Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease

BACKGROUND AND PURPOSE

Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD).

METHODS

The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls.

RESULTS

Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden.

CONCLUSIONS

The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

Physical Activity-Induced Modification of the Association of Long-Term Air Pollution Exposure with the Risk of Depression in Older Adults

PURPOSE

Evidence suggests that long-term air pollution exposures may induce depression; however, the influence of physical activity on this effect is unclear. We investigated modification of the associations between air pollution exposures and depression by the intensity of physical activity.

MATERIALS AND METHODS

This cross-sectional study included 1454 Korean adults. Depression was defined as a Geriatric Depression Scale score ≥8. Concentrations of particulate matter (PM10 and PM2.5: diameter ≤10 µm and ≤2.5 µm, respectively) and nitrogen dioxide (NO2) level at each participant's residential address were estimated. Based on metabolic equivalents, physical activity intensity was categorized as inactive, minimally active, or health-enhancing physical activity (HEPA).

RESULTS

Each 1-part per billion (ppb) NO2 concentration increase was significantly associated with a 6% [95% confidence interval (CI), 4%-8%] increase in depression risk. In older adults (≥65 years), a 1-ppb NO2 increase was associated (95% CI) with a 4% (1%-7%), 9% (5%-13%), and 21% (9%-33%) increase in depression risk in the inactive, minimally active, and HEPA groups, respectively. Compared with the inactive group, the minimally active (p=0.039) and HEPA groups (p=0.004) had higher NO2 exposure-associated depression risk. Associations of PM10 and PM2.5 with depression did not significantly differ by the intensity of physical activity.

CONCLUSION

We suggest that older adults who vigorously exercise outdoors may be susceptible to air pollution-related depression.

Polycyclic aromatic hydrocarbons and changes in brain cortical thickness and an Alzheimer's disease-specific marker for cortical atrophy in adults: A longitudinal neuroimaging study of the EPINEF cohort

Although several epidemiological studies have suggested that exposure to polycyclic aromatic hydrocarbons (PAHs) may induce brain atrophy, no longitudinal study has investigated the effect of PAH exposure on brain structural changes. This study examined the longitudinal associations between urinary PAH metabolites and brain cortical thickness. We obtained urinary concentrations of PAH metabolites and brain magnetic resonance images from 327 adults (≥50 years of age) without dementia at baseline and 3-year follow-up. We obtained whole-brain and regional cortical thicknesses, as well as an Alzheimer's disease (AD)-specific marker for cortical atrophy (a higher score indicated a greater similarity to patients with AD) at baseline and follow-up. We built a linear mixed-effect model including each of urinary PAH metabolites as the time-varying exposure variable of interest. We found that increases in urinary concentrations of 1-hydroxypyrene (β = -0.004; 95% CI, -0.008 to -0.001) and 2-hydroxyfluorene (β = -0.011; 95% CI, -0.015 to -0.006) were significantly associated with a reduced whole-brain cortical thickness. A urinary concentration of 2-hydroxyfluorene was significantly associated with an increased AD-specific cortical atrophy score (β = 2.031; 95% CI, 0.512 to 3.550). The specific brain regions showing the association of urinary concentrations of 1-hydroxypyrene, 2-naphthol, 1-hydroxyphenanthrene, or 2-hydroxyfluorene with cortical thinning were the frontal, parietal, temporal, and cingulate lobes. These findings suggested that exposure to PAHs may reduce brain cortical thickness and increase the similarity to AD-specific cortical atrophy patterns in adults.

Associations of ambient manganese exposure with brain gray matter thickness and white matter hyperintensities

Manganese (Mn) exposure is associated with increased risks of dementia and cerebrovascular disease. However, evidence regarding the impact of ambient Mn exposure on brain imaging markers is scarce. We aimed to investigate the association between ambient Mn exposure and brain imaging markers representing neurodegeneration and cerebrovascular lesions. We recruited a total of 936 adults (442 men and 494 women) without dementia, movement disorders, or stroke from the Republic of Korea. Ambient Mn concentrations were predicted at each participant's residential address using spatial modeling. Neurodegeneration-related brain imaging markers, such as the regional cortical thickness, were estimated using 3 T brain magnetic resonance images. White matter hyperintensity volume (an indicator of cerebrovascular lesions) was also obtained from a certain number of participants (n = 397). Linear regression analyses were conducted after adjusting for potential confounders. A log-transformed ambient Mn concentration was associated with thinner parietal (β = -0.02 mm; 95% confidence interval [CI], -0.05 to -0.01) and occipital cortices (β = -0.03 mm; 95% CI, -0.04 to -0.01) after correcting for multiple comparisons. These associations remained statistically significant in men. An increase in the ambient Mn concentration was also associated with a greater volume of deep white matter hyperintensity in men (β = 772.4 mm3, 95% CI: 36.9 to 1508.0). None of the associations were significant in women. Our findings suggest that ambient Mn exposure may induce cortical atrophy in the general adult population.

Associations of Particulate Matter Exposures With Brain Gray Matter Thickness and White Matter Hyperintensities: Effect Modification by Low-Grade Chronic Inflammation

BACKGROUND

Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH).

METHODS

We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant's home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction.

RESULTS

Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m³ increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07-2.97) and periventricular WMH (2.00; 1.20-3.33). A 1 μg/m³ increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08-2.56). These associations did not significantly differ by the level of high sensitivity CRP.

CONCLUSION

Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.

Correlation Between Neuromelanin-Sensitive MRI and 18F-FP-CIT PET in Early-Stage Parkinson's Disease: Utility of a Voxel-Wise Analysis by Using High-Spatial-Resolution MRI

BACKGROUND AND PURPOSE

The correlation between dopamine transporter (DAT) imaging and neuromelanin-sensitive magnetic resonance imaging (NM-MRI) in early-stage Parkinson's disease (PD) has not yet been established. This study aimed to determine the correlation between NM-MRI and DAT positron-emission tomography (PET) in patients with early-stage PD.

METHODS

Fifty drug-naïve patients with early-stage PD who underwent both 0.8-mm isovoxel NM-MRI and DAT PET were enrolled retrospectively. Using four regions of interest (nigrosome 1 and nigrosome 2 [N1 and N2] regions) from a previous study, the contrast ratios (CRs) of 12 regions were measured: N1, N2, flipped N1, flipped N2, combined N1 and N2, and whole substantia nigra pars compacta [SNpc] (all on both sides). The clinically more affected side was separately assessed. The standardized uptake value ratios (SUVRs) were measured in the striatum using DAT PET. A partial correlation analysis was performed between the SUVR and CR measurements.

RESULTS

CR of the flipped left N1 region was significantly correlated with SUVR of the right posterior putamen (p=0.047), and CR values of the left N1 region, left N2 region, flipped right N1 region, and combined left N1 and N2 regions were significantly correlated with SUVR of the left posterior putamen (p=0.011, 0.038, 0.020, and 0.010, respectively). SUVR of the left anterior putamen was significantly correlated with CR of the left N2 region (p=0.027). On the clinically more affected side, the CR values of the N1 region, combined N1 and N2 regions, and the whole SNpc were significantly correlated with SUVR of the posterior putamen (p=0.001, 0.024, and 0.021, respectively). There were significant correlations between the SUVR of the anterior putamen and the CR values of the N1 region, combined N1 and N2 regions, and whole SNpc (p=0.027, 0.001, and 0.036, respectively).

