bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cerebrovascular reactivity (CVR) MRI as a biomarker for cerebral small vessel disease (SVD) related cognitive decline: Multi‐site validation in the MarkVCID Consortium
Authors: Liu Peiying , Lin Zixuan , Hazel Kaisha , Pottanat George , Xu Cuimei , Jiang Dengrong , Pillai Jay J , Lucke Emma , Bauer Christopher E. , Gold Brian T , Greenberg Steven M. , Helmer Karl G , Jann Kay , Jicha Gregory A , Kramer Joel H. , Maillard Pauline , Mulavelil Rachel M. , Satizabal Claudia L , Schwab Kristin , Seshadri Sudha , Singh Herpreet , Velarde Angel G , Wang Danny JJ , Kalyani Rita , Moghekar Abhay , Rosenberg Paul B. , Yasar Sevil , Albert Marilyn S. , Lu Hanzhang 
Published: January 2025

AbstractBackgroundCerebral small vessel disease (SVD) related vascular contributions represent a major factor contributing to cognitive decline and dementia (VCID) in older adults. However, there has not been a validated biomarker for the diagnosis and treatment monitoring of this condition. Recently, the US National Institute on Neurological Disorders and Stroke (NINDS) funded the MarkVCID Consortium to identify and validate clinical‐trial‐ready biomarkers for VCID. Cerebrovascular reactivity (CVR) MRI is one of the selected biomarkers that underwent multi‐site testing in the Consortium. The present study aimed to report the relationship between CVR and cognitive function at independent sites, based on a pre‐specified hypothesis.MethodCVR, the ability of cerebral small vessels to dilate upon stimulus, is thought to directly reflect physiological function of the brain microvasculature. Based on previous single‐site findings, the pre‐defined hypothesis was that CVR will be associated with the global cognitive function measured by Montreal Cognitive Assessment (MoCA) after adjusting for age, sex, and education, and this association will be observed in data collected and analyzed at each individual site. A total of 264 older participants from three sites were included (Table 1). Each site performed an identical CVR MRI procedure using 5% CO2 inhalation, and a standardized clinical and cognitive evaluation. Multi‐linear regression analysis was conducted on a site‐by‐site basis to examine the pre‐defined hypothesis.ResultGray‐matter CVR showed a positive association with MoCA score after adjustment for age, sex, and education, in which participants with higher CVR had higher MoCA scores. This relationship was reproduced at each site (Fig.1, p<0.05 for each), confirming our pre‐specified hypothesis. In the secondary analysis of all data together, higher gray‐matter CVR was found to be significantly associated with better executive function measure of item response theory (IRT) score (ß = 2.87, p = 0.003).ConclusionThe present study evaluated the relationship between CVR and cognition in a multi‐center setting. CVR was found to be positively associated with global cognitive function measured by the MoCA, which was shown to be reproducible across different sites with diverse cohorts. These findings support the utility of CVR as a biomarker in future clinical trials of SVD and VCID.
Differences in amyloid positivity rates among racial groups with cognitive impairment in a suburban‐rural Southeast community
Authors: Chacon Eduardo Garcia , Woodring Annalise C , Crane Monica K 
Published: January 2025

AbstractBackgroundUnderrepresented racial and ethnic groups have higher rates of clinical dementia symptoms and are given clinical diagnoses of Alzheimer’s disease without confirmatory AD biomarkers.MethodWe compared amyloid PET positivity in cohort of all rural and suburban Southeastern participants from a single clinical practice who met appropriate‐use criteria for amyloid PET imaging between January 2020‐December 2024. We used a paired nominal date test McNemar test to compare amyloid PET positivity proportions between matched racial and ethnic groups and multivariable logistic regression to assess the odds of having a positive amyloid PET scan.ResultData from 94 individuals (6 Asian/Pacific Islander(Other), 42 Black, 7 Hispanic, and 39 White) with MCI or dementia and amyloid PET were analyzed between April 2020 and December 2023. The median (range) age of participants was 75 (68‐82) years; 51 participants (54.3%) were female and 43 (45.7%) were male. For patient amyloid status, 49 participants (52.1%) had a validated negative amyloid result, and 45 participants (47.9%) had a validated positive amyloid result. In the optimal 1:1 matching analysis (n = 81, White participants had a greater proportion of positive amyloid PET scans analyzed but did not achieve statistical significance when compared with Black participants (24 of 39; 61.5%; 95% CI, 0.38‐0.72 vs 14 of 42; 33.3%; 95% CI, 0.19‐0.51, respectively) p>0.05. In the adjusted model, the odds of having a positive amyloid PET scan were lower for Black participants (OR, 0.313; 95% CI, 0.126‐0.776; P = 0.012), and Hispanic participants (OR, 0.33; 95% CI, 0.163‐4.253; P>0.05) compared with White participants.ConclusionThe proportion of amyloid positive PET scan was greater among White as compared with Black and Hispanic participants when controlling for social and demographic factors. These findings may reflect differences in underlying pathology and raises concern that other factors may be contributing to higher levels of cognitive impairment in underrepresented groups.
