Neuroimaging in Dementia: More than Typical Alzheimer Disease

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into detailed perceptual mappings, thereby enriching our grasp of brain pathophysiology. To capitalize on the strengths of PET/MR imaging in diagnosing AD, we have introduced a novel deep learning framework named "IT", which is inspired by the Transformer architecture. This innovative model adeptly captures both local and global characteristics within the imaging data, refining these features through advanced feature engineering techniques to achieve a synergistic integration. The efficiency of our model is underscored by robust experimental validation, wherein it delivers superior performance on a host of evaluative benchmarks, all while maintaining low demands on computational resources. Furthermore, the features we extracted resonate with established medical theories regarding feature distribution and usage efficiency, enhancing the clinical relevance of our findings. These insights significantly bolster the arsenal of tools available for AD diagnostics and contribute to the broader narrative of deciphering brain functionality through state-of-the-art imaging modalities.

MRI for diagnosing dementia - update 2025

Magnetic resonance imaging (MRI) plays a crucial role alongside clinical and neuropsychological assessments in diagnosing dementia. The recent and ongoing advancements in MRI technology have significantly enhanced the detection and characterization of the specific neurostructural changes seen in various neurodegenerative diseases, thereby significantly increasing the precision of diagnosis. Within this context of perpetual evolution, this review article explores the recent advances in MRI with regard to diagnosing dementia.A retrospective literature review was conducted by searching the PubMed and ScienceDirect databases for the keywords "dementia", "imaging", and "MRI". The inclusion criteria were scientific papers in English that revolved around the role of MRI as a diagnostic tool in the field of dementia. A specific time frame was not determined but the focus was on current articles, with an overall of 20 articles dating from the last 6 years (after 2018), corresponding to 55% of the total number of articles.This review provides a comprehensive overview of the latest advances in the radiologic diagnosis of dementia using MRI, with a particular focus on the last 6 years. Technical aspects of image acquisition for clinical and research purposes are discussed. MRI findings typical of dementia are described. The findings are divided into non-specific findings of dementia and characteristic findings for certain dementia subtypes. This provides information about possible causes of dementia. In addition, developed scoring systems that support MRI findings are presented, including the MTA score for Alzheimer's disease with corresponding illustrative figures.The symbiosis of clinical evaluation with high-field MRI methodologies enhances dementia diagnosis and offers a holistic and nuanced understanding of structural brain changes associated with dementia and its various subtypes. The latest advances, mainly involving the emergence of ultra-high-field (7T) MRI, despite having limited use in clinical practice, mark a pragmatic shift in the field of research. · High-field MRI (3T) and specialized sequences allow for the detection of early structural changes indicative of dementia.. · Characteristic neuroanatomical MRI patterns enable the differentiation between various subtypes of dementia.. · Established scales provide added value to the quantification and categorization of MRI findings in dementia.. · Akl E, Dyrba M, Görß D et al. MRI for diagnosing dementia - update 2024. Rofo 2025; DOI 10.1055/a-2563-0725.

"Fill States": PET-derived Markers of the Spatial Extent of Alzheimer Disease Pathology