CONCLUSIONS

This study found that there were significant correlations between CR values in the SNpc on NM-MRI and striatal SUVR values on DAT PET on both sides in early-stage PD.

Effects of the APOEɛ4 Allele on the Relationship Between Tau and Amyloid-β in Early- and Late-Onset Alzheimer's Disease

BACKGROUND

Little is known regarding the differential effects of the apolipoprotein E (APOE) ɛ4 on the regional topography of amyloid and tau in patients with both early-onset (EOAD) and late-onset Alzheimer's disease (LOAD).

OBJECTIVE

To compare the distribution and association of tau, amyloid, and cortical thickness among groups classified by the presence of APOEɛ4 allele and onset age.

METHODS

A total of 165 participants including 54 EOAD patients (29 ɛ4-; 25 ɛ4+), 45 LOAD patients (21 ɛ4-; 24 ɛ4+), and 66 age-matched controls underwent 3T MRI, 18F-THK5351 (THK) and 18F-flutemetamol (FLUTE) PET scans, APOE genotyping, and neuropsychological tests. Data for voxel-wise and standardized uptake values from PET scans were analyzed in the context of APOE and age at onset.

RESULTS

EOAD ɛ4- patients showed greater THK retention in the association cortices, whereas their EOAD ɛ4+ counterparts had more retention in medial temporal areas. THK topography of LOAD ɛ4+ was similar to EOAD ɛ4 + . THK correlated positively with FLUTE and conversely with mean cortical thickness, being lowest in EOAD ɛ4-, highest in LOAD ɛ4-, and modest in ɛ4+ groups. Even in the APOEɛ4+ groups, THK tended to correlate with FLUTE and mean cortical thickness in the inferior parietal region in EOAD and in the medial temporal region in LOAD. LOAD ɛ4- manifested with prevalent small vessel disease markers and the lowest correlation between THK retention and cognition.

CONCLUSION

Our observations suggest the differential effects of the APOEɛ4 on the relationship between tau and amyloid in EOAD and LOAD.

Alzheimer's disease-like cortical atrophy mediates the effect of air pollution on global cognitive function

Little is known about the effect of air pollution on Alzheimer's disease (AD)-specific brain structural pathologies. There is also a lack of evidence on whether this effect leads to poorer cognitive function. We investigated whether, and the extent to which, AD-like cortical atrophy mediated the association between air pollution exposures and cognitive function in dementia-free adults. We used cross-sectional data from 640 participants who underwent brain magnetic resonance imaging and the Montreal Cognitive Assessment (MoCA). Mean cortical thickness (as the measure of global cortical atrophy) and machine learning-based AD-like cortical atrophy score were estimated from brain images. Concentrations of particulate matter with diameters ≤ 10 μm (PM₁₀) and ≤ 2.5 μm (PM_2.5) and nitrogen dioxide (NO₂) were estimated based on each participant's residential address. Following the product method, a mediation effect was tested by conducting a series of three regression analyses (exposure to outcome; exposure to mediator; and exposure and mediator to outcome). A 10 μg/m³ increase in PM₁₀ (β = -1.13; 95 % CI, -1.73 to -0.53) and a 10 ppb increase in NO₂ (β = -1.09; 95 % CI, -1.40 to -0.78) were significantly associated with a lower MoCA score. PM₁₀ (β = 0.27; 95 % CI, 0.06 to 0.48) and NO₂ (β = 0.35; 95 % CI, 0.25 to 0.45) were significantly associated with an increased AD-like cortical atrophy score. Effects of PM₁₀ and NO₂ on MoCA scores were significantly mediated by mean cortical thickness (proportions mediated: 25 %-28 %) and AD-like cortical atrophy scores (13 %-16 %). The findings suggest that air pollution exposures may induce AD-like cortical atrophy, and that this effect may lead to poorer cognitive function in dementia-free adults.

Transferability of Alzheimer Disease Polygenic Risk Score Across Populations and Its Association With Alzheimer Disease-Related Phenotypes

IMPORTANCE

Polygenic risk scores (PRSs), which aggregate the genetic effects of single-nucleotide variants identified in genome-wide association studies (GWASs), can help distinguish individuals at a high genetic risk for Alzheimer disease (AD). However, genetic studies have predominantly focused on populations of European ancestry.

OBJECTIVE

To evaluate the transferability of a PRS for AD in the Korean population using summary statistics from a prior GWAS of European populations.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study developed a PRS based on the summary statistics of a large-scale GWAS of a European population (the International Genomics of Alzheimer Project; 21 982 AD cases and 41 944 controls). This PRS was tested for an association with AD dementia and its related phenotypes in 1634 Korean individuals, who were recruited from 2013 to 2019. The association of a PRS based on a GWAS of a Japanese population (the National Center for Geriatrics and Gerontology; 3962 AD cases and 4074 controls) and a transancestry meta-analysis of European and Japanese GWASs was also evaluated. Data were analyzed from December 2020 to June 2021.

MAIN OUTCOMES AND MEASURES

Risk of AD dementia, amnestic mild cognitive impairment (aMCI), earlier symptom onset, and amyloid β deposition (Aβ).

RESULTS

A total of 1634 Korean patients (969 women [59.3%]), including 716 individuals (43.6%) with AD dementia, 222 (13.6%) with aMCI, and 699 (42.8%) cognitively unimpaired controls, were analyzed in this study. The mean (SD) age of the participants was 71.6 (9.0) years. Higher PRS was associated with a higher risk of AD dementia independent of APOE ɛ4 status in the Korean population (OR, 1.95; 95% CI, 1.40-2.72; P < .001). Furthermore, PRS was associated with aMCI, earlier symptom onset, and Aβ deposition independent of APOE ɛ4 status. The PRS based on a transancestry meta-analysis of data sets comprising 2 distinct ancestries showed a slightly improved accuracy.

CONCLUSIONS AND RELEVANCE

In this cohort study, a PRS derived from a European GWAS identified individuals at a high risk for AD dementia in the Korean population. These findings emphasize the transancestry transferability and clinical value of PRSs and suggest the importance of enriching diversity in genetic studies of AD.

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Background

Brain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.

Objective

This study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.

Methods

This study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.

Results

Our proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.

Conclusion

Our unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation

Structural changes in the brain due to Alzheimer's disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.

IntraBrain Injector (IBI): A Stereotactic-Guided Device for Repeated Delivery of Therapeutic Agents Into the Brain Parenchyma

BACKGROUND

To deliver therapeutics into the brain, it is imperative to overcome the issue of the blood-brain-barrier (BBB). One of the ways to circumvent the BBB is to administer therapeutics directly into the brain parenchyma. To enhance the treatment efficacy for chronic neurodegenerative disorders, repeated administration to the target location is required. However, this increases the number of operations that must be performed. In this study, we developed the IntraBrain Injector (IBI), a new implantable device to repeatedly deliver therapeutics into the brain parenchyma.

METHODS

We designed and fabricated IBI with medical grade materials, and evaluated the efficacy and safety of IBI in 9 beagles. The trajectory of IBI to the hippocampus was simulated prior to surgery and the device was implanted using 3D-printed adaptor and surgical guides. Ferumoxytol-labeled mesenchymal stem cells (MSCs) were injected into the hippocampus via IBI, and magnetic resonance images were taken before and after the administration to analyze the accuracy of repeated injection.

