Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns.

Importance: Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia. Objective: To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes. Design, Setting, and Participants: Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited. Main Outcomes and Measures: A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined. Results: Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: ß = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI. Conclusions and Relevance: These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on ß-amyloid (Aß) positive patients with amnestic mild cognitive impairment (aMCI). METHODS: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aß-negative = 220; SCD, Aß positive and negative = 139; aMCI, Aß-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aß positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Exploring the cognitive assessment potential of MRI-based volumetric hippocampal segmentation in Parkinson's disease.

PURPOSE: To investigate the potential of magnetic resonance imaging (MRI)-based total and segmental hippocampus volume analysis in the assessment of cognitive status in Parkinson's disease (PD). METHODS: We divided participants into three groups Group A-Parkinson patients (Pp) with normal cognitive status (n = 25), Group B-Pp with dementia (n = 17), and Group C-healthy controls (n = 37). Three-dimensional T1W Fast Spoiled Gradient Recalled Echo images were used for Volbrain hippocampus subfield segmentation. We used the "Winterburn" protocol, which divides the hippocampus into five segments, Cornu Ammonis (CA),CA2/CA3, CA4/dentate gyrus, stratum radiatum, lacunosum, and moleculare, and subiculum. RESULTS: A total of 79 participants were included in the study, consisting of 42 individuals with PD (64.2% male) and 37 healthy controls (54.1% male). The mean age of PD was 60.9 ± 10.7 years and the mean age of control group was 59.27 ± 12.3 years. Significant differences were found in total hippocampal volumes between Group A and B (p = .047. Statistically significant group differences were found in total, right, and left CA1 volumes (analysis of variance [ANOVA]: F(2,76) = 8.098, p = .001; F(2,76) = 7.628, p = .001; F(2,76) = 5.084, p = .008, respectively), as well as in total subiculum volumes (ANOVA: F(2,76) = 4.368, p = .016). Post hoc tests showed that total subiculum volume was significantly lower in individuals with normal cognitive status (0.474 ± 0.116 cm3) compared to healthy controls (0.578 ± 0.151 cm3, p = .013). CONCLUSION: Volumetric hippocampal MRI can be used to assess the cognitive status of Pp. Longitudinal studies that evaluate Pp who progress from normal cognition to dementia are required to establish a causal relationship.

Eye Adult Changes in Thought (Eye ACT) Study: Design and Report on the Inaugural Cohort.

Background: Conflicting research on retinal biomarkers of Alzheimer's disease and related dementias (AD/ADRD) is likely related to limited sample sizes, study design, and protocol differences. Objective: The prospective Eye Adult Changes in Thought (Eye ACT) seeks to address these gaps. Methods: Eye ACT participants are recruited from ACT, an ongoing cohort of dementia-free, older adults followed biennially until AD/ADRD, and undergo visual function and retinal imaging assessment either in clinic or at home. Results: 330 participants were recruited as of 03/2023. Compared to ACT participants not in Eye ACT (N = 1868), Eye ACT participants (N = 330) are younger (mean age: 70.3 versus 71.2, p = 0.014), newer to ACT (median ACT visits since baseline: 3 versus 4, p < 0.001), have more years of education (17.7 versus 16.2, p < 0.001) and had lower rates of visual impairment (12% versus 22%, p < 0.001). Compared to those seen in clinic (N = 300), Eye ACT participants seen at home (N = 30) are older (77.2 versus 74.9, p = 0.015), more frequently female (60% versus 49%, p = 0.026), and have significantly worse visual acuity (71.1 versus 78.9 Early Treatment Diabetic Retinopathy Study letters, p < 0.001) and contrast sensitivity (-1.9 versus -2.1 mean log units at 3 cycles per degree, p = 0.002). Cognitive scores and retinal imaging measurements are similar between the two groups. Conclusions: Participants assessed at home had significantly worse visual function than those seen in clinic. By including these participants, Eye ACT provides a unique longitudinal cohort for evaluating potential retinal biomarkers of dementia.