Background Alzheimer disease (AD) progression can be monitored by tracking intensity changes in PET standardized uptake value (SUV) ratios of amyloid, tau, and neurodegeneration. The spatial extent ("fill state") of these three hallmark pathologic abnormalities may serve as critical pathophysiologic information, pending further investigation. Purpose To examine the clinical utility and increase the accessibility of PET-derived fill states. Materials and Methods This secondary analysis of two prospective studies used data from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Tau Propagation over Time study (T-POT). Each cohort comprised amyloid-negative cognitively normal individuals (controls) and patients with subjective cognitive decline, mild cognitive impairment, or probable-AD dementia. Fill states of amyloid, tau, and neurodegeneration were computed as the percentages of significantly abnormal voxels relative to controls across PET scans. Fill states and SUV ratios were compared across stages (Kruskal-Wallis H test, area under the receiver operating characteristic curve analysis) and tested for association with the severity of cognitive impairment (Spearman correlation, multivariate regression analysis). Additionally, a convolutional neural network (CNN) was developed to estimate fill states from patients' PET scans without requiring controls. Results The ADNI cohort included 324 individuals (mean age, 72 years ± 6.8 [SD]; 173 [53%] female), and the T-POT cohort comprised 99 individuals (mean age, 66 years ± 8.7; 63 [64%] female). Higher fill states were associated with higher stages of cognitive impairment (P < .001), and tau and neurodegeneration fill states showed higher diagnostic performance for cognitive impairment compared with SUV ratio (P < .05) across cohorts. Similarly, all fill states were negatively correlated with cognitive performance (P < .001) and uniquely characterized the degree of cognitive impairment even after adjustment for SUV ratio (P < .05). The CNN estimated amyloid and tau accurately, but not neurodegeneration fill states. Conclusion Fill states provided reliable markers of AD progression, potentially improving early detection, staging, and monitoring of AD in clinical practice and trials beyond SUV ratio. Clinical trial registration no. NCT00106899 © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Yun and Kim in this issue.

Impact of Helicobacter pylori eradication on age-specific risk of incident dementia in patients with peptic ulcer disease: a nationwide population-based cohort study

The impact of peptic ulcer disease (PUD) and Helicobacter pylori (H. pylori) eradication therapy on dementia risk in high H. pylori prevalence populations remains uncertain. This study investigates the relationship between PUD, H. pylori eradication, and dementia risk, including Alzheimer's disease (AD), in an elderly South Korean cohort, considering age and eradication timing. Data from the Korean National Health Insurance Service (2002-2015) for individuals aged 55-79 were analyzed. Participants were divided based on PUD and H. pylori therapy status. Propensity score matching was used to evaluate dementia incidence and hazard ratios over 5 and 10 years, alongside the timing of eradication therapy. PUD is linked to higher dementia risk at 5 and 10 years, more for overall dementia than AD, with eradication status not significantly altering the risk. Age-specific analysis showed increased AD risk in the 60s and 70s age groups. Late eradication therapy is correlated with a higher dementia risk. PUD is a risk factor for dementia in elderly South Koreans, particularly with delayed H. pylori therapy. The findings emphasize timely H. pylori management and its potential role in neurodegenerative disease prevention.

The mechanisms, hallmarks, and therapies for brain aging and age-related dementia

Age-related cognitive decline and dementia are significant manifestations of brain aging. As the elderly population grows rapidly, the health and socio-economic impacts of cognitive dysfunction have become increasingly significant. Although clinical treatment of dementia has faced considerable challenges over the past few decades, with limited breakthroughs in slowing its progression, there has been substantial progress in understanding the molecular mechanisms and hallmarks of age-related dementia (ARD). This progress brings new hope for the intervention and treatment of this disease. In this review, we categorize the latest findings in ARD biomarkers into four stages based on disease progression: Healthy brain, pre-clinical, mild cognitive impairment, and dementia. We then systematically summarize the most promising therapeutic approaches to prevent or slow ARD at four levels: Genome and epigenome, organelle, cell, and organ and organism. We emphasize the importance of early prevention and detection, along with the implementation of combined treatments as multimodal intervention strategies, to address brain aging and ARD in the future.

Educational disparities in brain health and dementia across Latin America and the United States

BACKGROUND

Education influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown.

METHODS

A total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type.

RESULTS

Education influenced brain measures, explaining 24%-98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions.

DISCUSSION

The results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research.

HIGHLIGHTS

Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.

Temporo-frontoparietal hypoconnectivity as a biomarker for isolated language impairment in mild cognitive impairment: A cross-cohort comparison

INTRODUCTION

Whether brain functional connectivity (FC) is consistently disrupted in individuals with mild cognitive impairment (MCI) with isolated language impairment (ilMCI), and its potential to differentiate between MCI subtypes remains uncertain.

