bluuebunny/crossref_metadata_2025 huggingface dataset:
Nationwide Trends in Medical Comorbidity Burden of Patients Newly Diagnosed with Dementia, 2015‐2021
Authors: Lusk Jay B , Ford Cassie B , Blass Beau , Johnson Kim G , Clark Amy G , Soneji Samir , O'Brien Richard J , O'Brien Emily C , Hammill Bradley G
Published: January 2025

Background

Medical comorbidity burden has a substantial impact on care for patients with dementia and has major impacts on quality of life. No nationwide study has evaluated trends in medical comorbidity burden of patients with a new diagnosis of dementia. We therefore performed a nationwide study of medical claims data to understand the prevalence of comorbid medical conditions at time of dementia diagnosis in real‐world clinical practice.

Method

We studied 100% of nationwide Medicare claims from 2014‐2021 and evaluated the prevalence of medical comorbidities among patients with new diagnoses of dementia. Medical comorbidities were ascertained through searching inpatient and outpatient claims for 1 year prior to dementia diagnosis. We used validated international classification of diseases (ICD) code algorithms to identify both dementia diagnoses and medical comorbidities. We report the number and percentage of patients with each comorbid medical condition.

Result

A total of 5,721,711 patients with incident dementia were included in the study. Anxiety disorders were much more common among patients diagnosed in 2021 (29.8%) compared to patients diagnosed in 2015 (23.8%). Depression was also more common among patients diagnosed in 2021 (33.8%) versus 2015 (31.8%). Hypertension remained very common (85.7% in 2021 vs 86.6% in 2015). There was a major decrease in patients with prior history of psychosis (17.8% in 2015 vs 8.1% in 2021). There were modest increases in the rates of comorbid cardiovascular disease (Table 1). Figure 1 shows the percentage of patients with new diagnosis of dementia with various conditions in 2015 and 2021 respectively.

Conclusion

In a national database of Medicare claims, there were substantial changes from 2015‐2021 in comorbidity burden among patients newly diagnosed with dementia. There was a dramatic decrease in comorbid psychosis which could be attributable to Medicare initiatives to reduce the inappropriate use of antipsychotic medications among patients with dementia. Our results have implications for health system performance and risk adjustment, given an overall increase in comorbidity burden across many disease states from 2015‐2021. Strategies to address rising multimorbidity may be increasingly important for patients with dementia.
Serum calcium, alkaline phosphotase as risk factors for bone metabolism in Alzheimer's disease
Authors: Chintamanibatla Rupesh RSV , Mehta Preet , Kamarthi Sagar , Suryadevara Vidyani
Published: January 2025

Background

Alzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline and progressive deterioration of brain function. Recent research has suggested a complex interplay between AD and bone health, with individuals affected by AD exhibiting an increased propensity for fractures and falls. Our preclinical studies in PSEN, MAPT P301 S and FDD mice have shown sex‐dependent changes in the bone in AD mice, compared to their age‐matched wild type mice. To delve deeper into the molecular underpinnings of this relationship, our study leverages data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate alterations in bone‐specific markers during AD pathology; to unravel potential links between bone metabolism and AD progression.

Methods

Data from ADNI‐1(n=800), ADNI‐GO (n=700), ADNI‐2 (n=1300) and ADNI‐3 (n=2000) was considered for analysis. Spearman correlation analysis was performed between bone markers i.e. calcium and alkaline phosphatase with AD pathology‐ imaging and cognitive assessment.

Results

Calcium levels and alkaline phosphate levels which are critical for bone metabolism were found to be significantly lower in patients with EMCI, LMCI and AD compared to control patients. A negative correlation was found between Calcium levels and Left Hippocampal Volume (Corr Coeff = ‐0.025, p = 1.38e‐19), Right Hippocampal Volume (Corr Coeff = ‐0.030, p = 1.95e‐26), and Montreal Cognitive Assessment (MOCA) scores (Corr Coeff = ‐0.015, p = 4.34e‐08). Alkaline Phosphatase levels show a strong negative correlation with Left Hippocampal Volume (Corr Coeff = ‐0.075, p = 8.45e‐154) and Right Hippocampal Volume (Corr Coeff = ‐0.078, p = 5.74e‐168) and a negative correlation with MOCA scores (Corr Coeff = ‐0.065, p = 9.02e‐118). The negative correlations between Calcium and Alkaline Phosphatase levels with left and right hippocampal volume suggest that lower bone‐specific markers are associated with reduced hippocampal volume, elucidating brain‐bone crosstalk during AD

Conclusion

The study reveals correlation between serum bone markers (calcium, alkaline phosphatase) and declining hippocampal volume and cognitive function in Alzheimer's, suggesting a potential link between bone health and AD pathology. This discovery paves the way for deeper understanding and new avenues for diagnosis and treatment.
Leveraging T1 MRI Images for Amyloid Status Prediction in Diverse Cognitive Conditions Using Advanced Deep Learning Models
Authors: Hosseini Seyyed Ali , Servaes Stijn , Rahmouni Nesrine , Therriault Joseph , Tissot Cécile , Macedo Arthur C. , Wang Yi‐Ting , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Zheng Yansheng , Chan Tevy , Trudel Lydia , Gauthier Serge , Rosa‐Neto Pedro
Published: January 2025

Background

Timely and non‐invasive prediction of amyloid status are pivotal in Alzheimer’s disease (AD) diagnostics. This research leverages T1 MRI images to predict amyloid positivity or negativity, offering an economical and less invasive alternative to amyloid PET scans. Using the comprehensive TRAID dataset from McGill University, the study evaluates a spectrum of cognitive conditions including AD, atypical AD, Cognitively Normal (CN), Mild Cognitive Impairment (MCI), MCI not due to AD, Suspected Non‐Alzheimer's Pathophysiology (SNAP), and Vascular MCI (VMCI).