RESULTS

We compared the planned vs. insertion trajectory of IBI to the hippocampus. With a similarity of 0.990 ± 0.001 (mean ± standard deviation), precise targeting of IBI was confirmed by comparing planned vs. insertion trajectories of IBI. Multiple administrations of ferumoxytol-labeled MSCs into the hippocampus using IBI were both feasible and successful (success rate of 76.7%). Safety of initial IBI implantation, repeated administration of therapeutics, and long-term implantation have all been evaluated in this study.

CONCLUSION

Precise and repeated delivery of therapeutics into the brain parenchyma can be done without performing additional surgeries via IBI implantation.

Reduced brain subcortical volumes in patients with glaucoma: a pilot neuroimaging study using the region-of-interest-based approach

Background

While numerous neuroimaging studies have demonstrated that glaucoma is associated with smaller volumes of the visual cortices in the brain, only a few studies have linked glaucoma with brain structures beyond the visual cortices. Therefore, the objective of this study was to compare brain imaging markers and neuropsychological performance between individuals with and without glaucoma.

Methods

We identified 64 individuals with glaucoma and randomly selected 128 age-, sex-, and education level-matched individuals without glaucoma from a community-based cohort. The study participants underwent 3 T brain magnetic resonance imaging and neuropsychological assessment battery. Regional cortical thickness and subcortical volume were estimated from the brain images of the participants. We used a linear mixed model after adjusting for potential confounding variables.

Results

Cortical thickness in the occipital lobe was significantly smaller in individuals with glaucoma than in the matched individuals (β = − 0.04 mm, P = 0.014). This did not remain significant after adjusting for cardiovascular risk factors (β = − 0.02 mm, P = 0.67). Individuals with glaucoma had smaller volumes of the thalamus (β = − 212.8 mm³, P = 0.028), caudate (β = − 170.0 mm³, P = 0.029), putamen (β = − 151.4 mm³, P = 0.051), pallidum (β = − 103.6 mm³, P = 0.007), hippocampus (β = − 141.4 mm³, P = 0.026), and amygdala (β = − 87.9 mm³, P = 0.018) compared with those without glaucoma. Among neuropsychological battery tests, only the Stroop color reading test  score was significantly lower in individuals with glaucoma compared with those without glaucoma (β = − 0.44, P = 0.038).

Conclusions

We found that glaucoma was associated with smaller volumes of the thalamus, caudate, putamen, pallidum, amygdala, and hippocampus.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02807-x.

[18F]THK-5351 PET Patterns in Patients With Alzheimer's Disease and Negative Amyloid PET Findings

Background and Purpose

Alzheimer’s disease (AD) does not always mean amyloid positivity. [¹⁸F]THK-5351 has been shown to be able to detect reactive astrogliosis as well as tau accompanied by neurodegenerative changes. We evaluated the [¹⁸F]THK-5351 retention patterns in positron-emission tomography (PET) and the clinical characteristics of patients clinically diagnosed with AD dementia who had negative amyloid PET findings.

Methods

We performed 3.0-T magnetic resonance imaging, [¹⁸F]THK-5351 PET, and amyloid PET in 164 patients with AD dementia. Amyloid PET was visually scored as positive or negative. [¹⁸F]THK-5351 PET were visually classified as having an intratemporal or extratemporal spread pattern.

Results

The 164 patients included 23 (14.0%) who were amyloid-negative (age 74.9±8.3 years, mean±standard deviation; 9 males, 14 females). Amyloid-negative patients were older, had a higher prevalence of diabetes mellitus, and had better visuospatial and memory functions. The frequency of the apolipoprotein E ε4 allele was higher and the hippocampal volume was smaller in amyloid-positive patients. [¹⁸F]THK-5351 uptake patterns of the amyloid-negative patients were classified into intratemporal spread (n=10) and extratemporal spread (n=13). Neuropsychological test results did not differ significantly between these two groups. The standardized uptake value ratio of [¹⁸F]THK-5351 was higher in the extratemporal spread group (2.01±0.26 vs. 1.61±0.15, p=0.001). After 1 year, Mini Mental State Examination (MMSE) scores decreased significantly in the extratemporal spread group (-3.5±3.2, p=0.006) but not in the intratemporal spread group (-0.5±2.8, p=0.916). The diagnosis remained as AD (n=5, 50%) or changed to other diagnoses (n=5, 50%) in the intratemporal group, whereas it remained as AD (n=8, 61.5%) or changed to frontotemporal dementia (n=4, 30.8%) and other diagnoses (n=1, 7.7%) in the extratemporal spread group.

Conclusions

Approximately 70% of the patients with amyloid-negative AD showed abnormal [¹⁸F]THK-5351 retention. MMSE scores deteriorated rapidly in the patients with an extratemporal spread pattern.

Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

Objective

Analyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as “center effects,” and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).

Methods

For MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.

Results

First, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.

Conclusion

Through comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer’s disease or Parkinson’s disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.

The moderated mediating effect of gender in the relationship between unemployment, depression, and suicide during the COVID-19 pandemic: An examination based on big data

Introduction

The COVID-19 pandemic, and the consequent recession, have caused a decline in the job market, with the resultant job insecurity increasing the risk of depression. While this affected all genders, suicidal thoughts were observed to be more common among women than men, suggesting that the impact of unemployment on depression varies by gender, with gender differences affecting the outcome of depression.

Objectives

This study aims to verify the moderating effect of gender on the structural relationship between unemployment, depression, and suicide during the COVID-19 pandemic by using online search trend data.

Methods

The study utilized the search trend data from Naver’s Data Lab service, by analyzing the searches of men and women under 65, between March, 2020 and September 12, 2021. The search terms were “unemployment,” “depression,” and “suicide.” The analysis examined 1121 searches using the Model 7 research model through the SPSS Process Macro to verify the moderating effect of gender on the mediating pathways for unemployment, depression, and suicide.

Results

We observed that searches for “unemployment” significantly increased with searches for “depression” (B=1.860, p<.001) and “suicide” (B=.860, p<.001). The analysis further revealed that the correlation between the increase in searches relating to depression and unemployment was seen more in women than men. This resulted in an accompanying increase in the volume of searches for suicide (B=2.341, p<.001).

Conclusions

The job insecurity caused by the COVID-19 pandemic led to varying degrees of depression according to gender. Thus, social security measures related to unemployment, depression, and suicide interventions require a gender-specific approach.

Disclosure

No significant relationships.

Clinical Characteristic in Primary Progressive Aphasia in Relation to Alzheimer's Disease Biomarkers

BACKGROUND

Primary progressive aphasia (PPA) is associated with amyloid-β (Aβ) pathology. However, clinical feature of PPA based on Aβ positivity remains unclear.

OBJECTIVE

We aimed to assess the prevalence of Aβ positivity in patients with PPA and compare the clinical characteristics of patients with Aβ-positive (A+) and Aβ-negative (A-) PPA. Further, we applied Aβ and tau classification system (AT system) in patients with PPA for whom additional information of in vivo tau biomarker was available.

METHODS

We recruited 110 patients with PPA (41 semantic [svPPA], 27 non-fluent [nfvPPA], 32 logopenic [lvPPA], and 10 unclassified [ucPPA]) who underwent Aβ-PET imaging at multi centers. The extent of language impairment and cortical atrophy were compared between the A+ and A-PPA subgroups using general linear models.

RESULTS

The prevalence of Aβ positivity was highest in patients with lvPPA (81.3%), followed by ucPPA (60.0%), nfvPPA (18.5%), and svPPA (9.8%). The A+ PPA subgroup manifested cortical atrophy mainly in the left superior temporal/inferior parietal regions and had lower repetition scores compared to the A-PPA subgroup. Further, we observed that more than 90% (13/14) of the patients with A+ PPA had tau deposition.

