-
Proceedings of the National Academy of... Jul 2024While the intracellular-extracellular distribution of lactate has been suggested to play a critical role in the healthy and diseased brain, tools are lacking to...
While the intracellular-extracellular distribution of lactate has been suggested to play a critical role in the healthy and diseased brain, tools are lacking to noninvasively probe lactate in intracellular and extracellular spaces. Here, we show that, by measuring the diffusion of lactate with diffusion-weighted magnetic resonance (MR) spectroscopy in vivo and comparing it to the diffusion of purely intracellular metabolites, noninvasive quantification of extracellular and intracellular lactate fractions becomes possible. More specifically, we detect alterations of lactate diffusion in the APP/PS1 mouse model of Alzheimer's disease. Data modeling allows quantifying decreased extracellular lactate fraction in APP/PS1 mice as compared to controls, which is quantitatively confirmed with implanted enzyme-microelectrodes. The capability of diffusion-weighted MR spectroscopy to quantify extracellular-intracellular lactate fractions opens a window into brain metabolism, including in Alzheimer's disease.
Topics: Animals; Lactic Acid; Alzheimer Disease; Brain; Mice; Mice, Transgenic; Diffusion Magnetic Resonance Imaging; Extracellular Space; Disease Models, Animal; Magnetic Resonance Spectroscopy; Male; Amyloid beta-Protein Precursor
PubMed: 38950371
DOI: 10.1073/pnas.2403635121 -
IEEE Transactions on Medical Imaging Jul 2024Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of...
Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.
PubMed: 38949932
DOI: 10.1109/TMI.2024.3421360 -
Autophagy Jul 2024A growing number of studies link dysfunction of macroautophagy/autophagy to the pathogenesis of diseases such as Alzheimer disease (AD). Given the global importance of...
A growing number of studies link dysfunction of macroautophagy/autophagy to the pathogenesis of diseases such as Alzheimer disease (AD). Given the global importance of autophagy for homeostasis, how its dysfunction can lead to specific neurological changes is puzzling. To examine this further, we compared the global deactivation of autophagy in the adult mouse using the iKO with the impact of AD-associated pathogenic changes in autophagic processing of synaptic proteins. Isolated forebrain synaptosomes, rather than total homogenates, from iKO mice demonstrated accumulation of synaptic proteins, suggesting that the synapse might be a vulnerable site for protein homeostasis disruption. Moreover, the deactivation of autophagy resulted in impaired cognitive performance over time, whereas gross locomotor skills remained intact. Despite deactivation of autophagy for 6.5 weeks, changes in cognition were in the absence of cell death or synapse loss. In the symptomatic APP PSEN1 double-transgenic mouse model of AD, we found that the impairment in autophagosome maturation coupled with diminished presence of discrete synaptic proteins in autophagosomes isolated from these mice, leading to the accumulation of one of these proteins in the detergent insoluble protein fraction. This protein, SLC17A7/Vglut, also accumulated in iKO mouse synaptosomes. Taken together, we conclude that synaptic autophagy plays a role in maintaining protein homeostasis, and that while decreasing autophagy interrupts normal cognitive function, the preservation of locomotion suggests that not all circuits are affected similarly. Our data suggest that the disruption of autophagic activity in AD may have relevance for the cognitive impairment in this adult-onset neurodegenerative disease. : 2dRAWM: 2-day radial arm water maze; AD: Alzheimer disease; Aβ: amyloid-beta; AIF1/Iba1: allograft inflammatory factor 1; APP: amyloid beta precursor protein; ATG7: autophagy related 7; AV: autophagic vacuole; CCV: cargo capture value; Ctrl: control; DLG4/PSD-95: discs large MAGUK scaffold protein 4; GFAP: glial fibrillary acidic protein; GRIN2B/NMDAR2b: glutamate ionotropic receptor NMDA type subunit 2B; LTD: long-term depression; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; m/o: months-old; PNS: post-nuclear supernatant; PSEN1/PS1: presenilin 1; SHB: sucrose homogenization buffer; SLC32A1/Vgat: solute carrier family 32 member 1; SLC17A7/Vglut1: solute carrier family 17 member 7; SNAP25: synaptosome associated protein 25; SQSTM1/p62: sequestosome 1; SYN1: synapsin I; SYP: synaptophysin ; SYT1: synaptotagmin 1; Tam: tamoxifen; VAMP2: vesicle associated membrane protein 2; VCL: vinculin; wks: weeks.