Impact of AI‐based segmentation on PET analyses
Authors: Bracoud Luc , Cajgfinger Thomas , Rousset Olivier , Ingalhalikar Madhura , Conklin Chris , Lehmann Manja , Schaerer Joel , Scott David , Suhy Joyce 
Published: January 2025

AbstractBackgroundBrain MRI segmentation is required for quantitative PET analysis, in order to derive regional uptake and calculate uptake ratio relative to reference regions. FreeSurfer has been a popular method but is being supplanted by faster and more robust AI‐driven methods. The objective of this work is to confirm that the use of Clario’s novel AI segmentation method, whose impact was assessed towards various MRI endpoints, is also valid in the context of PET quantification.Method507 subjects from ADNI were selected, including normal controls, subjective memory complainers, early/late MCI and AD subjects. Besides, 210 were selected with available test/re‐test data. For each, a pair of good quality 3DT1 MRI and Amyloid PET scans were available. 3DT1 data were segmented with both FreeSurfer v6 (FS) and Clario’s AI method (AI), and registered to PET space for calculation of Standard Uptake Value (SUV) in a set of cortical regions (frontal, posterior cingulate, lateral parietal and lateral temporal) and reference regions (cerebellar grey and whole cerebellum). SUVR (ratio to reference region) was calculated for both. Results were also converted to Centiloid (CL) units. SUV and SUVR values were compared across segmentation methods via Bland‐Altmann plots and correlated with Pearson’s coefficient. The ability to distinguish between disease severity stages was assessed by ROC analysis using global AUC. Finally, classification as amyloid (Aβ) positive or negative was assessed by setting a cut‐off of 20.1 CL and looking at Cohen’s Kappa.ResultsSUV and SUVR values were highly and significantly correlated between FS and AI methods (r>0.99, p<0.001) and for test/re‐test, for all regions investigated, with a difference that did not exceed 0.41% on average (SD<1.38%). Global AUC was comparable for both methods and for test/retest (average gAUC = 0.62). Aβ classification was near‐perfect across methods (κ = 0.98) and between test/retest data (κFS = 0.97, κAI = 0.99).ConclusionComparing FreeSurfer and Clario’s AI‐based MRI segmentation method, no impact was found on subsequent PET analyses and test/retest performance was equivalent. These results support the use of either method for PET quantification. For future trials, the AI method could either be used as the primary method, or as a back‐up when traditional FS segmentation fails.
White matter hyperintensity onset, trajectories, and associations with cognitive decline in the presence of amyloid and tau
Authors: Du Lianlian , Langhough Rebecca E. , Hermann Bruce P , Betthauser Tobey J. , Planalp Elizabeth M. , Jonaitis Erin M. , Reyes Ramiro Eduardo Rea , Rivera‐Rivera Leonardo A. , Cody Karly Alex , Chin Nathaniel A. , Cadman Robert V. , Johnson Kevin M. , Field Aaron S , Rowley Howard A. , Pompa Christian C. , Hudson Sarah C. , Ryther Brenda , Sparks Lia J. , Miess Beryl , Norris Nia , Ramsey Celena M , Mueller Kimberly D , Asthana Sanjay , Eisenmenger Laura B. , Christian Bradley T. , Johnson Sterling C. 