CONCLUSION

Our findings will help clinicians understand the patterns of language impairment and cortical atrophy in patients with PPA based on Aβ deposition. Considering that most of the A+ PPA patents are tau positive, understanding the influence of Alzheimer's disease biomarkers on PPA might provide an opportunity for these patients to participate in clinical trials aimed for treating atypical Alzheimer's disease.

Cohort Profile: The Environmental-Pollution-Induced Neurological EFfects (EPINEF) study, a multicenter cohort study of Korean adults

The general population is exposed to numerous environmental pollutants, and it remains unclear which pollutants affect the brain, accelerating brain aging and increasing the risk of dementia. The Environmental-Pollution-Induced Neurological Effects study is a multi-city prospective cohort study aiming to comprehensively investigate the effect of different environmental pollutants on brain structures, neuropsychological function, and the development of dementia in adults. The baseline data of 3,775 healthy elderly people were collected from August 2014 to March 2018. The eligibility criteria were age ≥50 years and no self-reported history of dementia, movement disorders, or stroke. The assessment included demographics and anthropometrics, laboratory test results, and individual levels of exposure to air pollution. A neuroimaging sub-cohort was also recruited with 1,022 participants during the same period, and brain magnetic resonance imaging and neuropsychological tests were conducted. The first follow-up environmental pollutant measurements will start in 2022 and the follow-up for the sub-cohort will be conducted every 3-4 years. We have found that subtle structural changes in the brain may be induced by exposure to airborne pollutants such as particulate matter 10 μm or less in diameter (PM₁₀), particulate matter 2.5 μm or less in diameter (PM_2.5) and Mn₁₀, manganese in PM₁₀; Mn_2.5, manganese in PM_2.5. PM₁₀, PM_2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues.

OBJECTIVE

We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers.

METHODS

We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers).

RESULTS

Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity.

CONCLUSION

Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

The effect of body fatness on regional brain imaging markers and cognitive function in healthy elderly mediated by impaired glucose metabolism

Brain atrophy is related to vascular risk factors and can increase cognitive dysfunction risk. This community-based, cross-sectional study investigated whether glucose metabolic disorders due to body fatness are linked to regional changes in brain structure and a decline in neuropsychological function in cognitively healthy older adults. From 2016 to 2019, 429 participants underwent measurements for cortical thickness and subcortical volume using 3 T magnetic resonance imaging and for cognitive function using the neuropsychological screening battery. The effects of body fatness mediated by impaired glucose metabolism on neuroimaging markers and cognitive function was investigated using partial least square structural equation modeling. Total grey matter volume (β = -0.020; bias-corrected (BC) 95% confidence interval (CI) = -0.047 to -0.006), frontal (β = -0.029; BC 95% CI = -0.063 to -0.005) and temporal (β = -0.022; BC 95% CI = -0.051 to -0.004) lobe cortical thickness, and hippocampal volume (β = -0.029; BC 95% CI = -0.058 to -0.008) were indirectly related to body fatness. Further, frontal/temporal lobe thinning was associated with recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001) and delayed recall for visual information (β = -0.005; BC 95% CI = -0.013 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001). Additionally, the smaller the hippocampal volume, the lower the score in recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001), delayed recall for visual information (β = -0.005; BC 95% CI = -0.012 to -0.001), and verbal learning (β = -0.008; BC 95% CI = -0.017 to -0.002). Our findings indicate that impaired glucose metabolism caused by excess body fatness affects memory decline as well as regional grey matter atrophy in elderly individuals with no neurological disease.

Association Between Visceral Fat and Brain Cortical Thickness in the Elderly: A Neuroimaging Study

Background

Despite emerging evidence suggesting that visceral fat may play a major role in obesity-induced neurodegeneration, little evidence exists on the association between visceral fat and brain cortical thickness in the elderly.

Purpose

We aimed to examine the association between abdominal fat and brain cortical thickness in a Korean elderly population.

Methods

This cross-sectional study included elderly individuals without dementia (n = 316). Areas of visceral fat and subcutaneous fat (cm²) were estimated from computed tomography scans. Regional cortical thicknesses (mm) were obtained by analyzing brain magnetic resonance images. Given the inverted U-shaped relationship between visceral fat area and global cortical thickness (examined using a generalized additive model), visceral fat area was categorized into quintiles, with the middle quintile being the reference group. A generalized linear model was built to explore brain regions associated with visceral fat. The same approach was used for subcutaneous fat.

Results

The mean (standard deviation) age was 67.6 (5.0) years. The highest quintile (vs. the middle quintile) group of visceral fat area had reduced cortical thicknesses in the global [β = -0.04 mm, standard error (SE) = 0.02 mm, p = 0.004], parietal (β = -0.04 mm, SE = 0.02 mm, p = 0.01), temporal (β = -0.05 mm, SE = 0.02 mm, p = 0.002), cingulate (β = -0.06 mm, SE = 0.02 mm, p = 0.01), and insula lobes (β = -0.06 mm, SE = 0.03 mm, p = 0.02). None of the regional cortical thicknesses significantly differed between the highest and the middle quintile groups of subcutaneous fat area.

Conclusion

The findings suggest that a high level of visceral fat, but not subcutaneous fat, is associated with a reduced cortical thickness in the elderly.

Early-stage Parkinson's disease: Abnormal nigrosome 1 and 2 revealed by a voxelwise analysis of neuromelanin-sensitive MRI

Previous pathologic studies evaluated the substantia nigra pars compacta (SNpc) of a limited number of idiopathic Parkinson's disease (IPD) patients with relatively longer disease durations. Therefore, it remains unknown which region of the SNpc is most significantly affected in early‐stage IPD. We hypothesized that a voxelwise analysis of thin‐section neuromelanin‐sensitive MRI (NM‐MRI) may help determine the significantly affected regions of the SNpc in early‐stage IPD and localize these areas in each nigrosome on high‐spatial‐resolution susceptibility map‐weighted imaging (SMwI). Ninety‐six healthy subjects and 50 early‐stage IPD patients underwent both a 0.8 × 0.8 × 0.8 mm³ NM‐MRI and a 0.5 × 0.5 × 1.0 mm³ multi‐echo gradient‐recalled echo imaging for SMwI. Both NM‐MRI and SMwI templates were created by using image data from the 96 healthy subjects. Permutation‐based nonparametric tests were conducted to investigate spatial differences between the two groups in NM‐MRI, and the results were displayed on both NM‐MRI and SMwI templates. The posterolateral and anteromedial regions of the SNpc in NM‐MRI were significantly different between the two groups, corresponding to the nigrosome 1 and nigrosome 2 regions, respectively, on the SMwI template. There were the areas of significant spatial difference in the hypointense SN on SMwI between early‐stage IPD patients and healthy subjects. These areas on SMwI were slightly greater than those on NM‐MRI, including the areas showing group difference on NM‐MRI. Our voxelwise analysis of NM‐MRI suggests that two regions (nigrosome 1 and nigrosome 2) of the SNpc are separately affected in early‐stage IPD.