PubMed: 38949671
DOI: 10.1080/15548627.2024.2368335 -
Journal of Clinical and Experimental... Jul 2024Prior research on the Noise Pareidolia Test (NPT) has demonstrated its clinical utility in detecting patients with mild cognitive impairment and dementia due to Lewy...
Neuropsychological and neuroanatomical underpinnings of the face pareidolia errors on the noise pareidolia test in patients with mild cognitive impairment and dementia due to Lewy bodies.
OBJECTIVE
Prior research on the Noise Pareidolia Test (NPT) has demonstrated its clinical utility in detecting patients with mild cognitive impairment and dementia due to Lewy Body Disease (LBD). However, few studies to date have investigated the neuropsychological factors underlying pareidolia errors on the NPT across the clinical spectrum of LBD. Furthermore, to our knowledge, no research has examined the relationship between cortical thickness using MRI data and NPT subscores. As such, this study sought to explore the neuropsychological and neuroanatomical factors influencing performance on the NPT utilizing the National Alzheimer's Coordinating Center Lewy Body Dementia Module.
METHODS
Our sample included participants with normal cognition (NC; = 56), LBD with mild cognitive impairment (LBD-MCI; = 97), and LBD with dementia (LBD-Dementia; = 94). Archival data from NACC were retrospectively analyzed for group differences in neuropsychological test scores and cognitive and psychiatric predictors of NPT scores. Clinicoradiological correlates between NPT subscores and a small subsample of the above LBD participants were also examined.
RESULTS
Analyses revealed significant differences in NPT scores among groups. Regression analysis demonstrated that dementia severity, attention, and visuospatial processing contributed approximately 24% of NPT performance in LBD groups. Clinicoradiological analysis suggests a potential contribution of the right fusiform gyrus, but not the inferior occipital gyrus, to NPT pareidolia error scores.
CONCLUSIONS
Our findings highlight the interplay of attention and visuoperceptual functions in complex pareidolia in LBD. Further investigation is needed to refine the utility of NPT scores in clinical settings, including identifying patients at risk for visual illusions and hallucinations.
PubMed: 38949538
DOI: 10.1080/13803395.2024.2372876 -
American Journal of Epidemiology Apr 2024Dementia incidence is lower among Asian Americans than Whites, despite higher prevalence of type 2 diabetes, a well-known dementia risk factor. Determinants of dementia,...
Dementia incidence is lower among Asian Americans than Whites, despite higher prevalence of type 2 diabetes, a well-known dementia risk factor. Determinants of dementia, including type 2 diabetes, have rarely been studied in Asian Americans. We followed 4,846 Chinese, 4,129 Filipino, 2,784 Japanese, 820 South Asian, and 123,360 non-Latino White members of a California-based integrated healthcare delivery system from 2002-2020. We estimated dementia incidence rates by race/ethnicity and type 2 diabetes status, and fit Cox proportional hazards and Aalen additive hazards models for the effect of type 2 diabetes (assessed 5 years before baseline) on age of dementia diagnosis controlling for sex/gender, educational attainment, nativity, height, race/ethnicity, and a race/ethnicity*diabetes interaction. Type 2 diabetes was associated with higher dementia incidence in Whites (hazard ratio [HR] 1.46, 95% confidence interval [CI] 1.40-1.52). Compared with Whites, the estimated effect of diabetes was larger in South Asians (2.26 [1.48-3.44]), slightly smaller in Chinese (1.32 [1.08-1.62]) and Filipino (1.31 [1.08-1.60]), and similar in Japanese (1.44 [1.15-1.81]) individuals. Heterogeneity in this association across Asian subgroups may be related to type 2 diabetes severity. Understanding this heterogeneity may inform prevention strategies to prevent dementia for all racial and ethnic groups.