Published: January 2025

AbstractBackgroundCognitive decline is often influenced by Alzheimer’s disease (AD) pathology (e.g., beta‐amyloid burden) and other pathology (e.g., vascular abnormalities). Amyloid onset and chronicity estimation using Sampled Iterative Local Approximation (SILA), enhanced our understanding of AD progression and its preclinical phase. This study has three aims: 1) estimate White Matter Hyperintensities (WMH) onset and chronicity; 2) assess whether baseline WMH chronicity/burden moderates the association between Aß chronicity/burden and Clinical Dementia Rating‐sum of boxes (CDR‐SB) trajectories; 3) explore whether tau burden mediates WMH and amyloid associations with CDR‐SB.MethodParticipants were from the University of Wisconsin WRAP and ADRC cohorts and had completed at least one T1‐weighted and T2‐weighted FLAIR scans (Aim 1: n = 877, age 43‐93y). WMH values were aligned to a duration scale using SILA and WMH positivity threshold of ∼2.06 mL for WMH+ chronicity = 0. We compared mixed effects models with up to cubic time (age vs WMH chronicity) terms for characterizing WMH trajectories. We also examined baseline WMH burden*amyloid burden at each CDR‐SB assessment (PiB A+ DVR threshold ∼17CL; linear and quadratic amyloid burden) relative to longitudinal CDR‐SB (Aim 2; n = 426), and tau PET SUVR’s mediating effects on WMH*amyloid associations with last CDR‐SB (Aim 3; n = 385; meta‐temporal (MTC) SUVR; florquinitau tracer).ResultMean(SD) age at baseline and last MRI were 65.36(8.49) and 67.26(8.14) years, respectively (sample details in Table 1). Comparisons of simple slopes (95%CI) show overlapping annualized WMH‐ slope estimates using age (0.028(0.022,0.034)) or chronicity (0.037(0.033,0.040)); in contrast, the WMH+ slopes are 2.26 times larger when aligned to WMH chronicity vs age (WMH+: 0.042(0.036,0.047), WMH‐: (0.095(0.091,0.10); Figure 1A&B). Whether biomarker “burden” was modeled as ±, estimated DVR, or chronicity (Figure 1C&D), significant interactions showed a synergistic effect of WMH and amyloid on accelerated CDR‐SB trajectory. Moderated mediation models revealed MTC‐tau accumulation partially mediated (∼65%) the synergistic effect of WMH and amyloid on last CDR‐SB (Figure 2).ConclusionWMH accumulation follows a predictable trajectory post‐onset and appears to exacerbate cognitive decline in those with amyloid pathology. Future analyses will further elucidate the complex relationships between vascular risk, amyloid, tau accumulation and cognitive decline.
Comparison of MRI T2 FLAIR White Matter Hyperintensity Segmentation Algorithms in Clinical Trials
Authors: Scheel Norman , Hubert Josh , Baker Joshua H. , Fernandez Zachary , Yanev Pavel G. , Stowe Ann M , Vongpatanasin Wanpen , Zhang Rong , Zhu David C 
Published: January 2025

AbstractBackgroundWhite Matter Hyperintensities (WMH) appear on T2‐weighted Fluid‐Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI) and are an important biomarker of cerebral small vessel disease (CSVD), cognitive decline, and stroke. However, manual delineation is laborious and bias‐prone, while automated segmentation has proven challenging. With the recent conclusion of the MICCAI‐Society WMH segmentation challenge, and our large clinical trials, “Risk Reduction for Alzheimer’s Disease” (rrAD) and “Hypertension, Intracranial Pulsatility and Brain Amyloid‐beta Clearance in older adults” (HIPAC), we investigated the differences in the total WMH volume segmented by various algorithms to ensure the extraction of accurate and meaningful image‐derived phenotypes.MethodsThe rrAD trial provides 512 2D axial T2 FLAIR scans, fat saturation off (ages 60‐85). The HIPAC study provides 84 3D T2 FLAIR scans, fat saturation on (ages 55‐79). The MICCAI WMH challenge provides a test set of 110 scans of 2D axial T2 FLAIR images, 30 without, and 80 with fat saturation. Our clinical trials and the MICCAI datasets comprise data from over 700 subjects, across 10 scanners and diverse sequences. Three of the best‐performing MICCAI algorithms were selected: PGS, Sysu‐Media‐2, FMRIB‐TrUE‐Net2, as well as the LPA algorithm from SPM. Segmentation results were randomized, and fully blinded expert raters scored a subset of 120 scans.ResultsFigure 1 depicts the average normalized images for each T2 FLAIR contrast. The MICCAI test set shows a median WMH burden of 10‐17 ml, while rrAD subjects presented with relative medians of ˜3‐4 ml, and HIPAC subjects with ˜2‐3 ml. Figure 2 shows the WMH volume for each segmentation algorithm and dataset. For HIPAC Sysu‐Media‐2 had high false pickup within grey matter regions, while LPA produced high false pickup at the posterior septum pellucidum. PGS scored best in manual ratings.ConclusionsMRI sequences that use fat saturation change the T2 FLAIR image contrast substantially and impact segmentation performance, we observed better performance with fat saturation off. The MICCAI challenge algorithms outperformed classical methods (PGS leading), detecting small lesions, away from the ventricles. As WMH distribution, shape, and longitudinal tracking are likely more sensitive biomarkers than volume alone, further analysis is warranted.