Small-Vessel Disease and Intracranial Large Artery Disease in Brain MRI Predict Dementia and Acute Coronary Syndrome, Respectively: A Prospective, Observational Study in the Population at High Vascular Risk.

BACKGROUND: We aimed to clarify the predictive value of cerebral small-vessel disease and intracranial large artery disease (LAD) observed in magnetic resonance imaging of the brain and magnetic resonance angiography on future vascular events and cognitive impairment. METHODS AND RESULTS: Data were derived from a Japanese cohort with evidence of cerebral vessel disease on magnetic resonance imaging. This study included 862 participants who underwent magnetic resonance angiography after excluding patients with a modified Rankin Scale score >1 and Mini-Mental State Examination score <24. We evaluated small-vessel disease such as white matter hyperintensities and lacunes in magnetic resonance imaging and LAD with magnetic resonance angiography. Outcomes were incident stroke, dementia, acute coronary syndrome, and all-cause death. Over a median follow-up period of 4.5 years, 54 incident stroke, 39 cases of dementia, and 27 cases of acute coronary syndrome were documented. Both small-vessel disease (white matter hyperintensities and lacunes) and LAD were associated with stroke; however, only white matter hyperintensities were related to dementia. In contrast, only LAD was associated with acute coronary syndrome. Among the 357 patients with no prior history of stroke, coronary or peripheral artery disease, or atrial fibrillation, white matter hyperintensities emerged as the sole predictor of future stroke and dementia, while LAD was the sole predictor of acute coronary syndrome. CONCLUSIONS: Among cerebral vessels, small-vessel disease could underlie the cognitive impairment while LAD was associated with coronary artery disease as atherosclerotic vessel disease.

Association and prediction of Life's Essential 8 score, genetic susceptibility with MCI, dementia, and MRI indices: A prospective cohort study.

BACKGROUND: To examine the associations of Life's Essential 8 (LE8) and its predictive performance with mild cognitive impairment (MCI), dementia and brain MRI indices. METHODS: We used cohort data from UK Biobank. LE8 was categorized into low (<50 score), moderate (50-79 score), and high (≥80 score) levels. Cox regression models considering death as a competing risk were used to estimate the hazard ratios (HRs) and 95%CI on the association between LE8 and MCI and dementia. Multivariable linear regression models were used to analyze LE8 every 10-score increase and brain MRI indices. Area under the curve (AUC) was used to measure the predictive performances of LE8. RESULTS: We included 126,785 participants with a mean (SD) age of 56.0 (8.0) years and 53.5 % were female. The median follow-up was 13.0 years. Compared to individuals with a low LE8 score, those with a high LE8 score were associated with decreased risk of MCI (0.49, 95%CI: 0.40-0.62), all-cause dementia (0.60, 0.44-0.80), vascular dementia (VD, 0.44, 0.21-0.94), and non-Alzheimer non-vascular dementia (NAVD, 0.55, 0.35-0.84). High LE8 score was associated with increased total brain volume, hippocampus volume, grey matter volume, and grey matter in hippocampus volume (p all ≤0.001). LE8 combined age and sex had good performance for predicting all-cause dementia (AUC: 84.1 %), AD (85.4 %), VD (87.6 %), NAVD (81.4 %), and MCI (75.3 %). LIMITATIONS: Our findings only reflect the characteristics of UKB participants. CONCLUSIONS: High LE8 score was associated with reduced risk of MCI and dementia. It was also linked to brain MRI indices. LE8 score had good predicting performance for future risk of MCI and dementia.

The Mediterranean diet is not associated with neuroimaging or cognition in middle-aged adults: a cross-sectional analysis of the PREVENT dementia programme.

