Diabetes-related clinical and microstructural white matter changes in patients with Alzheimer's disease.

AIM: Although there exists substantial epidemiological evidence indicating an elevated risk of dementia in individuals with diabetes, our understanding of the neuropathological underpinnings of the association between Type-2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) remains unclear. This study aims to unveil the microstructural brain changes associated with T2DM in AD and identify the clinical variables contributing to these changes. METHODS: In this retrospective study involving 64 patients with AD, 31 individuals had concurrent T2DM. The study involved a comparative analysis of diffusion tensor imaging (DTI) images and clinical features between patients with and without T2DM. The FSL FMRIB software library was used for comprehensive preprocessing and tractography analysis of DTI data. After eddy current correction, the "bedpost" model was utilized to model diffusion parameters. Linear regression analysis with a stepwise method was used to predict the clinical variables that could lead to microstructural white matter changes. RESULTS: We observed a significant impairment in the left superior longitudinal fasciculus (SLF) among patients with AD who also had T2DM. This impairment in patients with AD and T2DM was associated with an elevation in creatine levels. CONCLUSION: The white matter microstructure in the left SLF appears to be sensitive to the impairment of kidney function associated with T2DM in patients with AD. The emergence of AD in association with T2DM may be driven by mechanisms distinct from the typical AD pathology. Compromised renal function in AD could potentially contribute to impaired white matter integrity.

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

BACKGROUND: The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS: We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS: The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION: Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.

Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several prevalent explainability methods such as gradient-weighted class activation mapping (Grad-CAM) to an fMRI-based 3D-VGG16 network for AD diagnosis to improve the model's explainability. The aim is to explore the specific Region of Interest (ROI) of brain the model primarily focuses on when making predictions, as well as whether there are differences in these ROIs between AD and normal controls (NCs). First, we utilized multiple resting-state functional activity maps including ALFF, fALFF, ReHo, and VMHC to reduce the complexity of fMRI data, which differed from many studies that utilized raw fMRI data. Compared to methods utilizing raw fMRI data, this manual feature extraction approach may potentially alleviate the model's burden. Subsequently, 3D-VGG16 were employed for AD classification, where the final fully connected layers were replaced with a Global Average Pooling (GAP) layer, aimed at mitigating overfitting while preserving spatial information within the feature maps. The model achieved a maximum of 96.4% accuracy on the test set. Finally, several 3D CAM methods were employed to interpret the models. In the explainability results of the models with relatively high accuracy, the highlighted ROIs were primarily located in the precuneus and the hippocampus for AD subjects, while the models focused on the entire brain for NC. This supports current research on ROIs involved in AD. We believe that explaining deep learning models would not only provide support for existing research on brain disorders, but also offer important referential recommendations for the study of currently unknown etiologies.

Locus coeruleus integrity is related to an exploitation-based decision-making bias in older adulthood.

Optimal decision-making balances exploration for new information against exploitation of known rewards, a process mediated by the locus coeruleus and its norepinephrine projections. We predicted that an exploitation-bias that emerges in older adulthood would be associated with lower microstructural integrity of the locus coeruleus. Leveraging in vivo histological methods from quantitative MRI-magnetic transfer saturation-we provide evidence that older age is associated with lower locus coeruleus integrity. Critically, we demonstrate that an exploitation bias in older adulthood, assessed with a foraging task, is sensitive and specific to lower locus coeruleus integrity. Because the locus coeruleus is uniquely vulnerable to Alzheimer's disease pathology, our findings suggest that aging, and a presymptomatic trajectory of Alzheimer's related decline, may fundamentally alter decision-making abilities in later life.

Irregular word reading as a marker of semantic decline in Alzheimer's disease: implications for premorbid intellectual ability measurement.

BACKGROUND: Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities. METHOD: Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory. RESULTS: EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. CONCLUSIONS: While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction.

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (ß-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly.

BACKGROUND: Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß (Aß) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to (i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and (ii) to determine how different cerebral pathologies (i.e., Aß deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample. METHODS: Sixty APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60 to 75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aß-PET ([11C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [11C]PiB standardized uptake value ratio was calculated for regions typical for Aß accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex). RESULTS: Serum NfL concentration was increased in APOE ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in APOE ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aß pathology. Plasma t-tau concentration did not associate with any of the measured pathologies. CONCLUSIONS: Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.

Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

APOE4 homozygozity represents a distinct genetic form of Alzheimer's disease.

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer's disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer's Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

Neuroticism polygenic risk predicts conversion from mild cognitive impairment to Alzheimer's disease by impairing inferior parietal surface area.

The high prevalence of conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) makes early prevention of AD extremely critical. Neuroticism, a heritable personality trait associated with mental health, has been considered a risk factor for conversion from aMCI to AD. However, whether the neuroticism genetic risk could predict the conversion of aMCI and its underlying neural mechanisms is unclear. Neuroticism polygenic risk score (N-PRS) was calculated in 278 aMCI patients with qualified genomic and neuroimaging data from ADNI. After 1-year follow-up, N-PRS in patients of aMCI-converted group was significantly greater than those in aMCI-stable group. Logistic and Cox survival regression revealed that N-PRS could significantly predict the early-stage conversion risk from aMCI to AD. These results were well replicated in an internal dataset and an independent external dataset of 933 aMCI patients from the UK Biobank. One sample Mendelian randomization analyses confirmed a potentially causal association from higher N-PRS to lower inferior parietal surface area to higher conversion risk of aMCI patients. These analyses indicated that neuroticism genetic risk may increase the conversion risk from aMCI to AD by impairing the inferior parietal structure.

The Associations Between Serum Insulin-like Growth Factor-I, Brain White Matter Volumes, and Cognition in Mild Cognitive Impairment and Alzheimer's Disease.

Background: Insulin-like growth factor-I (IGF-I) regulates myelin, but little is known whether IGF-I associates with white matter functions in subjective and objective mild cognitive impairment (SCI/MCI) or Alzheimer's disease (AD). Objective: To explore whether serum IGF-I is associated with magnetic resonance imaging - estimated brain white matter volumes or cognitive functions. Methods: In a prospective study of SCI/MCI (n = 106) and AD (n = 59), we evaluated the volumes of the total white matter, corpus callosum (CC), and white matter hyperintensities (WMHs) as well as Mini-Mental State Examination (MMSE), Trail Making Test A and B (TMT-A/B), and Stroop tests I-III at baseline, and after 2 years. Results: IGF-I was comparable in SCI/MCI and AD (113 versus 118 ng/mL, p = 0.44). In SCI/MCI patients, the correlations between higher baseline IGF-I and greater baseline and 2-year volumes of the total white matter and total CC lost statistical significance after adjustment for intracranial volume and other covariates. However, after adjustment for covariates, higher baseline IGF-I correlated with better baseline scores of MMSE and Stroop test II in SCI/MCI and with better baseline results of TMT-B and Stroop test I in AD. IGF-I did not correlate with WMH volumes or changes in any of the variables. Conclusions: Both in SCI/MCI and AD, higher IGF-I was associated with better attention/executive functions at baseline after adjustment for covariates. Furthermore, the baseline associations between IGF-I and neuropsychological test results in AD may argue against significant IGF-I resistance in the AD brain.

[Assessment of the significance of dilated perivascular spaces and nocture arterial hypertension in the development of Alzheimer's disease]. / Otsenka znacheniya rasshirennykh perivaskulyarnykh prostranstv i nochnoi arterial'noi gipertenzii v razvitii bolezni Al'tsgeimera.

OBJECTIVE: To study the severity and localization of dilated perivascular spaces (DPVS), the levels of protein markers of amyloidosis and neurodegeneration in the cerebrospinal fluid (CSF) at different daily blood pressure (BP) profiles in patients with Alzheimer's disease (AD) and other types of cognitive impairment. MATERIAL AND METHODS: A total of 119 people, aged 53 to 92 years, including 55 patients with AD, 27 patients with vascular cognitive disorders (VCD), 19 patients with frontotemporal degeneration (FTD). All patients underwent BP monitoring for 24 hours using a standard oscillometric measurement method, lumbar puncture to assess Aß-42 and Aß-40 amyloid protein, total and phosphorylated tau protein in the CSF, magnetic resonance imaging tomography of the brain with subsequent assessment of the severity of expansion and localization of DPVS according to the G.M. Potter scale. RESULTS: In 58.3% of patients with AD, there is no adequate reduction in BP at night in comparison with patients with VCD (p<0.05). A significant degree of expansion of the DPVS turned out to be most typical for patients with AD: grade 3 was detected in 45.7% of patients, and the maximum, grade 4, was detected in 13.4%. At the same time, DPVSs were significantly more often detected in the group of subjects with insufficient reduction in diastolic BP (DBP) at night. A strong inverse correlation was established between the level of Aß-42 in the CSF and the variability of DBP at night (r= -0.92; p<0.05). The decrease in the level of Aß-42 in AD, especially at the prodromal stage, is directly related to the low variability of DBP at night, which is more characteristic of an insufficient decrease or increase in BP during night sleep. CONCLUSION: Patients with AD were characterized by an insufficient decrease in BP at night, which is associated with the severity and degree of maximum expansion of the DPVS. A decrease in the level of Aß-42 amyloid protein in the CSF strongly correlates with the variability of DBP at night.