Immunoassay and Immunohistochemistry of TDP‐43 and the association with annual rate of hippocampal atrophy in Alzheimer's disease
Authors: Youssef Hossam , Gatto Rodolfo G. , Pham Nha Trang Thu , Petersen Ronald C. , Dickson Dennis W. , Petrucelli Leonard , Whitwell Jennifer L. , Prudencio Mercedes , Josephs Keith A. 
Published: January 2025

AbstractBackgroundTransactive response DNA‐binding protein of 43 kDa (TDP‐43) is implicated in numerous neurodegenerative diseases, including Alzheimer’s disease (AD). Immunohistochemically stained TDP‐43 is linked to faster rates of hippocampal atrophy (HA) in AD. However, it is unclear whether phosphorylated TDP‐43 (pTDP‐43) measured using an immunoassay is also associated with HA. We explored associations between pTDP‐43 levels, TDP‐43 immunohistochemistry, and annual rate of HA in AD. We hypothesized that higher pTDP‐43 levels would be associated with positive TDP‐43 immunohistochemistry, and faster rates of HA.MethodThirty‐seven cognitively impaired participants from the Mayo Clinic Alzheimer’s Disease Research Center underwent serial brain MRIs and brain autopsy. Hippocampal TDP‐43 was detected histochemically, and pTDP‐43 was quantified using biochemical assay. Hippocampal volumes were measured using the longitudinal pipeline in FreeSurfer‐7 and annualized HA rates were calculated. Linear regression analyzed the relationship between HA and pTDP‐43 biochemical assay levels. pTDP‐43 was categorized as positive (=400) or negative (<400), and logistic regression tested the association of these levels with HA rates. The correlation between TDP‐43 immunohistochemistry status and biochemical assay levels was evaluated using descriptive statistics. All analyses were adjusted for potential confounders.ResultsThe mean age at death was 83.5 (±9.9) years with 49% females. Median scan interval was 3‐years. A negative relationship was observed between age at death and annual HA rate (ß=‐3.1; 95% CI [‐5.3,‐0.9]; p=0.007), and a positive relationship was observed between pTDP‐43 level and annual HA rate (ß=0.02; 95% CI [0.003,0.03]; p=0.015). A one‐unit increase in HA rate was associated with a 3% increase in the odds of being pTDP‐43 positive (=400) (odds ratio=1.03; CI [1.007,1.05]; p=0.014). Cases that tested positive for Immunohistochemistry TDP‐43 had significantly higher mean pTDP‐43 immunoassay levels (1508 ± 2072 vs. 202 ± 131; p=0.008). More cases with positive IHC TDP‐43 surpassed the pTDP‐43 immunoassay cut‐off level of 400 vs the negative participants (p=0.003).ConclusionHigher pTDP‐43 levels in the hippocampus are linked to faster HA in AD and correlated with immunohistochemistry TDP‐43 positivity. These findings underscore the role of TDP‐43 in AD neurodegeneration. The biological and clinical implications of these observations necessitate further exploration in subsequent studies.