Method

The study involved 588 subjects, representing a cross‐section of cognitive states. A VGG16‐based feature extraction process was meticulously applied to T1 MRI images to capture complex biomarkers of AD. These high‐dimensional features, alongside demographic data, were harnessed to train two deep learning models: a Long Short‐Term Memory (LSTM) model and a Convolutional Neural Network (CNN) ensemble model. To prevent overfitting and data leakage, the models employ 10‐fold StratifiedKFold validation and early stopping, alongside regularization techniques like dropout and batch normalization. Data preprocessing is contained within StratifiedKFolds, and an independent test set is used for unbiased evaluation. The predictive accuracy was scrutinized using a range of metrics, including accuracy, F1‐score, recall, precision, and ROC AUC on the validation sets.

Result

The LSTM model reported average validation metrics with an accuracy of 70.88%, F1‐score of 70.64%, recall of 78.07%, precision of 70.88%, and a ROC AUC of 71.86%. The CNN ensemble model showed a marked improvement, particularly in its recall of 88.00% and ROC AUC of 82.47%, alongside an accuracy of 76.27% and F1‐score of 75.86% (Figure 1 and 2). However, in another model, it was the CNN ensemble model that demonstrated remarkable predictive power, achieving an ROC AUC of 88% within the AD spectrum (CN, MCI, and AD subjects), indicating a high degree of predictive power in classifying amyloid status.

Conclusion

This study underscores the CNN ensemble model's robustness in predicting amyloid status across a broad spectrum of cognitive conditions, with particular emphasis on its application in high‐sensitivity clinical settings. These findings represent a significant advancement in the field of AD diagnostics, contributing to the development of more accessible and cost‐effective prognostic techniques.
Trends in Incidence and Prevalence of Dementia Among United States Medicare Beneficiaries, 2014‐2021, by Race, Sex, and Neighborhood Socioeconomic Status
Authors: Blass Beau , Ford Cassie B , Johnson Kim G , Clark Amy G , Soneji Samir , O'Brien Richard J , Hammill Bradley G , O'Brien Emily C , Lusk Jay B
Published: January 2025

Background

Estimating the incidence and prevalence of dementia is challenging. Cohort and brain banking studies can provide very precise estimates of incidence and prevalence in specific populations, but it is not clear how generalizable those estimates are to other populations. Furthermore, diagnoses of dementia made in clinical practice may differ from gold‐standard neuropathological diagnoses. Understanding trends in the incidence and prevalence of dementia as determined by routinely collected administrative claims data can provide critical context for clinicians and policymakers. Furthermore, understanding disparities in incidence and prevalence in the population at large can help to ameliorate biases from inequitable representation in survey and cohort designs.

Method

We performed a retrospective study of 100% of Medicare claims from 2013‐2021 (2013 being reserved as a look‐back year). We defined incident dementia diagnoses in each year among patients without a known history of dementia based on validated international classification of diseases (ICD) algorithms. We defined prevalent dementia as patients with any prior history of dementia in each year. We stratified incidence and prevalence by race (classified as Black, non‐Hispanic, Other race/ethnicity, and White, non‐Hispanic), sex, and neighborhood socioeconomic status, measured by the well‐established Area Deprivation Index, which summarizes socioeconomic conditions at the census block group level (roughly 600‐1000 people).

Result

From 2014‐2021, there were 5,721,711 incident cases of dementia in the United States. The average age at diagnosis was 80.5 years. Patients were 57.7% female, 86.6% White and 7.8% Black, 15.8% dually eligible for Medicare and Medicaid, and 41.5% from the South region. Table 1 shows incidence rates and prevalence of dementia from 2014‐2021. Table 2 shows incidence rates and prevalence stratified by legal sex and race/ethnicity. Figure 1 shows incidence rates of dementia stratified by neighborhood socioeconomic status.

Conclusion

We found declining dementia incidence but increasing dementia prevalence in a sample that includes the vast majority of older adults in the United States. Differences in dementia incidence by sex, race/ethnicity, and neighborhood socioeconomic status remained notable in 2021. Our results have implications for health system performance in providing high‐quality and equitable clinical care for a growing population of patients with dementia.
Resting‐state functional connectivity correlates of cognitive function in Mild Cognitive Impairment on functional Near Infrared Spectroscopy
Authors: Rangarajan Subhashini K , Nichenametla Sonika , Sreeraj Vanteemar S , Harbishettar Vijaykumar , Sinha Preeti , Sivakumar Palanimuthu Thangaraju , Venkatasubramanian Ganesan
Published: January 2025

Background

Mild cognitive impairment(MCI) is characterized by an impairment in one or more cognitive domains greater than expected for a person’s age and educational background with preserved functional independence. Functional Near Infra‐Red Spectroscopy (fNIRS) is a modality of non‐invasive neuroimaging that utilizes the optical properties of oxygenated and deoxygenated hemoglobin in the tissue to measure their absolute or relative concentrations following neuronal activity. fNIRS has been used to evaluate neurohemodynamics in MCI. Lower tissue oxygenation in bilateral frontal and parietal regions was found at resting state, which correlated with cognitive functioning in MCI. The objective of this study was to evaluate the correlation between cognitive functioning and resting state functional connectivity on fNIRS in MCI.