PubMed: 38949483
DOI: 10.1093/aje/kwae051 -
Journal of Neuropsychology Jul 2024Recent research suggests that the retrieval of autobiographical memories among cognitively healthy middle-aged and older adults is sensitive to the Apolipoprotein E ε4...
Recent research suggests that the retrieval of autobiographical memories among cognitively healthy middle-aged and older adults is sensitive to the Apolipoprotein E ε4 (APOE4) allele, a genetic marker that increases the risk of Alzheimer's disease (AD) dementia. However, whether the APOE4-associated alteration in autobiographical memory retrieval encompasses rapid (i.e. direct retrieval) or iterative (i.e. generative retrieval) processes remains unclear. In the present study, 39 APOE4 carriers and 45 non-carriers (ages 60-80) who scored within normal limits on neuropsychological testing were cued to generate specific autobiographical events. We examined group differences in direct and generative retrieval and correlated direct and generative retrieval rates with performance on neuropsychological tests. Direct retrieval rates were lower in the APOE4 carriers compared to non-carriers. Episodic memory positively correlated with direct retrieval rates across the sample, though this relationship became non-significant when factoring in age and sex. There were no significant findings related to successful generative retrieval rates and its efficiency. In summary, compared to non-carriers, cognitively unimpaired middle-aged to older adult APOE4 carriers demonstrated greater difficulty, rapidly reconstructing specific autobiographical events without the support of semantic memory, suggesting that early autobiographical memory retrieval processes demonstrate vulnerability to AD-related risk factors.
PubMed: 38949213
DOI: 10.1111/jnp.12380 -
Neurodegenerative Disease Management Jun 2024
PubMed: 38949171
DOI: 10.1080/17582024.2024.2343539 -
JPMA. the Journal of the Pakistan... Jun 2024
Topics: Humans; Alzheimer Disease; Pakistan
PubMed: 38949013
DOI: 10.47391/JPMA.10703 -
Alzheimer's & Dementia : the Journal of... Jul 2024Although reproductive hormones are implicated in cerebral small vessel disease in women, few studies consider measured hormones in relation to white matter...
INTRODUCTION
Although reproductive hormones are implicated in cerebral small vessel disease in women, few studies consider measured hormones in relation to white matter hyperintensity volume (WMHV), a key indicator of cerebral small vessel disease. Even fewer studies consider estrone (E1), the primary postmenopausal estrogen, or follicle-stimulating hormone (FSH), an indicator of ovarian age. We tested associations of estradiol (E2), E1, and FSH to WMHV among women.
METHODS
Two hundred twenty-two women (mean age = 59) underwent hormone assays (E1, E2, FSH) and 3T brain magnetic resonance imaging. Associations of hormones to WMHV were tested with linear regression.
RESULTS
Higher E2 (B[standard error (SE)] = -0.17[0.06], P = 0.008) and E1 (B[SE] = -0.26[0.10], P = 0.007) were associated with lower whole-brain WMHV, and higher FSH (B[SE] = 0.26[0.07], P = 0.0005) with greater WMHV (covariates age, race, education). When additionally controlling for cardiovascular disease risk factors, associations of E1 and FSH to WMHV remained.
DISCUSSION
Reproductive hormones, particularly E1 and FSH, are important to women's cerebrovascular health.
HIGHLIGHTS
Despite widespread belief that sex hormones are important to women's brain health, little work has considered how these hormones in women relate to white matter hyperintensities (WMH), a major indicator of cerebral small vessel disease. We considered relations of estradiol (E2), estrone (E1), and follicle-stimulating hormone (FSH) to WMH in midlife women. Higher E2 and E1 were associated with lower whole-brain WMH volume (WMHV), and higher FSH with higher whole-brain WMHV. Associations of E1 and FSH, but not E2, to WMHV persisted with adjustment for cardiovascular disease risk factors. Findings underscore the importance of E2 and FSH to women's cerebrovascular health.
PubMed: 38948946
DOI: 10.1002/alz.14093 -
Frontiers in Neuroscience 2024Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke,...
INTRODUCTION
Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
METHODS
This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
RESULTS
A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
CONCLUSION
Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.
PubMed: 38948927
DOI: 10.3389/fnins.2024.1401329