Effects of Alzheimer's and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer's Disease

Early- and late-onset Alzheimer's disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [¹⁸F]-THK5351 PET, [¹⁸F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Voxel-wise partial volume correction method for accurate estimation of tissue sodium concentration in 23 Na-MRI at 7 T

Sodium is crucial for the maintenance of cell physiology, and its regulation of the sodium-potassium pump has implications for various neurological conditions. The distribution of sodium concentrations in tissue can be quantitatively evaluated by means of sodium MRI (²³ Na-MRI). Despite its usefulness in diagnosing particular disease conditions, tissue sodium concentration (TSC) estimated from ²³ Na-MRI can be strongly biased by partial volume effects (PVEs) that are induced by broad point spread functions (PSFs) as well as tissue fraction effects. In this work, we aimed to propose a robust voxel-wise partial volume correction (PVC) method for ²³ Na-MRI. The method is based on a linear regression (LR) approach to correct for tissue fraction effects, but it utilizes a 3D kernel combined with a modified least trimmed square (3D-mLTS) method in order to minimize regression-induced inherent smoothing effects. We acquired ²³ Na-MRI data with conventional Cartesian sampling at 7 T, and spill-over effects due to the PSF were considered prior to correcting for tissue fraction effects using 3D-mLTS. In the simulation, we found that the TSCs of gray matter (GM) and white matter (WM) were underestimated by 20% and 11% respectively without correcting tissue fraction effects, but the differences between ground truth and PVE-corrected data after the PVC using the 3D-mLTS method were only approximately 0.6% and 0.4% for GM and WM, respectively. The capability of the 3D-mLTS method was further demonstrated with in vivo ²³ Na-MRI data, showing significantly lower regression errors (ie root mean squared error) as compared with conventional LR methods (p < 0.001). The results of simulation and in vivo experiments revealed that 3D-mLTS is superior for determining under- or overestimated TSCs while preserving anatomical details. This suggests that the 3D-mLTS method is well suited for the accurate determination of TSC, especially in small focal lesions associated with pathological conditions.

Identification of Heterogeneous Subtypes of Mild Cognitive Impairment Using Cluster Analyses Based on PET Imaging of Tau and Astrogliosis

Background: Mild cognitive impairment (MCI) is a condition with diverse causes and clinical outcomes that can be categorized into subtypes. [¹⁸F]THK5351 has been known to detect reactive astrogliosis as well as tau which is accompanied by neurodegenerative changes. Here, we identified heterogeneous groups of MCI patients using THK retention patterns and a graph theory approach, allowing for the comparison of risk of progression to dementia in these MCI subgroups.

Methods: Ninety-seven participants including 60 MCI patients and individuals with normal cognition (NC, n = 37) were included and undertook 3T MRI, [¹⁸F]THK5351 PET, and detailed neuropsychological tests. [¹⁸F]Flutemetamol PET was also performed in 62 participants. We calculated similarities between MCI patients using their regional standardized uptake value ratio of THK retention in 75 ROIs, and clustered subjects with similar retention patterns using the Louvain method based on the modularity of the graph. The clusters of patients identified were compared with an age-matched control group using a general linear model. Dementia conversion was evaluated after a median follow-up duration of 34.6 months.

Results: MCI patients were categorized into four groups according to their THK retention patterns: (1) limbic type; (2) diffuse type; (3) sparse type; and (4) AD type (retention pattern as in AD). Subjects of the limbic type were characterized by older age, small hippocampal volumes, and reduced verbal memory and frontal/executive functions. Patients of the diffuse type had relatively large vascular burden, reduced memory capacity and some frontal/executive functions. Co-morbidity and mortality were more frequent in this subgroup. Subjects of the sparse type were younger and declined only in terms of visual memory and attention. No individuals in this subgroup converted to dementia. Patients in the AD type group exhibited the poorest cognitive function. They also had the smallest hippocampal volumes and the highest risk of progression to dementia (90.9%).

Conclusion: Using cluster analyses with [¹⁸F]THK5351 retention patterns, it is possible to identify clinically-distinct subgroups of MCI patients and those at greater risk of progression to dementia.

Extended approach or usage of nasoseptal flap is a risk factor for olfactory dysfunction after endoscopic anterior skullbase surgery: results from 928 patients in a single tertiary center

BACKGROUND

The aim of this study was to compare olfactory function change in patients who underwent endoscopic skull-base surgery.

METHODOLOGY

A total of 928 patients were included in this retrospective study. Olfactory function was measured using the non- validated Likert scale (0â€"100), the Cross-Cultural Smell Identification Test (CC-SIT) and the butanol threshold test (BTT). Patients were divided into two groups: an endoscopic trans-sellar approach group (ETA, n = 768) and an extended endoscopic endonasal approach group (EEEA, n = 160). The ETA group was sub-divided into Nasoseptal flap (NSF) and no NSF groups.

RESULTS

Non-validated olfactory function significantly worsened in the EEEA and ETA-NSF groups compared with that in the ETA- no NSF group for at least 6 months post-operatively. Validated olfactory impairment (BTT and CC-SIT) was also significantly worse in the EEEA and NSF groups compared with that in the ETA-no NSF group 3 months post-operatively. Additionally, the degrees of non-validated and validated olfactory deterioration were not significantly different between the EEEA and ETA-NSF groups. We also found that CC-SIT score changes were significantly impaired in tuberculum sellae meningioma patients than in craniopharyn- gioma patients.

CONCLUSIONS

We conclude that NSF was the key factor that led to olfactory impairment after endoscopic skull-base surgery.

Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study

BACKGROUND

Only a limited number of neuroimaging studies have explored the effects of ambient air pollution in adults. The prior studies have investigated only cortical volume, and they have reported mixed findings, particularly for gray matter. Furthermore, the association between nitrogen dioxide (NO2) and neuroimaging markers has been little studied in adults.

OBJECTIVES

We investigated the association between long-term exposure to air pollutants (NO2, particulate matter (PM) with aerodynamic diameters of ≤10μm (PM10) and ≤2.5μm (PM2.5), and neuroimaging markers.

METHODS

The study included 427 men and 530 women dwelling in four cities in the Republic of Korea. Long-term concentrations of PM10, NO2, and PM2.5 at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images. A generalized linear model was used, adjusting for potential confounders.

RESULTS

A 10-μg/m3 increase in PM10 was associated with reduced thicknesses in the frontal [-0.02mm (95% CI: -0.03, -0.01)] and temporal lobes [-0.06mm (95% CI: -0.07, -0.04)]. A 10-μg/m3 increase in PM2.5 was associated with a thinner temporal cortex [-0.18mm (95% CI: -0.27, -0.08)]. A 10-ppb increase in NO2 was associated with reduced thicknesses in the global [-0.01mm (95% CI: -0.01, 0.00)], frontal [-0.02mm (95% CI: -0.03, -0.01)], parietal [-0.02mm (95% CI: -0.03, -0.01)], temporal [-0.04mm (95% CI: -0.05, -0.03)], and insular lobes [-0.01mm (95% CI: -0.02, 0.00)]. The air pollutants were also associated with increased thicknesses in the occipital and cingulate lobes. Subcortical structures associated with the air pollutants included the thalamus, caudate, pallidum, hippocampus, amygdala, and nucleus accumbens.

DISCUSSION

The findings suggest that long-term exposure to high ambient air pollution may lead to cortical thinning and reduced subcortical volume in adults. https://doi.org/10.1289/EHP7133.