Methods

The study was conducted at the Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India (Institutional Ethics Committee approval no.: NIMHANS/34th IEC (BEH.SC.DIV.)/2022) on consenting participants with MCI (N = 38). Detailed cognitive assessment was performed using a validated comprehensive computerised cognitive battery. fNIRS data acquisition was done using the NIRScout device, NIRx medical technologies LLC, CA, USA, operating on continuous wave domain, with two wavelengths of 760nm and 850 nm. 8 sources and 8 detectors were placed in a bilateral prefrontal montage placement resulting in 18 channels (9 on each side). Nodal metrics such as clustering coefficient, degree centrality, shortest path length, local efficiency and a Global metric‐ small worldness was evaluated. Correlation coefficients were computed for the nodal and global metrics with cognitive domains such as reaction time, complex attention, memory, language functioning and perceptuomotor functioning.

Results

Mean age of participants was 66.18 ± 6.18 years. The mean duration of illness was 2.63 ± 2.5 years. The results of the resting state nodal and global metrics and their correlation with cognitive functioning will be presented during the conference.

Conclusion

fNIRS is reported to have a temporal resolution that is comparable to fMRI, at the same time being less expensive, portable and having less interference because of motion artifacts. Findings from this study will provide insights on the functional connectivity correlates of cognitive functioning in MCI.
Characterization of a novel Carbon Dot Inhibitor of Tau and Amyloid Beta Aggregation in Alzheimer’s Disease
Authors: Burr Hannah J , Smith Nathan Scott , Zhang Wei , Wang Chunyu
Published: January 2025

Background

Alzheimer’s Disease (AD) is a neurodegenerative disorder whose pathological hallmarks include tau and amyloid beta aggregation, a phenomenon that has been linked to inflammation and degradation of brain tissue. Prior data published in the Wang lab suggests that carbon dots (CDs) synthesized from citric acid and urea can inhibit aggregation. We sought to characterize the inhibitory effects of a new class of CDs synthesized from varied ratios of Congo red and citric acid. Since Congo red carbon dots (CRCDs) can cross the blood‐brain barrier (BBB), they have the potential to be used as a therapeutic.

Method

A new type of CD was synthesized via the muffle furnace method using citric acid, urea, thiourea, selenourea, formamide, and congo red. The CDs have a diameter less than 20 nanometers, enabling them to cross both the BBB and the cell membrane, which was confirmed experimentally with zebrafish models. The newly synthesized CDs were used in Thioflavin T (ThT) aggregation assays to assess protein aggregation inhibition. This was verified visually by Atomic Force Microscopy (AFM).

Result

All three CRCDs inhibited tau aggregation, with CRCD1 being the most effective. CRCD1 had the lowest IC50 of 0.21ug/mL, while CRCD2 and CRCD3 had an IC50 of 0.50ug/mL and 2.55ug/mL, respectively. All three CRCDs also inhibited amyloid beta aggregation, with CRCD being the most effective. CRCD1 had the lowest IC50 of 1.76 ug/mL, while CRCD2 and CRCD3 had an IC50 of 3.34 ug/mL and 5.61 ug/mL, respectively.

Conclusion

The ability to reduce tau aggregation in the brain has the potential to slow the growth of the disease and potentially alleviate symptoms. There are currently limited treatments available for AD, but CRCDs show great promise.
Disclosure of Alzheimer’s Disease Diagnosis: Patient Preferences and Physician Approach
Authors: Söderlund Maria Elvira , Guajardo Maria Elena , Cavagna Marina , Labos Edith , Schapira Marcelo , Seinhart Daniel Bernardo
Published: January 2025

Introduction

The announcement of an Alzheimer’s disease (AD) diagnosis is a complex process that requires careful consideration by healthcare professionals. This study aims to explore the desires of patients regarding the disclosure of their Alzheimer’s diagnosis and to understand the practices adopted by physicians in this regard.

Methods

Within the context of memory disorders consultations, patients were queried about their preferences regarding the disclosure of an AD diagnosis if it were determined to be the cause of their memory issues. Additionally, a survey was conducted among physicians specializing in the care of older adults, specifically at the Italian Hospital of Buenos Aires, to assess their practices and concerns related to the disclosure of AD diagnoses.

Results

Out of 250 patients, 223 expressed a desire to know their diagnosis, while 19 were uncertain, and 7 declined. Among the 76 participating physicians, the majority (82.9%) were aged between 35 and 65 years, with 68% being female. A significant portion (58.7%) were geriatricians while 27.8% were primary care physicians. Notably, 32% of physicians reported hardly ever using AD when delivering a diagnosis and 48% preferring the term “cognitive impairment”. Only 2 physicians always use AD in this context. The main reasons for avoiding this term were the potential risk of psychological impact (43.8%) and stigmatization (20.5%) associated with the diagnosis.

Conclusions

The incorporation of biomarkers enhances diagnostic certainty and therapeutic possibilities for AD. While most patients express a desire to know their diagnosis, a significant proportion of physicians opt for more general terms such as “cognitive impairment” instead of explicitly using the term AD. Understanding patient preferences and providing information in a sensitive manner are crucial aspects in the disclosure process, aiming to minimize potential harm. The right of patients to know or not to know their diagnosis should be respected, highlighting the importance of ethical and patient‐centered approaches in AD diagnosis disclosure.
Longitudinal changes in cerebral metabolic rate of oxygen in older adults without and with cognitive impairment
Authors: Wu Jiani , Oishi Kumiko , Soldan Anja , Pettigrew Corinne , Lin Zixuan , Zhu Yuxin , Jiang Dengrong , Li Xin , Moghekar Abhay , Liu Peiying , Oishi Kenichi , Albert Marilyn S. , Lu Hanzhang
Published: January 2025

Background

Cerebral metabolic rate of oxygen (CMRO2) denotes the amount of O2 that the brain consumes. Changes in CMRO2 during aging and neurodegeneration have not been fully characterized. Using a non‐invasive, non‐contrast MRI CMRO2 technique, the present study reports CMRO2 changes in older adults from a total of 526 measurements, the largest CMRO2 dataset to date.