METHODS

Cross-sectional data from 404 participants in two cohorts (the Chinese Preclinical Alzheimer's Disease Study and the Alzheimer's Disease Neuroimaging Initiative) were analyzed, including neuropsychological tests, resting-state functional magnetic resonance imaging (fMRI), cerebral amyloid positivity, and apolipoprotein E (APOE) status.

RESULTS

Temporo-frontoparietal FC, particularly between the bilateral superior temporal pole and the left inferior frontal/supramarginal gyri, was consistently decreased in ilMCI compared to amnestic MCI (aMCI) and normal controls, which was correlated with semantic impairment. Using mean temporo-frontoparietal FC as a classifier could improve accuracy in identifying ilMCI subgroups with positive cerebral amyloid deposition and APOE risk alleles.

DISCUSSION

Temporal-frontoparietal hypoconnectivity was observed in individuals with ilMCI, which may reflect semantic impairment and serve as a valuable biomarker to indicate potential mechanisms of underlying neuropathology.

HIGHLIGHTS

Temporo-frontoparietal hypoconnectivity was observed in impaired language mild cognitive impairment (ilMCI). Temporo-frontoparietal hypoconnectivity may reflect semantic impairment. Temporo-frontoparietal functional connectivity can classify ilMCI subtypes.

Neurochemical organization of cortical proteinopathy and neurophysiology along the Alzheimer's disease continuum

INTRODUCTION

Despite parallel research indicating amyloid-β accumulation, alterations in cortical neurophysiological signaling, and multi-system neurotransmitter disruptions in Alzheimer's disease (AD), the relationships between these phenomena remains unclear.

METHODS

Using magnetoencephalography, positron emission tomography, and an atlas of 19 neurotransmitters, we studied the alignment between neurophysiological alterations, amyloid-β deposition, and the neurochemical gradients of the cortex.

RESULTS

In patients with mild cognitive impairment and AD, changes in cortical rhythms were topographically aligned with cholinergic, serotonergic, and dopaminergic systems. These alignments correlated with the severity of clinical impairments. Additionally, cortical amyloid-β plaques were preferentially deposited along neurochemical boundaries, influencing how neurophysiological alterations align with muscarinic acetylcholine receptors. Most of the amyloid-β-neurochemical and alpha-band neuro-physio-chemical alignments replicated in an independent dataset of individuals with asymptomatic amyloid-β accumulation.

DISCUSSION

Our findings demonstrate that AD pathology aligns topographically with the cortical distribution of chemical neuromodulator systems and scales with clinical severity, with implications for potential pharmacotherapeutic pathways.

HIGHLIGHTS

Changes in cortical rhythms in Alzheimer's are organized along neurochemical boundaries. The strength of these alignments is related to clinical symptom severity. Deposition of amyloid-β (Aβ) is aligned with similar neurotransmitter systems. Aβ deposition mediates the alignment of beta rhythms with cholinergic systems. Most alignments replicate in participants with pre-clinical Alzheimer's pathology.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

INTRODUCTION

Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images.

METHODS

1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC).

RESULTS

Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did.

CONCLUSIONS

The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Genetic and Epigenetic Biomarkers Linking Alzheimer's Disease and Age-Related Macular Degeneration

Alzheimer's disease (AD) represents a prominent neurodegenerative disorder (NDD), accounting for the majority of dementia cases worldwide. In addition to memory deficits, individuals with AD also experience alterations in the visual system. As the retina is an extension of the central nervous system (CNS), the loss in retinal ganglion cells manifests clinically as decreased visual acuity, narrowed visual field, and reduced contrast sensitivity. Among the extensively studied retinal disorders, age-related macular degeneration (AMD) shares numerous aging processes and risk factors with NDDs such as cognitive impairment that occurs in AD. Histopathological investigations have revealed similarities in pathological deposits found in the retina and brain of patients with AD and AMD. Cellular aging processes demonstrate similar associations with organelles and signaling pathways in retinal and brain tissues. Despite these similarities, there are distinct genetic backgrounds underlying these diseases. This review comprehensively explores the genetic similarities and differences between AMD and AD. The purpose of this review is to discuss the parallels and differences between AMD and AD in terms of pathophysiology, genetics, and epigenetics.