A Three-Dimensional Sensor to Recognize Amyloid-β in Blood Plasma of Patients

Detecting amyloid beta (Aβ) in unpurified blood to diagnose Alzheimer's disease (AD) is challenging owing to low concentrations of Aβ and the presence of many other substances in the blood. Here, we propose a 3D sensor for AD diagnosis using blood plasma, with pairs of 3D silicon micropillar electrodes with a comprehensive circuit configuration. The sensor is developed with synthesized artificial peptide and impedance analysis based on a maximum signal-to-noise ratio. Its sensitivity and selectivity were verified using an in vitro test based on samples of human blood serum, which showed its feasibility for application in diagnosis of AD by testing blood plasma of the AD patient. The 3D sensor is designed to improve reliability by checking the impedance of each pair multiple times via constructing a reference pair and a working pair on the same sensor. Therefore, we demonstrate the ability of the 3D sensor to recognize cases of AD using blood plasma and introduce its potential as a self-health care sensor for AD patients.

Association of Subcortical Structural Shapes With Tau, Amyloid, and Cortical Atrophy in Early-Onset and Late-Onset Alzheimer's Disease

The objectives of this study were to compare the topographical subcortical shape and to investigate the effects of tau or amyloid burden on atrophic patterns in early onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). One hundred and sixty-one participants (53 EOAD, 44 LOAD, 33 young controls, and 31 older controls) underwent [18F]THK5351 positron emission tomography (PET), [18F]flutemetamol (FLUTE) PET, and 3T MRI scans. We used surface-based analysis to evaluate subcortical structural shape, permutation-based statistics for group comparisons, and Spearman's correlations to determine associations with THK, FLUTE, cortical thickness, and neuropsychological test results. When compared to their age-matched controls, EOAD patients exhibited shape reduction in the bilateral amygdala, hippocampus, caudate, and putamen, while in LOAD patients, the bilateral amygdala and hippocampus showed decreased shapes. In EOAD, widespread subcortical shrinkage, with less association of the hippocampus, correlated with THK retention and cortical thinning, while in LOAD patients, subcortical structures were limited which had significant correlation with THK or mean cortical thickness. Subcortical structural shape showed less correlation with FLUTE global retention in both EOAD and LOAD. Multiple cognitive domains, except memory function, correlated with the bilateral amygdala, caudate, and putamen in EOAD patients, while more restricted regions in the subcortical structures were correlated with neuropsychological test results in LOAD patients. Subcortical structures were associated with AD hallmarks in EOAD. However, the correlation was limited in LOAD. Moreover, relationship between subcortical structural atrophy and cognitive decline were quite different between EOAD and LOAD. These findings suggest that the effects of Alzheimer's pathologies on subcortical structural changes in EOAD and LOAD and they may have different courses of pathomechanism.

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FP_avg) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FP_avg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach

•

A new two-stage deep learning approach for efficient microbleeds detection is proposed.

•

1st stage detects potential microbleeds candidates, while 2nd stage reduces false positives.

•

A sensitivity of 93.62% is achieved via 1st stage using high in-plane resolution data.

•

The average number of false positives per subject is reduced to 1.42 in the 2nd stage.

•

A validation of using low in-plane resolution data is performed as well.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detection and identification of CMBs in Magnetic Resonance (MR) images is a very challenging task due to their wide distribution throughout the brain, small sizes, and the high degree of visual similarity between CMBs and CMB mimics such as calcifications, irons, and veins. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two-channel input for the regional-based YOLO method. This enables YOLO to learn more reliable and representative hierarchal features and hence achieve better detection performance. The proposed work was independently trained and evaluated using high and low in-plane resolution data, which contained 72 subjects with 188 CMBs and 107 subjects with 572 CMBs, respectively. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and 78.85% and an average number of false positives per subject (FPavg) of 52.18 and 155.50 throughout the five-folds cross-validation for both the high and low in-plane resolution data, respectively. These findings outperformed results by previously utilized techniques such as 3D fast radial symmetry transform, producing fewer FPavg and lower computational cost. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42 and 1.89 for the high and low in-plane resolution data, respectively. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Association between exposure to polycyclic aromatic hydrocarbons and brain cortical thinning: The Environmental Pollution-Induced Neurological EFfects (EPINEF) study

BACKGROUND

Although some studies have suggested that exposure to polycyclic aromatic hydrocarbons (PAHs) induces neurodevelopmental disturbances in children and neurodegeneration in animals, the neurotoxic effect of PAH exposure is unclear in adults. The aim was to examine the associations of PAH exposure with brain structure and neuropsychological function in adults without known neurological diseases.

METHODS

This study included 421 men and 528 women dwelling in four cities in the Republic of Korea. Urinary concentrations of four PAH metabolites (1-hydroxypyrene, 2-naphthol, 1-hydroxyphenanthrene, and 2-hydroxyfluorene) were obtained. Participants underwent brain 3 T magnetic resonance imaging and neuropsychological tests. Cortical thickness and volume were estimated using the region-of-interest method. Separate generalized linear models were constructed for each sex, adjusting for age, years of education, cohabitation status, income, tobacco use, alcohol consumption, and vascular risk factors.

RESULTS

The mean (standard deviation) age was 68.3 (6.6) years in men and 66.4 (6.1) years in women. In men, those in quartile 4 (versus quartile 1, the lowest) of urinary 2-naphthol concentration had cortical thinning in the global (β = -0.03, P = .02), parietal (β = -0.04, P = .01), temporal (β = -0.06, P < .001), and insular lobes (β = -0.05, P = .02). Higher quartiles of urinary 2-naphthol concentration were associated with cortical thinning in the global (P = .01), parietal (P = .004), temporal (P < .001), and insular lobes (P = .01). In women, those in quartile 4 (versus quartile 1) of urinary 1-hydroxypyrene concentration had cortical thinning in the frontal (β = -0.03, P = .006) and parietal lobes (β = -0.03, P = .003). Higher quartiles of urinary 1-hydroxypyrene concentration were associated with cortical thinning in the frontal (P = .006) and parietal lobes (P = .001). In both sexes, verbal learning and memory scores significantly declined with an increase in quartile of urinary 1-hydroxypyrene concentration.

CONCLUSIONS

PAH exposure was associated with cortical thinning and decline in verbal learning and memory function in cognitively healthy adults. This suggests PAHs as an environmental risk factor for neurodegeneration.

Marriage as a social tie in the relation of depressive symptoms attributable to air pollution exposure among the elderly

BACKGROUND

Air pollution is a risk factor for depression or depressive symptoms. However, few studies have examined an effect modifier as a protective factor against depressive symptoms associated with air pollution, including social support. Notably, less is known about a married relationship in the association between exposure to air pollution and depressive symptoms among the elderly.

METHODS

This study included 2122 marrieds and 607 non-marrieds, recruited in 2014-2017 from different regions of South Korea. Depressive symptoms were measured by the Korean version of the Geriatric Depression Scale Short Form (SGDS-K). After adjustment for potential confounders using propensity score of being assigned to the marrieds, we examined the extent of whether the effects of exposure to air pollutants (PM₁₀, PM_2.5, and NO₂) on depressive symptoms were different between marrieds and non-marrieds. Subgroup analyses by gender and residence area were also performed.

RESULTS

Marrieds than non-marrieds were less likely to have depressive symptoms and had smaller SGDS-K associated with increased exposure to PM₁₀ and PM_2.5 concentrations, respectively. After stratification of subjects by gender and residence area, the interaction term appeared to be significant among men and the non-metropolitan group, indicating the protective effect of married relationships on depressive symptoms attributable to air pollution exposure in them.

LIMITATIONS

Although we adjusted the propensity score, our findings might be confounded by the contextual effect associated with married relationships.