Method

Experimental procedure: Analyses included 227 participants from the Biomarkers‐for‐Older‐Controls‐at‐Risk‐for‐Dementia (BIOCARD) cohort. Figure 1 shows the demographic information. Participants were categorized into 3 diagnostic categories: cognitively normal individuals who remained cognitively normal (n=187), individuals who progressed from normal cognition to Mild Cognitive Impairment (MCI) or dementia during follow‐up (n=13), and individuals with MCI/dementia at baseline (n=27). Participants were scanned on a 3T MRI (Philips) in a longitudinal study design. On average, participants had 2 scans (range = 1‐4) collected over 3.1 years of follow‐up. CMRO2 was estimated from the arterio‐venous difference in oxygen content, using a non‐invasive MRI technique. Data analysis: Linear mixed effect models were used to examine longitudinal changes in CMRO2 over time, as well as the effect of impairment (MCI/dementia) on CMRO2.

Result

In individuals who remained cognitively normal during follow‐up (Figure 2), there was an increase in CMRO2 over time (p<0.001), suggesting that the brain is “working harder” to compensate for its lower efficiency. However, the rate of increase in CMRO2 was attenuated among individuals with higher baseline ages (p=0.039). There were also main effects of baseline age (p=0.001) and sex (p=0.014), indicating lower CMRO2 levels among older than younger adults and among men than women. Next, we included participants who were cognitively impaired and those who progressed from normal cognition to cognitive impairment (Figure 3A). CMRO2 was lower among progressors and impaired participants than those who remained normal (p=0.001). There was an interaction effect between time and diagnosis category (p=0.044), indicating that participants who were impaired at baseline or progressed to impairment showed a different pattern of CMRO2 change over time compared to those who remained normal (Figure 3B).

Conclusion

CMRO2 revealed a non‐monotonic change in aging and cognitive impairment, suggesting a multi‐factorial and/or multi‐phase alteration of brain metabolism.
White Matter Changes in Alzheimer’s and Treated Huntington’s Disease Mouse Models Detected by Diffusion MRI
Authors: Liu Andy , Simmons Danielle , Alvarez Connor , Wang Yi , Spincemaille Pascal , Longo Frank M. , Zeineh Michael , Georgiadis Marios
Published: January 2025

Background

Myelin integrity is central to healthy brains and is increasingly shown to be compromised in neurodegenerative diseases. Diffusion‐ and susceptibility‐based MRI metrics can detect myelin changes. We show advanced diffusion and susceptibility metrics can detect degenerative myelin changes in ex vivo AD and HD mouse models.

Method

AD mice included 2 groups: 8‐month‐old male hAPP (Lond/Swe mutations, n = 4) and wild‐type (WT, n = 4). HD mice included 3 groups: 12‐week‐old vehicle‐treated controls (WT, n = 4), R6/2 HD treated for 7 weeks with vehicle (HD, n = 4) or LM11A‐31 (HD‐treated, n = 4). After perfusion‐fixation and brain extraction, 4‐shell diffusion (b = 1,2,5,10ms/µm2, 100 directions) and multi‐echo gradient echo (MGE) MRI (10 echoes, GRE = 4‐40ms) were performed on a Bruker 7T scanner. We used DESIGNER and MEDI to generate diffusion‐ and susceptibility‐based myelin‐sensitive metrics, respectively (diffusion: mean/radial/axial diffusivities ‐MD/RD/AD, kurtoses ‐MK/RK/AK, axonal water fraction ‐AWF; susceptibility: R2*, quantitative susceptibility mapping‐QSM) (Fig. 1A). Datasets were registered to the Allen Atlas through linear/non‐linear transformations using FSL flirt/ANTs (Fig. 1B). Median values were derived for two Allen Atlas white matter regions‐of‐interest ‐ROIs (corpus callosum and fornix). For AD, scan timing effects were included in a 2‐way ANOVA analysis. For HD, two‐sample t‐tests were performed between the 3 groups. For histological validation, a second set of R6/2 HD brains (WT, HD, HD‐treated n = 10‐13 mice/group) were myelin basic protein (MBP)‐stained, and the percent area of striatum immunostaining determined by ImageJ thresholding.

Results

AD mice showed increased RD and reduced AWF and RK compared to controls in both ROIs, suggesting a loss of myelinated axons. Susceptibility‐based metrics show no clear pattern. HD mice showed an increasing trend in AWF and RK and decreases in RD in both ROIs vs WT, suggesting increased myelination. Interestingly, LM11A‐31‐treated HD mice showed a significant reversal in RD and AWF. Histologically, myelin immunostaining was increased in the striatum of HD mice, reversed by LM11A‐31.

