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Background Cerebrospinal fluid (CSF) dynamics are increasingly studied in aging and neurological disorders. Models of CSF-mediated waste clearance suggest that altered CSF dynamics could play a role in the accumulation of toxic waste in the CNS, with implications for Alzheimer’s disease and other proteinopathies. Therefore, approaches that enable quantitative and volumetric assessment of CSF flow velocities could be of value. In this study we demonstrate the feasibility of 4D flow MRI for simultaneous assessment of CSF dynamics throughout the ventricular system, and evaluate associations to arterial pulsatility, ventricular volumes, and age. Methods In a cognitively unimpaired cohort ( N  = 43; age 41–83 years), cardiac-resolved 4D flow MRI CSF velocities were obtained in the lateral ventricles (LV), foramens of Monro, third and fourth ventricles (V3 and V4), the cerebral aqueduct (CA) and the spinal canal (SC), using a velocity encoding (venc) of 5 cm/s. Cerebral blood flow pulsatility was also assessed with 4D flow (venc = 80 cm/s), and CSF volumes were obtained from T1- and T2-weighted MRI. Multiple linear regression was used to assess effects of age, ventricular volumes, and arterial pulsatility on CSF velocities. Results Cardiac-driven CSF dynamics were observed in all CSF spaces, with region-averaged velocity range and root-mean-square (RMS) velocity encompassing from very low in the LVs (RMS 0.25 ± 0.08; range 0.85 ± 0.28 mm/s) to relatively high in the CA (RMS 6.29 ± 2.87; range 18.6 ± 15.2 mm/s). In the regression models, CSF velocity was significantly related to age in 5/6 regions, to CSF space volume in 2/3 regions, and to arterial pulsatility in 3/6 regions. Group-averaged waveforms indicated distinct CSF flow propagation delays throughout CSF spaces, particularly between the SC and LVs. Conclusions Our findings show that 4D flow MRI enables assessment of CSF dynamics throughout the ventricular system, and captures independent effects of age, CSF space morphology, and arterial pulsatility on CSF motion.

Introduction This work investigated the relationship between cerebrovascular disease (CVD) markers and Alzheimer's disease (AD) biomarkers of amyloid beta deposition, and neurofibrillary tau tangles in subjects spanning the AD clinical spectrum. Methods A total of 136 subjects participated in this study. Four groups were established based on AD biomarker positivity from positron emission tomography (amyloid [A] and tau [T]) and clinical diagnosis (cognitively normal [CN] and impaired [IM]). CVD markers were derived from structural and quantitative magnetic resonance imaging data. Results Transcapillary pulse wave delay was significantly longer in controls compared to AT biomarker–confirmed groups (A+/T–/CN P < .001, A+/T+/CN P < .001, A+/T+/IM P = .003). Intracranial low‐frequency oscillations were diminished in AT biomarker–confirmed groups both CN and impaired (A+/T–/CN P = .039, A+/T+/CN P = .007, A+/T+/IM P = .011). A significantly higher presence of microhemorrhages was measured in A+/T+/CN compared to controls (P = .006). Discussion Cerebrovascular markers indicate increased vessel stiffness and reduced vasomotion in AT biomarker–positive subjects during preclinical AD.