CONCLUSIONS

A married relationship, as a social tie, may attenuate the effect of exposure to air pollution on depressive symptoms among the elderly. Nonetheless, additional research is worthwhile to explore the extent of other social relationships in the association between air pollution exposure and depressive symptoms.

[¹⁸F]THK5351 PET Imaging in Patients with Mild Cognitive Impairment

Background and Purpose

Mild cognitive impairment (MCI) is a condition with diverse clinical outcomes and subgroups. Here we investigated the topographic distribution of tau in vivo using the positron emission tomography (PET) tracer [¹⁸F]THK5351 in MCI subgroups.

Methods

This study included 96 participants comprising 38 with amnestic MCI (aMCI), 21 with nonamnestic MCI (naMCI), and 37 with normal cognition (NC) who underwent 3.0-T MRI, [¹⁸F]THK5351 PET, and detailed neuropsychological tests. [¹⁸F]flutemetamol PET was also performed in 62 participants. The aMCI patients were further divided into three groups: 1) verbal-aMCI, only verbal memory impairment; 2) visual-aMCI, only visual memory impairment; and 3) both-aMCI, both visual and verbal memory impairment. Voxel-wise statistical analysis and region-of-interest -based analyses were performed to evaluate the retention of [¹⁸F]THK5351 in the MCI subgroups. Subgroup analysis of amyloid-positive and -negative MCI patients was also performed. Correlations between [¹⁸F]THK5351 retention and different neuropsychological tests were evaluated using statistical parametric mapping analyses.

Results

[¹⁸F]THK5351 retention in the lateral temporal, mesial temporal, parietal, frontal, posterior cingulate cortices and precuneus was significantly greater in aMCI patients than in NC subjects, whereas it did not differ significantly between naMCI and NC participants. [¹⁸F] THK5351 retention was greater in the both-aMCI group than in the verbal-aMCI and visualaMCI groups, and greater in amyloid-positive than amyloid-negative MCI patients. The cognitive function scores were significantly correlated with cortical [¹⁸F]THK5351 retention.

Conclusions

[¹⁸F]THK5351 PET might be useful for identifying distinct topographic patterns of [¹⁸F]THK5351 retention in subgroups of MCI patients who are at greater risk of the progression to Alzheimer's dementia.

Gender Difference in the Effects of Outdoor Air Pollution on Cognitive Function Among Elderly in Korea

Background/Aim: Given a fast-growing aging population in South Korea, the prevalence of cognitive impairment in elderly is increasing. Despite growing evidence of air pollution exposure as one of the risk factors for declining cognition, few studies have been conducted on gender difference in the relation of cognitive function associated with outdoor air pollution. The aim of this study is to investigate the effect modification of gender difference in the association between cognitive function and air pollutant exposure (PM₁₀, PM_2.5−10, and NO₂).

Methods: The study focused on elderly, and the resulting sample included 1,484 participants aged 55 and older with no neurologic diseases, recruited from the four regions in Korea (Seoul, Incheon, Pyeongchang, and Wonju). We used the Mini-Mental State Examination (MMSE) score (with the conventional cut-off point “23–24”) to assess cognitive decline as the primary outcome of the study. Air pollution data used in this study were based on the 5-year average of predicted PM₁₀ and NO₂ concentrations, as well as the 2015 average PM_2.5 concentration. Additionally, a survey questionnaire was utilized to obtain information about general health assessment. To explore gender differences in the effects of air pollution exposure on cognitive function, we used penalized logistic regression, negative binomial regression, and generalized linear mixed model analyses. Subgroup analyses were also performed by the geographic location of residence (metropolitan vs. non-metropolitan).

Results: We found that women than men had a higher risk for decreased cognitive function associated with increased exposure to PM₁₀ and PM_2.5−10, respectively, even after adjustments for confounding factors (OR 1.01 [95%CI 1.00-1.03] in PM₁₀; OR 1.03 [95%CI 1.01–1.07] in PM_2.5−10). After stratification by metropolitan status, we also found that the adverse effect of NO₂ exposure on cognitive function was higher in women than men [OR 1.02 [95%CI 1.00–1.05] in metropolitan; OR 1.12 [95%CI 1.04–1.20] in non-metropolitan]. Notably, the magnitude of the effect sizes was greater among those in non-metropolitan regions than metropolitan ones.

Conclusions: Although our findings suggest that the adverse effects of outdoor air pollution on cognitive function appeared to be higher in women than men, this should be tentatively reflected due to some limitations in our results. While additional research is warranted to confirm or dispute our results, our findings suggest an indication of the need for developing and implementing prevention or interventions with a focus on elderly women with increased risk for air pollution exposure.

A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Most clinical trials focus on amyloid-β positive (Aβ+) amnestic mild cognitive impairment (aMCI), but screening failures are high because only a half of patients with aMCI are positive on Aβ PET. Therefore, it becomes necessary for clinicians to predict which patients will have Aβ biomarker.

OBJECTIVE

We aimed to compare clinical factors, neuropsychological (NP) profiles, and apolipoprotein E (APOE) genotype between Aβ+ aMCI and Aβ-aMCI and to develop a clinically useful prediction model of Aβ positivity on PET (PET-Aβ+) in aMCI using a nomogram.

METHODS

We recruited 523 aMCI patients who underwent Aβ PET imaging in a nation-wide multicenter cohort. The results of NP measures were divided into following subgroups: 1) Stage (Early and Late-stage), 2) Modality (Visual, Verbal, and Both), 3) Recognition failure, and 4) Multiplicity (Single and Multiple). A nomogram for PET-Aβ+ in aMCI patients was constructed using a logistic regression model.

RESULTS

PET-Aβ+ had significant associations with NP profiles for several items, including high Clinical Dementia Rating Scale Sum of Boxes score (OR 1.47, p = 0.013) and impaired memory modality (impaired both visual and verbal memories compared with visual only, OR 3.25, p = 0.001). Also, presence of APOEɛ4 (OR 4.14, p < 0.001) was associated with PET-Aβ+. These predictors were applied to develop the nomogram, which showed good prediction performance (C-statistics = 0.79). Its prediction performances were 0.77/0.74 in internal/external validation.

CONCLUSIONS

The nomogram consisting of NP profiles, especially memory domain, and APOEɛ4 genotype may provide a useful predictive model of PET-Aβ+ in patients with aMCI.

Topographical Heterogeneity of Alzheimer's Disease Based on MR Imaging, Tau PET, and Amyloid PET

Alzheimer’s disease (AD) patients are known to have heterogeneous clinical presentation and pathologic patterns. We hypothesize that AD dementia can be categorized into subtypes based on multimodal imaging biomarkers such as magnetic resonance imaging (MRI), tau positron emission tomography (PET), and amyloid PET. We collected 3T MRI, ¹⁸F-THK5351 PET, and ¹⁸F-flutemetamol (FLUTE) PET data from 83 patients with AD dementia [Clinical Dementia Rating (CDR) ≤1] and 60 normal controls (NC), and applied surface-based analyses to measure cortical thickness, THK5351 standardized uptake value ratio (SUVR) and FLUTE SUVR for each participant. For the patient group, we performed an agglomerative hierarchical clustering analysis using the three multimodal imaging features on the vertices (n = 3 × 79,950). The identified AD subtypes were compared to NC using general linear models adjusting for age, sex, and years of education. We mapped the effect size within significant cortical regions reaching a corrected p-vertex <0.05 (random field theory). Our surface-based multimodal framework has revealed three distinct subtypes among AD patients: medial temporal-dominant subtype (MT, n = 44), parietal-dominant subtype (P, n = 19), and diffuse atrophy subtype (D, n = 20). The topography of cortical atrophy and THK5351 retention differentiates between the three subtypes. In the case of FLUTE, three subtypes did not show distinct topographical differences, although cortical composite retention was significantly higher in the P type than in the MT type. These three subtypes also differed in demographic and clinical features. In conclusion, AD patients may be clustered into three subtypes with distinct topographical features of cortical atrophy and tau deposition, although amyloid deposition may not differ across the subtypes in terms of topography.