Conclusion

Diffusion MRI myelin‐sensitive metrics detected possibly compromised myelin in AD mouse white matter (corpus callosum and fornix). In HD mice, both diffusion MRI and histology showed a trend towards increased myelin, which was reversed by LM11A‐31 treatment.
Blood biomarkers of amyloid and tau pathologies, brain degeneration, inflammation, and oxidative stress in early‐ and late‐onset Alzheimer's disease
Authors: Xia Xinyi , Qin Qi , Tang Yi
Published: January 2025

Background

Blood biomarkers provide a non‐invasive and accessible means of detection for Alzheimer's disease (AD). Specifically, biomarkers associated with early‐onset AD (EOAD) illuminate the underlying mechanisms and provide valuable guidance for potential therapies. We aim to present blood biomarker evidence in individuals with both EOAD and late‐onset AD (LOAD), as well as in age‐matched healthy controls.

Methods

A total of 95 participants were enrolled in the study. We assessed levels of 18 distinct blood biomarkers, and concurrently examined cerebrospinal fluid amyloid and tau markers, neuropsychological test scores, APOEe4 carrier status, and neuroimaging markers. Group disparities, correlations, and the diagnostic utility of these biomarkers were investigated.

Results

In patients with EOAD, levels of blood IL‐4, IL‐6, and TNF‐a were notably lower when compared to LOAD. AD patients exhibited higher blood levels of p‐tau181, NfL, GFAP, tAOC, and MDA, as well as lower levels of Aß1‐42, SOD, and IL‐12p70. Intricate interactions were observed among these blood biomarkers, with inflammatory markers demonstrating correlations with neuroimaging markers within the AD cohort. Individuals carrying the APOEe4 allele displayed elevated levels of GFAP and IL‐4 in comparison to non‐carriers. Additionally, blood levels of Aß1‐42, p‐tau181, NfL, and GFAP were found to correlate with neuropsychological scores and proved effective in distinguishing AD, with GFAP exhibiting particular relevance in early‐onset cases.

Conclusion

The study demonstrated that individuals with EOAD may experience less inflammatory responses than older patients. Peripheral inflammation was associated with amyloid and tau pathologies, astrogliosis, oxidative stress, and alterations in brain structure and function. Blood Aß1‐42, p‐tau181, NfL, and GFAP showed promise in detecting cognitive decline and AD, with GFAP exhibiting relevance in early‐onset cases.
Predicting cognitive function from multimodal mesoscale neuroimaging data
Authors: Duchesne Simon , Potvin Olivier , Dieumegarde Louis
Published: January 2025

Background

Our objective was to assess individual and joint relationships between various mesoscale indicators of brain health (e.g., neuronal, metabolic, and vascular integrity) and cognitive function.

Method

We used 7224 timepoint measures originating from 1825 participants of the ADNI study who were cognitively healthy or with mild cognitive impairment at initial assessment. Cognitive function outcomes were test results on the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog), the Rey auditory Verbal Learning Test immediate recall (RAVLTi), the Rey auditory Verbal Learning Test delayed recall (RAVLTd), the Trail Making Test A (Trail a), and the Trail Making Test B (Trail b) tasks. Four families of markers were used. First were three families of mesoscale neuroimaging assessed by MRI for 88 brain regions: 1. Neuronal integrity was estimated by FreeSurfer 6.0 produced volumes extracted from T1‐weighted (T1w) images; 2. Neurite integrity was estimated through T1w/T2‐weighted MRI ratios; 3. Cerebral metabolism integrity was measured by synthetic fluorodeoxyglucose‐positron emission tomography, generated by a model based on a latent‐space regularized generative adversarial net from T1w MRIs. Secondly, 4. Systemic markers of vascular health‐related factors were included in the model (history of cardiovascular diseases, alcohol abuse, smoking history, systolic and diastolic blood pressure, history of hypertension, type 2 diabetes, Hachinski score). Cross‐sectional and future cognitive outcomes were all predicted in a single machine learning model using all combinations of predictor families at baseline, time interval between outcomes and baseline as long as sex and education. Three types of regression models were tested (Ridge, Partial Least Squares and Support Vector).

Result

Individually, neuronal and metabolism integrity were the best predictors of cognitive function (Fig1; R2: .15‐.35 and MAE in Z score: .08‐.23). The combination of two or more mesoscale brain health markers improved predictions, with the best combination being neuronal integrity with metabolism (Figs2‐3; mean R2: .28; mean MAE: .12). However, increasing the number of markers led to similar results.

Conclusion

While the prediction of cognition improved with a combination of brain health mesoscale markers, a plateau seems to be reached after combining two families.
Increased blood‐brain barrier permeability following COVID‐19 and its correlation with Alzheimer's biomarkers
Authors: Sun Zhe , Frontera Jennifer , Masurkar Arjun V. , Boutajangout Allal , Jiang Li , Thawani Sujata , Links Jon , Vedvyas Alok , Betensky Rebecca A , Debure Ludovic , Ahmed Wajiha , Wisniewski Thomas , Ge Yulin
Published: January 2025

Background

Post‐COVID cognitive dysfunctions, impacting attention, memory, and learning, might be linked to inflammation‐induced blood‐brain barrier (BBB) impairment. This study explores post‐COVID BBB permeability changes using a non‐contrast water‐exchange based MRI and their associations with blood Alzheimer’s biomarkers.