INTRODUCTION Amyloid (A) and vascular (V) pathologies often co‐occur and progress over decades. We leveraged chronicity, defined as the years above a biomarker‐positivity threshold, to examine how the timing of A and V relates to cognitive decline. METHODS We modeled Clinical Dementia Rating–Sum of Boxes (CDR‐SB) trajectories in n = 558 participants with [C‐11] Pittsburgh compound B positron emission tomography (PET), magnetic resonance imaging–derived white matter hyperintensities (WMHs), and longitudinal CDR assessments. In n = 500 with MK6240 PET, we tested whether tau mediates A–V associations with CDR‐SB in a moderated mediation framework. RESULTS Whether biomarker “burden” was modeled as chronicity (A+years, V+years), or estimated amyloid and WMH at CDR visits, significant interactions showed a synergistic effect of WMHs and amyloid on accelerated CDR‐SB trajectories. Tau significantly mediated these associations. DISCUSSION Operationalizing chronicity clarifies how long individuals have exceeded A and V thresholds and improves clinical interpretability. WMH accumulation exacerbates amyloid‐related cognitive decline. Longitudinal tau imaging could further inform staging and intervention timing. Highlights Longer amyloid (A) and vascular disease (V) chronicity were associated with faster cognitive decline, emphasizing the importance of considering chronic exposure to these pathologies. Individuals with higher V burden experienced a steeper decline in cognition in the presence of A pathology, highlighting the interaction between V and neurodegenerative processes. Progression from early to mild dementia was faster with greater white matter hyperintensity chronicity, even when A duration was held constant, supporting the idea that V pathology amplifies the clinical impact of amyloid in Alzheimer's disease. Tau accumulation played a significant mediating role in linking A and V burden to cognitive decline, suggesting that tau pathology is a critical downstream factor in symptom progression. Person‐level chronicity estimates of A and V provide a more precise understanding of cognitive decline trajectories, offering insights for early intervention strategies.

Cognitive 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 Aβ 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. Participants 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). Mean(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). WMH 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.

BackgroundHyperpolarized helium 3 magnetic resonance imaging (3He MRI) is useful for investigating pulmonary physiology of pediatric asthma, but a detailed assessment of the safety profile of this agent has not been performed in children.ObjectiveTo evaluate the safety of 3He MRI in children and adolescents with asthma.Materials and methodsThis was a retrospective observational study. 3He MRI was performed in 66 pediatric patients (mean age 12.9 years, range 8–18 years, 38 male, 28 female) between 2007 and 2017. Fifty-five patients received a single repeated examination and five received two repeated examinations. We assessed a total of 127 3He MRI exams. Heart rate, respiratory rate and pulse oximetry measured oxygen saturation (SpO2) were recorded before, during (2 min and 5 min after gas inhalation) and 1 h after MRI. Blood pressure was obtained before and after MRI. Any subjective symptoms were also noted. Changes in vital signs were tested for significance during the exam and divided into three subject age groups (8–12 years, 13–15 years, 16–18 years) using linear mixed-effects models.ResultsThere were no serious adverse events, but three minor adverse events (2.3%; headache, dizziness and mild hypoxia) were reported. We found statistically significant increases in heart rate and SpO2 after 3He MRI. The youngest age group (8–12 years) had an increased heart rate and a decreased respiratory rate at 2 min and 5 min after 3H inhalation, and an increased SpO2 post MRI.ConclusionThe use of 3He MRI is safe in children and adolescents with asthma.

Introduction While it is generally appreciated that amyloid precedes symptomatic Alzheimer's disease (AD) by decades, a greater understanding of this timeline may increase prognostic accuracy, planning, and care of persons who are on the AD continuum. Methods We examined trajectories of Clinical Dementia Rating–Sum of Boxes (CDR‐SB) relative to estimated years of amyloid positivity (A+) in n = 123 participants who were all A+ based on [C‐11]Pittsburgh compound B positron emission tomography. Results The average amyloid chronicity at CDR‐SB of 2.5 was 20.1 years. The average trajectory of CDR‐SB accelerated after 10 years of elevated amyloid and varied greatly between 10 and 30 years. Exploratory analyses suggested that older age and higher volume of white matter hyperintensities shortened the interval between amyloid onset and cognitive impairment. Discussion The recontextualization of amyloid burden into the time domain will facilitate studies of disease progression, the influence of co‐pathology, and factors that hasten or slow cognitive impairment.