Quantitative assessment of phased array coils with different numbers of receiving channels in terms of signal-to-noise ratio and spatial noise variation in magnetic resonance imaging

The neuroimaging of humans using 7T magnetic resonance imaging (MRI) has been conducted using phased array (PA) coils with different numbers of receiving channels. PA coils with a high number of channels may offer parallel imaging (PI) with a high reduction (R)-factor, which is enabled via under-sampling and coil geometry (g) factor, increasing the radiofrequency signal sensitivity provided by a small coil. The goals of this study were to assess and validate the coil performance of PA coils with different numbers of receiver (Rx)-channels in and to propose the coil selection guidelines by visualizing 7T brain images. The combined magnetic flux density (||B₁||) distributions of four configurations of PA coils—4-, 8-, 12-, and 16-channel Rx-only mode under the local transmit (Tx) mode of birdcage coils—were evaluated using electromagnetic (EM) calculations. These four configurations of PA coils and a local Tx coil were designed and built for a 7T MRI experiment. For 7T brain imaging experiments, all PA coils with (w/) and without (w/o) R-factors were compared in terms of signal-to-noise ratio (SNR) and spatial noise variation (SNV). EM simulation results clearly demonstrated that PA coils with a high number of Rx channels showed more homogeneously distributed ||B₁|| fields than a PA coils with a low number of Rx coils. The results of this study demonstrate that a collection of smaller surface coils can contribute to high RF signal sensitivity in terms of the anatomical coverage of the brain and may facilitate PI. With further improvement in coil technology, researchers and clinicians will be provided with PA coils with different numbers of channels, which can ensure the optimum SNR and PI benefits for 7T brain MR imaging.

Relationships between [¹⁸F]-THK5351 Retention and Language Functions in Primary Progressive Aphasia

BACKGROUND AND PURPOSE

There are three distinct subtypes of primary progressive aphasia (PPA): the nonfluent/agrammatic variant (nfvPPA), the semantic variant (svPPA), and the logopenic variant (lvPPA). We sought to characterize the pattern of [¹⁸F]-THK5351 retention across all three subtypes and determine the topography of [¹⁸F]-THK5351 retention correlated with each neurolinguistic score.

METHODS

We enrolled 50 participants, comprising 13 PPA patients (3 nfvPPA, 5 svPPA, and 5 lvPPA) and 37 subjects with normal cognition (NC) who underwent 3.0-tesla magnetic resonance imaging, [¹⁸F]-THK5351 positron-emission tomography scans, and detailed neuropsychological tests. The PPA patients additionally participated in extensive neurolinguistic tests. Voxel-wise and region-of-interest-based analyses were performed to analyze [¹⁸F]-THK5351 retention.

RESULTS

The nfvPPA patients exhibited higher [¹⁸F]-THK5351 retention in the the left inferior frontal and precentral gyri. In svPPA patients, [¹⁸F]-THK5351 retention was elevated in the anteroinferior and lateral temporal cortices compared to the NC group (left>right). The lvPPA patients exhibited predominant [¹⁸F]-THK5351 retention in the inferior parietal, lateral temporal, and dorsolateral prefrontal cortices, and the precuneus (left>right). [¹⁸F]-THK5351 retention in the left inferior frontal area was associated with lower fluency scores. Comprehension was correlated with [¹⁸F]-THK5351 retention in the left temporal cortices. Repetition was associated with [¹⁸F]-THK5351 retention in the left inferior parietal and posterior temporal areas, while naming difficulty was correlated with retention in the left fusiform and temporal cortices.

CONCLUSIONS

The pattern of [¹⁸F]-THK5351 retention was well matched with clinical and radiological findings for each PPA subtype, in agreement with the anatomical and functional location of each language domain.

Initial diagnostic workup of parkinsonism: Dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T

BACKGROUND AND PURPOSE

Evaluation of dorsal nigral hyperintensity on MRI can help detect nigrostriatal degeneration. We aimed to compare the diagnostic performance between susceptibility map-weighted imaging (SMWI) and N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophenyl) nortropane (¹⁸F-FP-CIT) positron emission tomography (PET) as an initial diagnostic tool of parkinsonism.

MATERIALS AND METHODS

This local ethics committee-approved retrospective study enrolled 223 patients with parkinsonism and 15 healthy subjects (mean age, 69.7 years; 135 females) who underwent both SMWI at 3T and ¹⁸F-FP-CIT PET. The diagnostic performances of the two tests for nigrostriatal degeneration were compared by evaluating whether the 90% confidence interval (CI) of the difference between the two tests was within the equivalence margin by using the DTComPair package of R. The concordance rate was tested by Cohen's kappa.

RESULTS

The diagnostic sensitivities of SMWI and ¹⁸F-FP-CIT PET were 94.5% and 100% per SN and 100% and 100% per participant, respectively; their specificities were 95.3% and 86.7% per SN and 94.4% and 84.0% per participant, respectively. While the diagnostic sensitivity was comparable between the two tests for each SN and participant, the lower 90% CI of the differences in the specificity were -0.086 per SN and -0.104 per participant, indicating a higher diagnostic specificity of SMWI than that of ¹⁸F-FP-CIT PET. When excluding 20 participants with basal ganglia lesions, the two tests exhibited similar diagnostic performance and had excellent agreement (k = 0.899 per SN; k = 0.945 per participant).

CONCLUSION

For patients with parkinsonism, SMWI and ¹⁸F-FP-CIT PET exhibit similar diagnostic performance.

Distinct amyloid distribution patterns in amyloid positive subcortical vascular cognitive impairment

Amyloid-β (Aβ) and cerebral small vessel disease (CSVD) commonly coexist. They can occur independently by chance, or may interact with each other. We aimed to determine whether the distribution of Aβ in subcortical vascular cognitive impairments (SVCI) patients can be classified by the underlying pathobiologies. A total of 45 11C-Pittsburgh compound B PET positive (PiB(+)) SVCI patients were included in this study. They were classified using a new cluster analysis method which adopted the Louvain method, which finds optimal decomposition of the participants based on similarity of relative Aβ deposition pattern. We measured atherosclerotic cerebral small vessel disease (CSVD) markers and cerebral amyloid angiopathy (CAA) markers. Forty-five PiB(+) SVCI patients were classified into two groups: 17 patients with the characteristic Alzheimer's disease like Aβ uptake with sparing of occipital region (OccSp) and 28 patients with occipital predominant Aβ uptake (OccP). Compared to OccSp group, OccP group had more postive association of atherosclerotic CSVD score (p for interaction = 0.044), but not CAA score with occipital/global ratio of PiB uptake. Our findings suggested that Aβ positive SVCI patients might consist of heterogeneous groups with combined CSVD and Aβ resulting from various pathobiologies. Furthermore, atherosclerotic CSVD might explain increased occipital Aβ uptakes.