Method

Sixty‐seven participants were classified based on COVID (COV) and cognitive (COG) statuses into three groups: COV+/COG‐ (n=34), COV+/COG+ (n=23), and COV‐ (n=10) for comparisons (COV+: Laboratory‐verified SARS‐CoV‐2 infection; COV‐: No history of SARS‐CoV‐2 infection and negative SARS‐CoV‐2 nucleocapsid antibody test.). All patients underwent neuropsychological testing and were coded as having cognitive impairment (COG+) based on neuropsychological testing (=1 abnormal test result). Clinician interview with the patient and informant. Patients were imaged with the following 3T‐MRI sequences: 1) T1‐MPRAGE; 2) Phase contrast (PC)‐MRI for quantification of cerebral blood flow (ƒ). 3) Water‐extraction‐with‐phase‐contrast‐arterial‐spin‐tagging (WEPCAST) [1], estimating permeability‐surface‐area‐product (PS) calculated with Renkin‐Crone Model: PS = ‐ln(1‐E) *ƒ (Figure 1). Plasma biomarkers of Alzheimer’s disease (AD), including Aß40, Aß42, NFL, and pTau181, were measured on the same day as the MRI. PS was compared between groups. Correlations were computed between PS and AD biomarkers.

Result

We included 67 non‐hospitalized participants (age: 64.7, F/M: 43/24) who successfully completed their clinical and MRI exams. Illustrative WEPCAST images and the results of group comparisons indicated that BBB permeability (PS) was higher in COV+ than that in COV‐ subjects (119 vs 96 mg/100 g/min, P=0.006), as well as in any patient with cognitive dysfunction (COG+ vs COG‐, 122 vs 111 mg/100 g/min, P=0.0319). Similarly, among COV+ subjects, PS was significantly higher in COV+COG+ compared to COV+COG‐ subjects (129 vs 113 mg/100 g/min, P=0.0041; Figure 2). After adjusting age, PS was negatively correlated with plasma Aß42 (r=‐0.28, P=0.02), and positively correlated with pTau181 (r=0.24, P=0.046) in all subjects. (Figure 3). There was no significant correlation between PS and NLF or Aß40.

Conclusion

Our findings demonstrate that SARS‐CoV‐2 virus has an impact on BBB integrity. This effect correlates with several AD biomarkers, including plasma Aß42 and pTau181, highlighting the potential contribution of increased BBB permeability in post‐COVID to AD pathology. Reference: 1. Lin Z, et al. Magn. Reson. Med. 2018;80:1507–1520.
Examining Methylation at the Intersection of Genetic Risk To Alzheimer's Disease And Alcohol Use Disorder
Authors: Pathak Gita , Pietrzak Robert H , Levey Daniel , Montalvo‐Ortiz Janitza , Kranzler Henry , Gelernter Joel , Polimanti Renato A
Published: January 2025

Background

Alcohol Use Disorder (AUD) affects over 15 million individuals in the United States, contributing to oxidative stress, neuroinflammation, and elevating the risk of neurodegeneration. Despite this, the connection between AUD and aging conditions, particularly Alzheimer’s disease (AD), remains unclear. AD, with a heritability of 60‐80%, is genetically linked, necessitating an exploration of the molecular implications of AUD and genetic susceptibility to AD.

Method

Participants were assessed for AUD using DSM‐5 criteria, and genotype data were imputed with the TOPMED reference panel. The polygenic score (PGS) of AD was calculated in European ancestry participants (mean±SD, 40.12 ±13.3 yrs; N= 4,915), adjusting for age, sex, and genetic ancestry. Methylation was assayed using the Illumina EPIC array using blood to conduct an epigenome‐wide association study (EWAS) on the interaction between PGS‐AD and AUD at 657,226 CpG sites in a subset of participants (N=452). Intrinsic and extrinsic epigenetic age acceleration (IEAA, EEAA) were calculated using Hannum and Horvath’s epigenetic clocks.

Result

We observed a significant association between PGS of AD and AUD diagnosis (cases=34%; OR=1.44; p=0.005). The subsequent EWAS revealed top CpG sites in genes associated with AD pathogenesis, including RLF, CD2AP, GLUL, GJA3, TRIM26, JAG2, RERE, SRRM3, COL11A2. For these top CpGs, we investigated the beta (methylation intensity) in individuals with high‐PGS of AD (top 25% quantile) and stratified them by AUD diagnosis. Briefly, the top two CpG sites in AUD cases‐cg01101221(RLF; p=5.51E‐07) was hypomethylated (p=0.0205); and cg05804888(CD2AP; p=4.61E‐06) was hypermethylated (p=0.0142). We also observed interaction of AUD and PGS of AD is associated with accelerated aging‐ IEAA and EEAA from both Horvath and Hannum’s clocks (p<8.56E‐06<0.0092).

Conclusion

The identified genes have been implicated in AD pathogenesis, such as CD2AP's association with Aß production and GLUL's role in brain lipid metabolism. The study also highlighted the association between AUD and PGS‐AD interaction and accelerated epigenetic aging. While the CpG associations did not reach Bonferroni significance, our ongoing research aims to extend these findings to other ancestries and cohorts to improve statistical power. Thus providing a comprehensive understanding of the complex interactions between AUD diagnosis and genetic susceptibility to AD.
PhysicOmics: Combining MR physics, proteomics, and transcriptomics to study cerebral aging and neurodegeneration
Authors: Bae Jonghyun , Gong Zhaoyuan , Guo Alex , Laporte John P , Faulkner Mary E , Bouhrara Mustapha
Published: January 2025

Background

Neuroimaging‐based evidence suggests that changes in cerebral tissue determinants, including axonal density and myelin content, are associated with aging and neurodegenerative diseases. While neuroimaging markers show strong association with physiological changes, direct validation of their specificity remains challenging. Histology provides useful information for validation, however, faces limitations including denaturation of the sample during preparation. However, in the era of omics, validation of neuroimaging markers becomes accessible with dataset from various biological measurements. We provide two examples of validations of our MR physics‐based markers of myelin water fraction (MWF), a surrogate of myelin content, and neurite density index (NDI), a proxy of axonal density, using proteomics and transcriptomics (Fig.1).

