention therapies.

Effects of manual acupuncture combined with donepezil in a mouse model of Alzheimer's disease

OBJECTIVES

To explore whether combined therapy with donepezil and acupuncture is better than treatment with donepezil or acupuncture individually in a rat model of Alzheimer's disease.

METHODS

In this study, we randomly divided 40 7.5-month-old senescence-accelerated mouse prone 8 (SAMP8) male mice into four groups: SAMP8, SAMP8+D, SAMP8+MA and SAMP8+D+MA. An additional 10 7.5-month-old SAMR1 male mice were included as a healthy control group (SAMR1). Mice in the SAMP8+D group were given donepezil at a dose of 0.65 µg/g/day; mice in the SAMP8+MA group underwent manual acupuncture at GV20, GV26 and Yintang for 20 min per day; mice in the SAMP8+D+MA received both donepezil and manual acupuncture; and mice in the SAMR1 and SAMP8 groups underwent restraint only to control for the effects of handling. After the 15-day treatment, the Morris water maze test, micro-PET(positron-emission tomography), H&E (haematoxylin and eosin) staining, and immunohistochemistry were used to study the differences between donepezil (SAMP8+D), acupuncture (SAMP8+MA), and donepezil combined with acupuncture (SAMP8+D+MA) therapy for the treatment of Alzheimer's disease.

RESULTS

We found that, compared with the untreated SAMP8 group, donepezil, manual acupuncture, and combined therapy with donepezil and manual acupuncture all improved spatial learning and memory ability, the level of glucose metabolism in the brain, and the content of Aβ amyloid in the cortex. Moreover, combined therapy outperformed treatment with donepezil or acupuncture individually in the SAMP8 mice.

CONCLUSION

This study shows that the combination of manual acupuncture and donepezil in an Alzheimer's disease animal model is superior to acupuncture and donezepil alone. However, randomised controlled trials should be undertaken to clarify the clinical efficacy of combination therapy.

A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

The impact of amyloid-beta and tau on prospective cognitive decline in older individuals

OBJECTIVES

Amyloid-beta (Aβ) and tau pathologies are commonly observed among clinically normal older individuals at postmortem and can now be detected with in vivo neuroimaging. The association and interaction of these proteinopathies with prospective cognitive decline in normal aging and preclinical Alzheimer's disease (AD) remains to be fully elucidated.

METHODS

One hundred thirty-seven older individuals (age = 76.3 ± 6.22 years) participating in the Harvard Aging Brain Study underwent Aβ (11 C-Pittsburgh compound B) and tau (18 F-flortaucipir) positron emission tomography (PET) with prospective neuropsychological assessments following PET imaging (mean number of cognitive visits = 2.8 ± 1.1). Tau and Aβ PET measures were assessed in regions of interest (ROIs) as well as vertex-wise map analyses. Cognitive change was evaluated with Memory and Executive Function composites.

RESULTS

Higher levels of Aβ and tau were both associated with greater memory decline, but not with change in executive function. Higher cortical Aβ was associated with higher tau levels in all ROIs, independent of age, and very elevated levels of tau were observed primarily in clinically normal with elevated Aβ. A significant interaction between tau and Aβ was observed in both ROI and map-level analyses, such that rapid prospective memory decline was observed in participants who had high levels of both pathologies.

INTERPRETATION

Our results are consistent with the supposition that both Aβ and tau are necessary for memory decline in the preclinical stages of AD. These findings may be relevant for disambiguating aging and early cognitive manifestations of AD, and to inform secondary prevention trials in preclinical AD. Ann Neurol 2019;00:1-3 ANN NEUROL 2019;85:181-193.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

BACKGROUND

Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment.

METHODS

SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD.

RESULTS

Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758).

CONCLUSION

The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

Exploratory Study of Associations Between DNA Repair and Oxidative Stress Gene Polymorphisms and Cognitive Problems Reported by Postmenopausal Women With and Without Breast Cancer

PURPOSE

Women with breast cancer report varying frequencies of cognitive problems during adjuvant systemic therapy. This variability suggests latent subgroups. Therefore, we identified latent subgroups of self-reported cognitive problems among postmenopausal women with and without breast cancer. We explored associations between membership in these subgroups and (a) demographic, clinical, and symptom characteristics and (b) variations in candidate gene polymorphisms.