[Comparative characteristics of neuropsychological and neurometabolic changes in patients with Alzheimer's disease and vascular cognitive impairment]. / Sravnitel'naya kharakteristika neiropsikhologicheskikh i neirometabolicheskikh pokazatelei u patsientov s bolezn'yu Al'tsgeimera i sosudistymi kognitivnymi rasstroistvami.

OBJECTIVE: To investigate the pattern and connections of neuropsychological and metabolic indices in patients with cognitive disorders of Alzheimer's and vascular (subcortical-cortical) types of different severity. MATERIAL AND METHODS: A total of 177 patients were examined, including 85 patients with Alzheimer's disease (AD) and 92 patients with vascular cognitive impairment (VCI). All patients underwent complex neuropsychological examination; 18F-FDG PET was performed in 17 patients with AD and 15 patients with VCI. RESULTS: The greatest changes in patients with AD were noted in the mnestic sphere, and the indicators significantly differed from the results of the study of patients with VCI already at the pre-dementia stage. Neurodynamic and dysregulatory disorders prevailed in patients with VCI. Patients with AD showed bilateral symmetrical reduction of metabolic activity in the cortex of parietal and temporal lobes, often in combination with marked hypometabolism in the hippocampal region. In patients with VCI, there were areas of decreased brain tissue metabolism of different localization and size, mainly in the projection of the basal ganglia and in the prefrontal and parietal cortex, as well as in the cingulate gyrus, which indirectly confirms the mechanism of disconnection of subcortical and cortical structures. In AD, impaired metabolic activity in the hippocampal region correlated with impaired temporal and spatial orientation (ρ=-0.54, p<0.05), memory impairment (ρ=-0.71, p<0.005). Hypometabolism of the parietal lobe cortex was associated with total MMSE score (ρ=-0.8, p<0.001), 10-word test (ρ=-0.89, p<0.001 and ρ=-0.82, p<0.001), visual-spatial impairment (ρ=-0.64, p<0.01), categorical association test (ρ=-0.73, p<0.005). In patients with VCI, dysregulatory disorders correlated with hypometabolism in the thalamic projection (ρ=-0.56, p<0.05), prefrontal cortex (ρ=-0.64, p<0.05) and in the cingulate gyrus (anterior regions) (ρ=-0.53, p<0.05). CONCLUSION: The results indicate the presence of differences in cognitive impairment and cerebral metabolism in patients with AD and VCI.

[Features of the clinical and neuroimaging picture in patients with early-onset Alzheimer's disease]. / Osobennosti klinicheskoi i neirovizualizatsionnoi kartiny u patsientov s bolezn'yu Al'tsgeimera s rannim nachalom.

The most common cause of severe cognitive impairment in adults is Alzheimer's disease (AD). Depending on the age of onset, AD is divided into early (<65 years) and late (≥65 years) forms. Early-onset AD (EOAD) is significantly less common than later-onset AD (LOAD) and accounts for only about 5-10% of cases. However, its medical and social significance, as a disease leading to loss of ability to work and legal capacity, as well as premature death in patients aged 40-64 years, is extremely high. Patients with EOAD compared with LOAD have a greater number of atypical clinical variants - 25% and 6-12.5%, respectively, which complicates the differential diagnosis of EOAD with other neurodegenerative diseases. However, the typical classical amnestic variant predominates in both EOAD and LOAD. Also, patients with EOAD have peculiarities according to neuroimaging data: when performing MRI of the brain, patients with EOAD often have more pronounced parietal atrophy and less pronounced hippocampal atrophy compared to patients with LOAD. The article pays attention to the features of the clinical and neuroimaging data in patients with EOAD; a case of a patient with EOAD is presented.