Method

Association between neurofilament light chain (NfL) and NDI: 58 participants (age = 21‐83yrs.) underwent our multishell diffusion imaging protocol for NDI determination using C‐NODDI. Blood was collected at the time of MRI to quantify the NfL level, a biomarker of axonal integrity, using Simoa assays. The association between NDI and NfL was evaluated using a multiple linear regression analysis. Association between myelin gene expression and MWF: 132 subjects (age = 22‐94yrs.) underwent our relaxometry protocol for MWF determination using BMC‐mcDESPOT. Further, microarray data were obtained from the Allen Human Brain Atlas, providing gene expression data from 6 healthy donors. Using Pearson correlations, we assessed the association between regional MWF measurements (averaged across participants) and regional gene expressions, with a focus on Myelin Basic Protein (MBP) and transcription factors involved in myelin synthesis.

Result

Figure 2 shows strong negative regional correlations between NfL and NDI derived using C‐NODDI. Further, we found strong positive correlations between MWF and genes involved in myelin production (Fig.3).

Conclusion

Given the strong correlations between NfL or MBP and NDI or MWF, this work provides evidence of the specificity of these MRI biomarkers of axonal density and myelin content, making C‐NODDI and BMC‐mcDESPOT powerful and relevant MR methods for clinical investigations and trials. While further investigations are still required in the context of neurodegeneration, our work demonstrates the potential of combining MR physics and omics to delve deeper into the neurobiological and molecular mechanisms underlying aging and age‐related diseases.
Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer’s Disease Model
Authors: Chamberlain‐Alvarado Helena , Cortes‐Canteli Marta , Casquero‐Veiga Marta
Published: January 2025

Background

Alzheimer’s Disease (AD) has a long preclinical stage, in which brain metabolic alterations precede the symptoms onset. Therefore, an early and proper diagnosis of AD is essential for prevention and therapeutic evaluation. Current diagnosis and staging procedures rely on neuroimaging techniques, such as positron emission tomography (PET), which require intensity normalization to ensure the correct interpretation of the data. However, the variety of normalization methods and lack of standardization produce a major drawback regarding the proper interpretation of AD data by these techniques.

Method

We formally analyzed the performance of six well‐established intensity normalization methods (proportional scaling and data‐driven approaches) on brain PET images of fluorodeoxyglucose (FDG) obtained from healthy mice (N = 10). The accuracy of each method was determined based on the recovery of different computer‐simulated patterns in the FDG‐PET scans achieved with voxel‐wise analyses. Furthermore, we applied this newly acquired knowledge to evaluate differences in cerebral FDG uptake in 16 and 62 weeks‐old AD mice (N16w = 7; N62w = 10) of the TgCRND8 model, and their wild‐type littermates (N16w = 10; N62w = 5).

Result

Our results showed a remarkable ambivalence between and within each normalization method under different simulation scenarios. They supported the use of an iterative histogram‐based normalization (iHN, Martí Fuster et al., 2013) when the data compared is not clearly biased (Fig. 1B). Nonetheless, the reference cluster technique based on preserved regions (Yakushev et al., 2009) stood out for patterns with large and accentuated bias in the data (Fig. 1A). Thus, the iHN method helped us to evaluate AD‐related differences in real FDG‐PET images, identifying genotypic effects that change with age (Fig. 1C).

Conclusion

This study evidences the crucial importance of understanding the distribution of the radiotracer uptake in PET studies before selecting an intensity normalization strategy. Furthermore, our results provide simple and accurate guidelines to help to choose a proper normalization method in preclinical PET imaging, demonstrating its applicability in real data from AD mice. References: Martí Fuster et al., 2013. Neuroinformatics 11(1), 77‐89. Yakushev et al., 2009. Neuroimage 44(1), 43‐50. Funding: EU Joint Programme – Neurodegenerative Disease Research (JPND), BrightFocus Foundation, and JDC2022‐048922‐I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.
Predicting Cognitive Decline: A Comprehensive Study On relative Brain Age, Cognitive Reserve, And Longitudinal Changes
Authors: Motaghi Mahboubeh , Potvin Olivier , Turcotte Valérie , Beheshti Iman , Duchesne Simon
Published: January 2025

Background

Brain age is a metric that can be determined through anatomical measurements obtained from MRI scans. Relative brain age (RBA) reflects the difference in brain morphometry for an individual relative to a comparison group at a similar chronological age. Increased RBA was found related to poor physical health, neurodegeneration, increase RBA, cognitive decline, and mortality risk. The concept of cognitive reserve (CR), operationalized by merging education, complexity of occupation, and verbal IQ, explains on the other hand how individuals seem able to tolerate the impact of age‐related and/or neurodegenerative changes. This study aimed at exploring the relationship between RBA, CR, and cognitive performance.

Method

We assessed cognitively healthy and MCI individuals from the ADNI study on four cognitive domains (verbal episodic memory; language and semantic memory; attention; executive functions) at each follow‐up visit up to 5 years. First, we employed robust statistical models, including Generalized Linear Models(GLM), to investigate the association between baseline variables and cognitive performance changes over different follow‐up intervals. Secondly, we used support vector regression models to predict cognitive performance over multiple follow‐up periods. We employed 10‐fold cross‐validation to assess the predictive accuracy of the model. Finally, we used GLM to assess the link between RBA and CR with the speed of cognitive changes during a 36‐month period.