BACKGROUND AND PURPOSE: The Mediterranean diet (MedDiet) has been associated with reduced dementia incidence in several studies. It is important to understand if diet is associated with brain health in midlife, when Alzheimer's disease and related dementias are known to begin. METHODS: This study used data from the PREVENT dementia programme. Three MedDiet scores were created (the Pyramid, Mediterranean Diet Adherence Screener [MEDAS] and MEDAS continuous) from a self-reported food frequency questionnaire. Primary outcomes were hippocampal volume and cube-transformed white matter hyperintensity volume. Secondary outcomes included cornu ammonis 1 and subiculum hippocampal subfield volumes, cortical thickness and measures of cognition. Sex-stratified analyses were run to explore differential associations between diet and brain health by sex. An exploratory path analysis was conducted to study if any associations between diet and brain health were mediated by cardiovascular risk factors for dementia. RESULTS: In all, 504 participants were included in this analysis, with a mean Pyramid score of 8.10 (SD 1.56). There were no significant associations between any MedDiet scoring method and any of the primary or secondary outcomes. There were no differences by sex in any analyses and no significant mediation between the Pyramid score and global cognition by cardiovascular risk factors. CONCLUSIONS: Overall, this study did not find evidence for an association between the MedDiet and either neuroimaging or cognition in a midlife population study. Future work should investigate associations between the MedDiet and Alzheimer's disease and related dementias biomarkers as well as functional neuroimaging in a midlife population.

MRI-derived brain iron, grey matter volume, and risk of dementia and Parkinson's disease: Observational and genetic analysis in the UK Biobank cohort.

BACKGROUND: Iron overload is observed in neurodegenerative diseases, especially Alzheimer's disease (AD) and Parkinson's disease (PD). Homozygotes for the iron-overload (haemochromatosis) causing HFE p.C282Y variant have increased risk of dementia and PD. Whether brain iron deposition is causal or secondary to the neurodegenerative processes in the general population is unclear. METHODS: We analysed 39,533 UK Biobank participants of European genetic ancestry with brain MRI data. We studied brain iron estimated by R2* and quantitative susceptibility mapping (QSM) in 8 subcortical regions: accumbens, amygdala, caudate, hippocampus, pallidum, putamen, substantia nigra, and thalamus. We performed genome-wide associations studies (GWAS) and used Mendelian Randomization (MR) methods to estimate the causal effect of brain iron on grey matter volume, and risk of AD, non-AD and PD. We also used MR to test whether genetic liability to AD or PD causally increased brain iron (R2* and QSM). FINDINGS: In GWAS of R2* and QSM we replicated 83% of previously reported genetic loci and identified 174 further loci across all eight brain regions. Higher genetically predicted brain iron, using both R2* and QSM, was associated with lower grey matter volumes in the caudate, putamen and thalamus (e.g., Beta-putamenQSM: -0.37, p = 2*10-46). Higher genetically predicted thalamus R2* was associated with increased risk of non-AD dementia (OR 1.36(1.16;1.60), p = 2*10-4) but not AD (p > 0.05). In males, genetically predicted putamen R2* increased non-AD dementia risk, but not in females. Higher genetically predicted iron in the caudate, putamen, and substantia nigra was associated with an increased risk of PD (Odds Ratio QSM âˆ¼ substantia-nigra 1.21(1.07;1.37), p = 0.003). Genetic liability to AD or PD was not associated with R2* or QSM in the dementia or PD-associated regions. INTERPRETATION: Our genetic analysis supports a causal effect of higher iron deposition in specific subcortical brain regions for Parkinson's disease, grey matter volume, and non-Alzheimer's dementia.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

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.

Neuroinflammation in dementia: A meta-analysis of PET imaging studies.