METHODS

We evaluated frequency of cognitive problems using the Patient Assessment of Own Functioning Inventory. Growth mixture modeling identified latent subgroups over 18 months of adjuvant systemic therapy and at matched time points for women without cancer ( N = 331). We evaluated for differences among subgroups in demographic, clinical, and symptom characteristics and in 41 single nucleotide polymorphisms in 10 candidate genes involved in DNA repair and oxidative stress pathways ( n = 199). We modeled associations between genotypes and subgroup membership using multinomial logistic regression.

RESULTS

We identified three latent subgroups: more frequent, persistent, and almost never. Receipt of chemotherapy plus anastrozole, depressive symptoms, and baseline neuropathic symptoms increased the odds of belonging to the more frequent subgroup. Anxiety and depressive symptoms increased the odds of belonging to the persistent subgroup. With covariates controlled for, carrying the ERCC5 rs873601 G minor allele increased the odds of reporting more frequent cognitive problems.

CONCLUSIONS

Chemotherapy plus anastrozole, depressive symptoms, and presence of neuropathic symptoms may predict more frequent cognitive problems during systemic therapy that later resolve. Mood dysregulation before therapy may predict persistent cognitive problems during therapy. ERCC5 genotype may influence frequency of cognitive problems after controlling for these risk factors.

Identifying preclinical vascular dementia in symptomatic small vessel disease using MRI

Sporadic cerebral small vessel disease is an important cause of vascular dementia, a syndrome of cognitive impairment together with vascular brain damage. At post-mortem pure vascular dementia is rare, with evidence of co-existing Alzheimer's disease pathology in 95% of cases. This work used MRI to characterize structural abnormalities during the preclinical phase of vascular dementia in symptomatic small vessel disease. 121 subjects were recruited into the St George's Cognition and Neuroimaging in Stroke study and followed up longitudinally for five years. Over this period 22 individuals converted to dementia. Using voxel-based morphometry, we found structural abnormalities present at baseline in those with preclinical dementia, with reduced grey matter density in the left striatum and hippocampus, and more white matter hyperintensities in the frontal white-matter. The lacunar data revealed that some of these abnormalities may be due to lesions within the striatum and centrum semiovale. Using support vector machines, future dementia could be best predicted using hippocampal and striatal Jacobian determinant data, achieving a balanced classification accuracy of 73%. Using cluster ward linkage we identified four anatomical subtypes. Successful predictions were restricted to groups with lower levels of vascular damage. The subgroup that could not be predicted were younger, further from conversion, had the highest levels of vascular damage, with milder cognitive impairment at baseline but more rapid deterioration in processing speed and executive function, consistent with a primary vascular dementia. In contrast, the remaining groups had decreasing levels of vascular damage and increasing memory impairment consistent with progressively more Alzheimer's-like pathology. Voxel-wise rates of hippocampal atrophy supported these distinctions, with the vascular group closely resembling the non-dementing cohort, whereas the Alzheimer's like group demonstrated global hippocampal atrophy. This work reveals distinct anatomical endophenotypes in preclinical vascular dementia, forming a spectrum between vascular and Alzheimer's like pathology. The latter group can be identified using baseline MRI, with 73% converting within 5 years. It was not possible to predict the vascular dominant dementia subgroup, however 19% of negative predictions with high levels of vascular disease would ultimately develop dementia. It may be that techniques more sensitive to white matter damage, such as diffusion weighted imaging, may prove more useful for this vascular dominant subgroup in the future. This work provides a way to accurately stratify patients using a baseline MRI scan, and has utility in future clinical trials designed to slow or prevent the onset of dementia in these high-risk cohorts.

Decreased hemodynamic response in inferior frontotemporal regions in elderly with mild cognitive impairment

The verbal fluency task (VFT) is a well-established cognitive marker for mild cognitive impairment (MCI) in the prodromal stage of Alzheimer´s dementia (AD). The behavioral VFT performance of patients allows the prediction of dementia two years later. But effective compensatory mechanism might cover or reduce the predictive value of the VFT. Therefore the aim of this study is to measure the hemodynamic response during VFT in patients with mild cognitive impairment (MCI) to establish the hemodynamic response during the VFT as a screening instrument for the prediction of dementia. One method which allows measuring the hemodynamic response during speech production without severe problems with moving artifacts like in functional magnetic resonance imaging (fMRI) is the functional near-infrared spectroscopy (fNIRS). It is optimal as a screening instrument, as it is easy to apply and without any contraindications. In this study we assessed the hemodynamic response in prefrontal and temporal regions in patients with MCI as well as matched healthy controls with fNIRS. We found a decreased hemodynamic response in the inferior frontotemporal cortex for the MCI group. Our results indicate that a frontotemporal decreased hemodynamic response could serve as a diagnostic biomarker for dementia.