PET imaging utility of a novel Aβ‐tracking PET radiotracer, [18F]FC119S in aged vervet monkeys
Authors: Bhoopal Bhuvanachandra , Frye Brett M. , Bansode Avinash , Miller Mack , Damuka Naresh , Gollepalli Krishna Kumar , Barcus Richard A. , Lockhart Samuel N. , Sutphen Courtney L. , Jorgensen Matthew J. , Craft Suzanne , Register Thomas C. , Whitlow Christopher T , Shively Carol A. , Sai Kiran K. Solingapuram 
Published: January 2025

AbstractBackgroundOlder vervet monkeys are an excellent model for studying age‐associated Aß deposition; however, they have high proportions of low‐affinity Aß sites compared to human brains. Commonly used Aß PET radiotracers are most useful in detecting high affinity Aß fibrils. Measuring real‐time levels of low affinity Aß fibrils through PET provides critical information of early AD progression. The radiotracer [18F]FC119S has high binding strength for Aß1‐42 protein aggregates (0.16 nM) and AD brain homogenates (13‐15 nM). A pilot clinical trial showed significantly higher hippocampal and cortical uptake in AD patients over cognitively normal participants. Here we report the fully‐automated [18F]FC119S radiochemistry and preliminary imaging and fluid biomarker correlation data in aged vervet monkeys.MethodsRadiochemistry followed [18F]¯‐assisted nucleophilic substitution of mesityl precursor and deprotection of Boc group. [18F]FC119S(∼0.37GBq)‐based brain PET (0‐120min) and T1‐weighted whole‐brain MRI scans were conducted in aged vervets (19‐27 y, n = 14). CSF and plasma Aß40, Aß42, pTau181, and NfL were measured in samples taken close to the time of PET. Standard Uptake Values with cerebellum as reference (SUVr) and time‐activity‐curves (TACs) were calculated from PET/MR (Fig 1). SUVrs of anterior and posterior cingulate, hippocampus, frontal, parietal, temporal, and occipital lobes were correlated with age, MRI outcomes (grey and white matter volumes), and fluid biomarker data.Results[18F]FC119S production was highly reproducible (n>25 runs) in high (>97%) radiochemical purities, and molar activities (∼140 GBq/µmol). TACs showed favorable washout kinetics from all brain regions. SUVrs of hippocampus and cortical parietal lobe positively correlated with age and SUVrs of all the regions analyzed negatively correlated with MRI deep gray and white matter volumes. Among the high SUVr regions, cortical anterior cingulate SUVr was highly positively correlated (Fig 2) with CSF pTau181 (r = 0.78, **p = 0.002).ConclusionPreliminary [18F]FC119S PET evaluations agrees with human PET data in vervets with high uptake in regions associated with Aß deposition. Among all fluid biomarkers analyzed, CSF pTau181 was the best correlate of all the brain PET SUVrs. Association of Aß PET imaging data with synaptic density and microtubule data in the same cohort is being evaluated to explore the potential neurodegeneration pathways in aged vervet monkeys.
Perivascular Space Analysis: A Possible Tool for Predicting Cerebral Amyloid Angiopathy in Pathology‐Confirmed Cases
Authors: Fotouhi Maryam , Sheikh‐Bahaei Nasim , Choupan Jeiran 
Published: January 2025

AbstractBackgroundDiagnosis of cerebral amyloid angiopathy (CAA) presents a significant challenge in determining whether to propose anti‐amyloid treatment plans. The identification of perivascular spaces (PVS) through MRI serves as a possible strategy to elucidate the physiopathological interconnections between the brain’s clearance mechanisms and the accumulation of amyloid. This study endeavors to the association between PVS morphology and CAA pathology.MethodsT1‐weighted and T2‐FLAIR MRI data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Demographic details (sex, age, post‐mortem interval) and ADNI pathologic scores were gathered. Histological images provided scores for cerebral amyloid angiopathy. Leveraging our on the last premortem MRI, we automatically mapped regional PVS in white matter and basal ganglia. analysis was then employed to investigate whether premortem regional PVS volume fractions serve as predictors for postmortem CAA pathology, accounting for age, sex, and post‐mortem interval.ResultsThe investigation revealed notable predictive associations between specific occipital regions' PVS volume fractions and the presence of amyloid angiopathy pathology, after multiple comparison correction. Specifically, the study identified the right cuneus PVS volume fraction as a robust predictor for the manifestation of amyloid angiopathy pathology (t = 3.12, p = 0.001). Similarly, the left precuneus PVS volume fraction emerged as a statistically significant predictor (t = 2.78, p = 0.005).ConclusionThis study presents a noteworthy advancement in amyloid angiopathy diagnosis, employing standard MRI analysis of perivascular spaces. The identified correlation offers a pragmatic means to enhance diagnostic accuracy without complexity. This research underscores the potential applicability of PVS analysis in routine clinical practice for early CAA detection, contributing significantly to the practical improvement of CAA diagnostics. Reference: 1. Sepehrband, F., Barisano, G., Sheikh‐Bahaei, N. et al. Image processing approaches to enhance perivascular space visibility and quantification using MRI. Sci Rep 9, 12351 (2019). https://doi.org/10.1038/s41598‐019‐48910‐x