BACKGROUND: Dementia is a major public health challenge for aging societies worldwide. Neuroinflammation is thought to be a key factor in dementia development. The aim of this study was to comprehensively assess translocator protein (TSPO) expression by positron emission tomography (PET) imaging to reveal the characteristics of neuroinflammation in dementia. METHODS: We used a meta-analysis to retrieve literature on TSPO expression in dementia using PET imaging technology, including but not limited to the quality of the study design, sample size, and the type of TSPO ligand used in the study. For the included studies, we extracted key data, including TSPO expression levels, clinical characteristics of the study participants, and specific information on brain regions. Meta-analysis was performed using R software to assess the relationship between TSPO expression and dementia. RESULTS: After screening, 12 studies that met the criteria were included. The results of the meta-analysis showed that the expression level of TSPO was significantly elevated in patients with dementia, especially in the hippocampal region. The OR in the hippocampus was 1.50 with a 95% CI of 1.09 to 1.25, indicating a significant increase in the expression of TSPO in this region compared to controls. Elevated levels of inflammation in the prefrontal lobe and cingulate gyrus are associated with cognitive impairment in patients. This was despite an OR of 1.00 in the anterior cingulate gyrus, indicating that TSPO expression in this region did not correlate significantly with the findings. The overall heterogeneity test showed I² = 51%, indicating moderate heterogeneity. CONCLUSION: This study summarizes the existing literature on TSPO expression in specific regions of the brain in patients with dementia, and also provides some preliminary evidence on the possible association between neuroinflammation and dementia. However, the heterogeneity of results and limitations of the study suggest that we need to interpret these findings with caution. Future studies need to adopt a more rigorous and consistent methodological design to more accurately assess the role of neuroinflammation in dementia, thereby providing a more reliable evidence base for understanding pathological mechanisms and developing potential therapeutic strategies.

Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia.

BACKGROUND: The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine - have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. METHODS: In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. RESULTS: Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. CONCLUSIONS: Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.

Associations among plasma, MRI, and amyloid PET biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities in a community-dwelling cohort.

INTRODUCTION: We evaluated associations between plasma and neuroimaging-derived biomarkers of Alzheimer's disease and related dementias and the impact of health-related comorbidities. METHODS: We examined plasma biomarkers (neurofilament light chain, glial fibrillary acidic protein, amyloid beta [Aß] 42/40, phosphorylated tau 181) and neuroimaging measures of amyloid deposition (Aß-positron emission tomography [PET]), total brain volume, white matter hyperintensity volume, diffusion-weighted fractional anisotropy, and neurite orientation dispersion and density imaging free water. Participants were adjudicated as cognitively unimpaired (CU; N = 299), mild cognitive impairment (MCI; N = 192), or dementia (DEM; N = 65). Biomarkers were compared across groups stratified by diagnosis, sex, race, and APOE ε4 carrier status. General linear models examined plasma-imaging associations before and after adjusting for demographics (age, sex, race, education), APOE ε4 status, medications, diagnosis, and other factors (estimated glomerular filtration rate [eGFR], body mass index [BMI]). RESULTS: Plasma biomarkers differed across diagnostic groups (DEM > MCI > CU), were altered in Aß-PET-positive individuals, and were associated with poorer brain health and kidney function. DISCUSSION: eGFR and BMI did not substantially impact associations between plasma and neuroimaging biomarkers. HIGHLIGHTS: Plasma biomarkers differ across diagnostic groups (DEM > MCI > CU) and are altered in Aß-PET-positive individuals. Altered plasma biomarker levels are associated with poorer brain health and kidney function. Plasma and neuroimaging biomarker associations are largely independent of comorbidities.

Associations between accelerometer-measured circadian rest-activity rhythm, brain structural and genetic mechanisms, and dementia.