Assessment of neurovascular uncoupling: APOE status is a key driver of early metabolic and vascular dysfunction

BACKGROUND

Alzheimer's disease (AD) is the most common cause of dementia worldwide, with apolipoprotein Eε4 (APOEε4) being the strongest genetic risk factor. Current clinical diagnostic imaging focuses on amyloid and tau; however, new methods are needed for earlier detection.

METHODS

PET imaging was used to assess metabolism-perfusion in both sexes of aging C57BL/6J, and hAPOE mice, and were verified by transcriptomics, and immunopathology.

RESULTS

All hAPOE strains showed AD phenotype progression by 8 months, with females exhibiting the regional changes, which correlated with GO-term enrichments for glucose metabolism, perfusion, and immunity. Uncoupling analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (↓ glucose uptake, ↑ perfusion) at 8 and 12 months, while APOEε3/ε4 demonstrated Type-2 uncoupling (↑ glucose uptake, ↓ perfusion), while immunopathology confirmed cell specific contributions.

DISCUSSION

This work highlights APOEε4 status in AD progression manifests as neurovascular uncoupling driven by immunological activation, and may serve as an early diagnostic biomarker.

HIGHLIGHTS

We developed a novel analytical method to analyze PET imaging of 18F-FDG and 64Cu-PTSM data in both sexes of aging C57BL/6J, and hAPOEε3/ε3, hAPOEε4/ε4, and hAPOEε3/ε4 mice to assess metabolism-perfusion profiles termed neurovascular uncoupling. This analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (decreased glucose uptake, increased perfusion) at 8 and 12 months, while APOEε3/ε4 demonstrated significant Type-2 uncoupling (increased glucose uptake, decreased perfusion) by 8 months which aligns with immunopathology and transcriptomic signatures. This work highlights that there may be different mechanisms underlying age related changes in APOEε4/ε4 compared with APOEε3/ε4. We predict that these changes may be driven by immunological activation and response, and may serve as an early diagnostic biomarker.

Data-driven normative values based on generative manifold learning for quantitative MRI

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approach to personalized normative values for each brain structure using an unsupervised Artificial Intelligence technique known as generative manifold learning. We test the potential benefit of these personalized normative values in comparison with the more traditional average normative values on a population of patients with drug-resistant epilepsy operated for focal cortical dysplasia, as well as on a supplementary healthy group and on patients with Alzheimer's disease.

Assessment of Neurovascular Uncoupling: APOE Status is a Key Driver of Early Metabolic and Vascular Dysfunction

BACKGROUND

Alzheimer's disease (AD) is the most common cause of dementia worldwide, with apolipoprotein ε4 (APOEε4) being the strongest genetic risk factor. Current clinical diagnostic imaging focuses on amyloid and tau; however, new methods are needed for earlier detection.

METHODS

PET imaging was used to assess metabolism-perfusion in both sexes of aging C57BL/6J, and hAPOE mice, and were verified by transcriptomics, and immunopathology.

RESULTS

All hAPOE strains showed AD phenotype progression by 8 mo, with females exhibiting the regional changes, which correlated with GO-term enrichments for glucose metabolism, perfusion, and immunity. Uncoupling analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (↓ glucose uptake, ↑ perfusion) at 8 and 12 mo, while APOEε3/ε4 demonstrated Type-2 uncoupling (↑ glucose uptake, ↓ perfusion), while immunopathology confirmed cell specific contributions.

DISCUSSION

This work highlights APOEε4 status in AD progression manifest as neurovascular uncoupling driven by immunological activation, and may serve as an early diagnostic biomarker.