Result

Our findings reveal that in MCI group, RBA significantly contributes to the variance in verbal episodic memory changes across various time intervals. To predict cognitive performance, our support vector regression model demonstrated robust performance, yielding low mean absolute errors for episodic memory (0.305 to 0.523), attention (0.392 to 0.661), language and semantic memory (0.369 to 0.537), and executive function (0.386 to 0.521) across diverse follow‐up periods. RBA was further associated with a slower decline in verbal episodic memory, attention, language and semantic memory. These findings underscore the significant predictive role of RBA in estimating the speed of decline in cognitive performance within the MCI group.

Conclusion

We have shown how RBA interacts with CR, in shaping cognitive performance and aging trajectories. Our findings underscore the pivotal role of RBA in influencing future cognitive decline, offering valuable insights for early clinical detection.
Phosphorylated tau is more closely associated with the spatial extent of tauopathy than with tau load
Authors: Macedo Arthur C. , Rahmouni Nesrine , Therriault Joseph , Wang Yi‐Ting , Servaes Stijn , Tissot Cécile , Hall Brandon J , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Trudel Lydia , Lussier Firoza Z , Brum Wagner Scheeren , Zimmer Eduardo R. , Triana‐Baltzer Gallen , Kolb Hartmuth C. , Ashton Nicholas J. , Benedet Andrea L. , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Pascoal Tharick A. , Rosa‐Neto Pedro
Published: January 2025

Background

Recent evidence indicated that cognitive impairment is more closely associated with the spatial extent of tauopathy (SEOT) than with tau load. It remains unclear whether this is also true for other markers of Alzheimer’s disease (AD) severity, such as fluid levels of phosphorylated tau (pTau). Here, we compared the link between fluid pTau and the SEOT and tau load in the brain, as assessed by PET.

Method

We studied individuals across the aging and AD continuum or with non‐AD neurodegenerative diseases from the TRIAD and ADNI cohorts. TRIAD participants underwent [18F]MK6240 tau‐PET and CSF and plasma pTau 181, 217, and 231. ADNI participants were evaluated with [18F]AV1451 tau‐PET and CSF pTau181. We calculated standard uptake value ratios (SUVR) as a proxy of tau load, and the proportion of abnormal voxels as a measure of the SEOT. Abnormal voxels were those 2.5 SD higher than the mean of a group of young adults. Spearman’s correlations assessed the associations of pTau with SEOT and tau load in a temporal meta‐region of interest (ROI) and Braak‐like cumulative ROIs. Correlation coefficients were compared in R using the cocor package.

Result

We included 325 participants from TRIAD (mean [SD] age, 68 [9.8] years) and 417 from ADNI (mean [SD] age, 71.8 [9.0] years). All pTau biomarkers showed significant correlations with SEOT and tau load in all ROIs (Fig. 1A‐B). All pTau epitopes were more closely associated with SEOT than with tau load in Braak I‐V and I‐VI (Fig. 2). In TRIAD, this was also observed in Braak I‐III and I‐IV for CSF pTau 217 and 231, and in Braak I‐II for plasma pTau 231. In ADNI, this was also seen for CSF pTau181 in Braak I‐II and I‐III. Tau load did not correlate better with pTau in any ROI.

Conclusion

PTau biomarkers seem to inform better on SEOT than on tau load. There is a trend for better performance of SEOT as an imaging correlate of pTau when studying late Braak stages. Future studies should investigate the relationship between these measures and the clinicopathological progression of AD.
Dissociation of Structural and Functional Changes in Alzheimer’s Disease and Mild Cognitive Impairment
Authors: Dang Annie , Wang Di , Towne Jonathan , Habes Mohamad , Fox Peter T
Published: January 2025

Background

The Amyloid‐Tau‐Neurodegeneration (ATN) biomarker framework for Alzheimer’s disease (AD) indicates binary (presence/absence) designations for each type of pathology, without regard for anatomical distribution. Neurodegeneration is designated as positive if atrophy or hypometabolism are found on imaging. However, Clifford Jack et al., 2016 noted that atrophy and hypometabolism were differently distributed and referenced each to different co‐localized pathologies. Thus, there exists a need to further characterize atrophy and hypometabolic changes in AD, with the goal of advancing the application of anatomically‐based biomarkers in the ATN framework.

Method

Query of the BrainMap databases of published, group‐wise neuroimaging, case‐control contrasts was used to identify AD and mild cognitive impairment (MCI) studies for meta‐analysis. The voxel‐based morphometry (VBM) and voxel‐based physiology (VBP) databases were used to identify studies involving atrophy and hypometabolism respectively. 157 VBM contrasts (110 AD, 47 MCI) and 146 VBP contrasts (88 AD, 58 MCI) were identified. Activation likelihood estimation coordinate‐based meta‐analysis was performed separately for VBM and VBP, to identify cross‐study convergence of brain alteration patterns. Mango was then used to visualize results and quantify spatial overlap between VBM and VBP.

Result

Structural (atrophy) and functional (hypophysiology) neurodegenerations in AD/MCI exhibit markedly different neuroanatomical distributions (Figure 1). Structural abnormalities chiefly involve the bilateral hippocampus and bilateral temporal lobes; functional abnormalities chiefly involve the bilateral parietal lobes and posterior cingulate. There is a small overlap (2184 mm3) between VBM and VBP, accounting for 10.1% of VBM and 7.1% of VBP.

Conclusion

VBM and VBP patterns of alteration appear distinct, aligning with the anterior and posterior default mode network respectively. This dissociation may reflect distinct underlying neuropathologies. We suggest that this knowledge can be used to advance the application of anatomically‐based biomarkers in the ATN framework. Network modeling of VBM and VBP data is currently ongoing.