AIM: Knowledge of how circadian rhythm influences brain health remains limited. We aimed to investigate the associations of accelerometer-measured circadian rest-activity rhythm (CRAR) with incident dementia, cognitive dysfunction, and structural brain abnormalities in the general population and underlying biological mechanisms. METHODS: Fifty-seven thousand five hundred and two participants aged over 60 years with accelerometer data were included to investigate the association of CRAR with incidental dementia. Non-parametric CRAR parameters were utilized, including activity level during active periods of the day (M10), activity level during rest periods of the day (L5), and the relative difference between the M10 and L5 (relative amplitude, RA). Associations of CRAR with cognitive dysfunction and brain structure were studied in a subset of participants. Neuroimaging-transcriptomics analysis was utilized to identify the underlying molecular mechanisms. RESULTS: Over 6.86 (4.94-8.78) years of follow-up, 494 participants developed dementia. The risk of incident dementia was associated with decreasing M10 (hazard ratio [HR] 1.45; 95% conference interval [CI], 1.28-1.64) and RA (HR 1.37; 95% CI, 1.28-1.64), increasing L5 (HR 1.14, 95% CI 1.07-1.21) and advanced L5 onset time (HR 1.12; 95% CI, 1.02-1.23). The detrimental associations were exacerbated by APOE ε4 status and age (>65 years). Decreased RA was associated with lower processing speed (Beta -0.04; SE 0.011), predominantly mediated by abnormalities in subcortical regions and white matter microstructure. The genes underlying CRAR-related brain regional structure variation were enriched for synaptic function. CONCLUSIONS: Our study underscores the potential of intervention targeting at maintaining a healthy CRAR pattern to prevent dementia risk.

Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis.

OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valuable information from medical images. This article aims to provide a comparative analysis of radiomics features, 3D-deep learning convolutional neural network (CNN) and the fusion of them, in the evaluation of 18F-FDG PET whole brain images in patients with dementia and normal controls. METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomics analysis was performed on the whole brain after normalization to an optimized template. After selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, the features obtained were added to a neural network model to find the accuracy in classifying HC and demented patients. Forty subjects not included in the training were used to test the models. The results of the three models (radiomics, 3D-CNN, combined model) were compared with each other. RESULTS: Four radiomics features were selected. The sensitivity was 100% for the three models, but the specificity was higher with radiomics and combined one (100% vs. 85%). Moreover, the classification scores were significantly higher using the combined model in both normal and demented subjects. CONCLUSION: The combination of radiomics features and 3D-CNN in a single model, applied to the whole brain 18FDG PET study, increases the accuracy in demented patients.

Radiographers' experiences in working with patients living with dementia in Norway - A qualitative study.

INTRODUCTION: Imaging departments are seeing an increase in the number of patients living with dementia (PWD), driven by the ageing population and diagnostic benefits offered by medical imaging. This study explored radiographers' experiences during imaging examinations for PWD. METHODS: A semi-structured interview guide comprising questions about radiographers' experiences, knowledge concerning PWD, challenges faced, and departmental initiatives was developed. Eight radiographers were interviewed, four working in MRI or general imaging, including CT and four in nuclear medicine, at three hospital trusts in Norway. Data analysis was conducted using inductive content analysis as described by Elo and Kyngäs, following a three-step process of preparation, organising and reporting. The qualified radiographers coded, categorised, and defined the themes and sub-themes to report on the findings. RESULTS: Three main categories emerged: 1. Radiographers' experiences, which included overall challenges and the radiographers' attitudes. 2. Measures undertaken, outlining the actions radiographers take during procedures, and 3.Competencies, highlighting the knowledge possessed by radiographers. Organisational challenges, such as the absence of overarching protocols and insufficient training for radiographers related to PWD, posed difficulties in effectively conducting procedures. Creating a calm environment, collaborating with caregivers, scheduling adequate time for examinations, and possessing good communication skills were viewed as facilitators for conducting examinations successfully. CONCLUSION: Radiographers perceived imaging of patients living with dementia to be generally uncomplicated. However, challenges in planning for and communicating with patients, particularly for advanced examinations or acute settings, were reported. Establishing dementia-friendly departments and training radiographers in specific communication techniques could be beneficial. IMPLICATIONS FOR PRACTICE: There is a need for more dementia-friendly imaging departments and communication training for radiographers working with PWD.

Hypoperfusion in Alzheimer's Disease-Prone Regions and Dementia Conversion in Parkinson's Disease.