Association between retinal microvascular abnormalities and late-life brain amyloid-ß deposition: the ARIC-PET study.

BACKGROUND: Retinal microvascular signs are accessible measures of early alterations in microvascular dysregulation and have been associated with dementia; it is unclear if they are associated with AD (Alzheimer's disease) pathogenesis as a potential mechanistic link. This study aimed to test the association of retinal microvascular abnormalities in mid and late life and late life cerebral amyloid. METHODS: Participants from the ARIC-PET (Atherosclerosis Risk in Communities-Positron Emission Tomography) study with a valid retinal measure (N = 285) were included. The associations of mid- and late-life retinal signs with late-life amyloid-ß (Aß) by florbetapir PET were tested. Two different measures of Aß burden were included: (1) elevated amyloid (SUVR > 1.2) and (2) continuous amyloid SUVR. The retinal measures' association with Aß burden was assessed using logistic and robust linear regression models. A newly created retinal score, incorporating multiple markers of retinal abnormalities, was also evaluated in association with greater Aß burden. RESULTS: Retinopathy in midlife (OR (95% CI) = 0.36 (0.08, 1.40)) was not significantly associated with elevated amyloid burden. In late life, retinopathy was associated with increased continuous amyloid standardized value uptake ratio (SUVR) (ß (95%CI) = 0.16 (0.02, 0.32)) but not elevated amyloid burden (OR (95%CI) = 2.37 (0.66, 9.88)) when accounting for demographic, genetic and clinical risk factors. A high retinal score in late life, indicating a higher burden of retinal abnormalities, was also significantly associated with increased continuous amyloid SUVR (ß (95% CI) = 0.16 (0.04, 0.32)) independent of vascular risk factors. CONCLUSIONS: Retinopathy in late life may be an easily obtainable marker to help evaluate the mechanistic vascular pathway between retinal measures and dementia, perhaps acting via AD pathogenesis. Well-powered future studies with a greater number of retinal features and other microvascular signs are needed to test these findings.

Predicting Alzheimer's progression in MCI: a DTI-based white matter network model.

OBJECTIVE: This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients. METHODS: This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation. RESULTS: APOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers. CONCLUSIONS: A comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Combined in vivo MRI assessment of locus coeruleus and nucleus basalis of Meynert integrity in amnestic Alzheimer's disease, suspected-LATE and frontotemporal dementia.

BACKGROUND: The locus coeruleus (LC) and the nucleus basalis of Meynert (NBM) are altered in early stages of Alzheimer's disease (AD). Little is known about LC and NBM alteration in limbic-predominant age-related TDP-43 encephalopathy (LATE) and frontotemporal dementia (FTD). The aim of the present study is to investigate in vivo LC and NBM integrity in patients with suspected-LATE, early-amnestic AD and FTD in comparison with controls. METHODS: Seventy-two participants (23 early amnestic-AD patients, 17 suspected-LATE, 17 FTD patients, defined by a clinical-biological diagnosis reinforced by amyloid and tau PET imaging, and 15 controls) underwent neuropsychological assessment and 3T brain MRI. We analyzed the locus coeruleus signal intensity (LC-I) and the NBM volume as well as their relation with cognition and with medial temporal/cortical atrophy. RESULTS: We found significantly lower LC-I and NBM volume in amnestic-AD and suspected-LATE in comparison with controls. In FTD, we also observed lower NBM volume but a slightly less marked alteration of the LC-I, independently of the temporal or frontal phenotype. NBM volume was correlated with the global cognitive efficiency in AD patients. Strong correlations were found between NBM volume and that of medial temporal structures, particularly the amygdala in both AD and FTD patients. CONCLUSIONS: The alteration of LC and NBM in amnestic-AD, presumed-LATE and FTD suggests a common vulnerability of these structures to different proteinopathies. Targeting the noradrenergic and cholinergic systems could be effective therapeutic strategies in LATE and FTD.

Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease.

Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (ß = .837, r = 0.472, p < .001) and glucose covariance (ß = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.