Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 μm (PM2.5) may increase the risk for Alzheimer's disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer's disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer's disease associated with exposure. Participants included older females (n = 998; aged 73-87) enrolled in both the Women's Health Initiative Study of Cognitive Aging and the Women's Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999-2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1-3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005-06; MRI-2: 2009-10). Subjects were assigned Alzheimer's disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer's disease), developed by supervised machine learning and validated with data from the Alzheimer's Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 μg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1-3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer's disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1-3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer's disease risk, independent of cerebrovascular damage.

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Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer’s disease

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

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Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Test-retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners.

Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.

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Hippocampal subfield volumetry from structural isotropic 1 mm3 MRI scans: A note of caution.

Alzheimer’s disease (AD) is the most common neurodegenerative disease in elderly individuals. We conducted this study to examine whether alterations in the fractional amplitudes of low-frequency fluctuations (fALFF) in the AD spectrum were frequency-dependent and symptom-relevant. A total of 43 patients with subjective cognitive decline (SCD), 52 with amnestic mild cognitive impairment (aMCI), 44 with Alzheimer’s dementia (d-AD) and 55 well-matched controls participated in resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitudes were measured using fALFF within the slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz) bands. Repeated-measures analysis of variance was performed on fALFF within two bands and correlated with neuropsychological test scores. The significant main effects of frequency and group on fALFF differed widely across brain regions. There were more varied areas in the slow-5 band than the slow-4 band. The fALFF associated with primary disease effects was mainly distributed in the parietal lobe. Obvious frequency band and group interaction effects were observed in the left angular gyrus, left calcarine fissure and surrounding cortex, left superior cerebellum, left cuneus and right lingual gyrus. Neuropsychological tests scores were significantly correlated with the fALFF magnitude of the left cuneus and right lingual in the slow-5 band. Our results suggested that the AD continuum had abnormal amplitudes in intrinsic brain activity, and these abnormalities were frequency-dependent and mainly associated with the slow-5 band rather than the slow-4 band. This may guide the frequency choice of future rs-fMRI studies and provide new insights into the neuropathophysiology of AD.

Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer’s disease: a resting-state fMRI study

Background: The hippocampus and hippocampal subfields have been found to be diversely affected in Alzheimer’s Disease (AD) and early stages of Alzheimer’s disease by neuroimaging studies. However, our knowledge is still lacking about the the trajectories of the hippocampus and hippocampal subfields atrophy with the progression of Alzheimer’s disease.. Objective: To identify which subfields of the hippocampus differ in the trajectories of Alzheimer’s disease by magnetic resonance imaging (MRI) and to determine whether individual differences on memory could be explained by structural volumes of hippocampal subfields. Methods: Four groups of participants including 41 AD patients, 43 amnestic mild cognitive impairment (aMCI) patients, 35 subjective cognitive decline (SCD) patients and 42 normal controls (NC) received their structural MRI brain scans. Structural MR images were processed by the FreeSurfer 6.0 image analysis suite to extract the hippocampus and its subfields. Furthermore, we investigated relationships between hippocampal subfield volumes and memory test variables (AVLT-immediate recall, AVLT-delayed recall, AVLT-recognition) and the regression model analyses were controlled for age, gender, education and eTIV. Results: CA1, subiculum, presubiculum, molecular layer and fimbria showed the trend towards significant volume reduction among four groups with the progression of Alzheimer’s disease. Volume of left subiculum was most strongly and actively correlated with performance across AVLT measures. Conclusion: The trend changes in the hippocampus subfields and further illustrate that SCD is the preclinical stage of AD earlier than aMCI. Future studies should aim to associate the atrophy of the hippocampal subfields in SCD with possible conversion to aMCI or AD with longitudinal design.

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Trajectories of the Hippocampal Subfields Atrophy in the Alzheimer's Disease: A Structural Imaging Study.

Soybean straw biochar activating peroxydisulfate to simultaneously eliminate tetracycline and tetracycline resistance bacteria: Insights on the mechanism.

The traditional medical image recognition methods are limited by image resolution, image brightness and color processing parameters, and image quality evaluation is low. In particular, the incomplete visual information of medical images and the disorder of color structure make the complexity of human visual perception and recognition significantly increased and the accuracy is poor. In order to solve the above problems, this paper is based on the mechanism of deep brain information perception and dementia induced brain magnetic resonance imaging (BMI-DVDI). On the one hand, based on the depth fusion of the visual information system, the medical image depth vision system and its perception model with high precision and low complexity are designed for the two damage of medical image quality and the perception of visual information. On the other hand, the dementia model is designed by means of matrix representation of dementia image signal, screening of dementia sensing brain signal and image reconstruction. The model is helpful to solve the problems of image signal deformation, measurement precision of signal degree and reconstruction of image enhancement in brain magnetic resonance imaging. This model enhances the accuracy of brain diseases such as dementia. Then, we combine the sensing algorithm with the degree of dementia in the brain, and apply it to the MRI of the brain. Finally, through simulation experiments and nuclear magnetic resonance imaging experiments, the space complexity, time complexity, system execution efficiency and image quality evaluation are compared. The result is that the proposed algorithm has excellent performance.

Mengfei He

Papers

Trajectories of the Hippocampal Subfields Atrophy in the Alzheimer's Disease: A Structural Imaging Study.

Soybean straw biochar activating peroxydisulfate to simultaneously eliminate tetracycline and tetracycline resistance bacteria: Insights on the mechanism.

Brain MRI imaging mechanism based on deep visual information perception and dementia degree induction

Single Image Depth Estimation Using Improved U-Net and Edge-Guide Loss

Reducing agents enhanced prometon degradation by CuBi2O4/peroxymonosulfate: Development of interfacial electron transport and circulation of Cu+/Cu2+