PURPOSE OF THE REPORT: Although early detection of individuals at risk of dementia conversion is important in patients with Parkinson's disease (PD), there is still no consensus on neuroimaging biomarkers for predicting future cognitive decline. We aimed to investigate whether cerebral perfusion patterns on early-phase 18 F-N-(3-fluoropropyl)-2ß-carboxymethoxy-3ß-(4-iodophenyl) nortropane ( 18 F-FP-CIT) PET have the potential to serve as a neuroimaging predictor for early dementia conversion in patients with PD. MATERIALS AND METHODS: In this retrospective analysis, we enrolled 187 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET at initial assessment and serial cognitive assessments during the follow-up period (>5 years). Patients with PD were classified into 2 groups: the PD with dementia (PDD)-high-risk (PDD-H; n = 47) and the PDD-low-risk (PDD-L; n = 140) groups according to dementia conversion within 5 years of PD diagnosis. We explored between-group differences in the regional uptake in the early-phase 18 F-FP-CIT PET images. We additionally performed a linear discriminant analysis to develop a prediction model for early PDD conversion. RESULTS: The PDD-H group exhibited hypoperfusion in Alzheimer's disease (AD)-prone regions (inferomedial temporal and posterior cingulate cortices, and insula) compared with the PDD-L group. A prediction model using regional uptake in the right entorhinal cortex, left amygdala, and left isthmus cingulate cortex could optimally distinguish the PDD-H group from the PDD-L group. CONCLUSIONS: Regional hypoperfusion in the AD-prone regions on early-phase 18 F-FP-CIT PET can be a useful biomarker for predicting early dementia conversion in patients with PD.

Risk of conversion to mild cognitive impairment or dementia among subjects with amyloid and tau pathology: a systematic review and meta-analysis.

BACKGROUND: Measurement of beta-amyloid (Aß) and phosphorylated tau (p-tau) levels offers the potential for early detection of neurocognitive impairment. Still, the probability of developing a clinical syndrome in the presence of these protein changes (A+ and T+) remains unclear. By performing a systematic review and meta-analysis, we investigated the risk of mild cognitive impairment (MCI) or dementia in the non-demented population with A+ and A- alone and in combination with T+ and T- as confirmed by PET or cerebrospinal fluid examination. METHODS: A systematic search of prospective and retrospective studies investigating the association of Aß and p-tau with cognitive decline was performed in three databases (MEDLINE via PubMed, EMBASE, and CENTRAL) on January 9, 2024. The risk of bias was assessed using the Cochrane QUIPS tool. Odds ratios (OR) and Hazard Ratios (HR) were pooled using a random-effects model. The effect of neurodegeneration was not studied due to its non-specific nature. RESULTS: A total of 18,162 records were found, and at the end of the selection process, data from 36 cohorts were pooled (n= 7,793). Compared to the unexposed group, the odds ratio (OR) for conversion to dementia in A+ MCI patients was 5.18 [95% CI 3.93; 6.81]. In A+ CU subjects, the OR for conversion to MCI or dementia was 5.79 [95% CI 2.88; 11.64]. Cerebrospinal fluid Aß42 or Aß42/40 analysis and amyloid PET imaging showed consistent results. The OR for conversion in A+T+ MCI subjects (11.60 [95% CI 7.96; 16.91]) was significantly higher than in A+T- subjects (2.73 [95% CI 1.65; 4.52]). The OR for A-T+ MCI subjects was non-significant (1.47 [95% CI 0.55; 3.92]). CU subjects with A+T+ status had a significantly higher OR for conversion (13.46 [95% CI 3.69; 49.11]) than A+T- subjects (2.04 [95% CI 0.70; 5.97]). Meta-regression showed that the ORs for Aß exposure decreased with age in MCI. (beta = -0.04 [95% CI -0.03 to -0.083]). CONCLUSIONS: Identifying Aß-positive individuals, irrespective of the measurement technique employed (CSF or PET), enables the detection of the most at-risk population before disease onset, or at least at a mild stage. The inclusion of tau status in addition to Aß, especially in A+T+ cases, further refines the risk assessment. Notably, the higher odds ratio associated with Aß decreases with age. TRIAL REGISTRATION: The study was registered in PROSPERO (ID: CRD42021288100).

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.