Background Cerebrospinal fluid (CSF) dynamics are increasingly studied to understand potential pathologic coupling with neurological disorders. In Alzheimer’s disease (AD), CSF dynamics may be altered secondary to AD‐related atrophy and enlarged CSF spaces. Additionally, animal studies suggest that altered CSF dynamics could impair clearance of metabolic waste, leading to accumulation of amyloid‐beta (Aß). However, assessment of human CSF dynamics has typically been limited to the cerebral aqueduct using 2D phase contrast MRI. In this work, we used 4D flow MRI to enable volumetric characterization of CSF flow dynamics in the human brain, to study its associations with CSF morphology and Aß burden from 11C‐PiB PET. Method Cardiac‐resolved 4D flow MRI was used to assess CSF motion in N=69 participants (age 69±8 years; 17 Aß+) from the Wisconsin Registry for Alzheimer’s Prevention, using a clinical 3T scanner (GE Premier) and a velocity encoding of 5 cm/s. Region‐averaged velocity waveforms were acquired in the spinal canal (SC), fourth ventricle (4V), cerebral aqueduct (CA), third ventricle (3V), the foramens of Monro (FMo), and the lateral ventricles (LV), segmented from T1‐weighted volumes. The waveforms were characterized by root‐mean‐square (RMS) velocity and were evaluated in relation to total CSF volume and Aß status from an established PiB global distribution volume ratio threshold >1.16. Result Velocity waveforms demonstrated cardiac‐pulsatility in all regions of interest (ROIs) (Figure 1), with RMS velocities ranging from high in the SC (6.56±2.29 mm/s) to low in the lateral ventricles (right LV 0.31±0.15 mm/s). RMS velocities were related to total CSF volume for the SC (R=‐0.24; p=0.043), CA (R=0.27; p=0.034), 3V (R=‐0.28; p=0.018), left LV (R=‐0.39; p=0.002), and right LV (R=‐0.35; p=0.009) (Figure 2). In preliminary analysis, RMS velocities were higher in Aß+ compared to Aß‐ for the SC (p=0.048) and right LV (p=0.024) (Figure 3). Conclusion 4D CSF flow MRI is feasible for the assessment of CSF dynamics throughout the ventricular system. Total CSF volume was related to RMS velocities in 5/8 ROIs, highlighting the influence of CSF morphology on CSF dynamics. Finally, Aß status was related to RMS velocity in 2/8 ROIs, indicating that altered CSF dynamics could play a role in incipient AD.

Background Cerebrospinal fluid (CSF) dynamics are increasingly studied to understand potential pathologic coupling with neurological disorders. In Alzheimer’s disease (AD), CSF dynamics may be altered secondary to AD‐related atrophy and enlarged CSF spaces. Additionally, animal studies suggest that altered CSF dynamics could impair clearance of metabolic waste, leading to accumulation of amyloid‐beta (Aβ). However, assessment of human CSF dynamics has typically been limited to the cerebral aqueduct using 2D phase contrast MRI. In this work, we used 4D flow MRI to enable volumetric characterization of CSF flow dynamics in the human brain, to study its associations with CSF morphology and Aβ burden from 11C‐PiB PET. Method Cardiac‐resolved 4D flow MRI was used to assess CSF motion in N=69 participants (age 69±8 years; 17 Aβ+) from the Wisconsin Registry for Alzheimer’s Prevention, using a clinical 3T scanner (GE Premier) and a velocity encoding of 5 cm/s. Region‐averaged velocity waveforms were acquired in the spinal canal (SC), fourth ventricle (4V), cerebral aqueduct (CA), third ventricle (3V), the foramens of Monro (FMo), and the lateral ventricles (LV), segmented from T1‐weighted volumes. The waveforms were characterized by root‐mean‐square (RMS) velocity and were evaluated in relation to total CSF volume and Aβ status from an established PiB global distribution volume ratio threshold >1.16. Result Velocity waveforms demonstrated cardiac‐pulsatility in all regions of interest (ROIs) (Figure 1), with RMS velocities ranging from high in the SC (6.56±2.29 mm/s) to low in the lateral ventricles (right LV 0.31±0.15 mm/s). RMS velocities were related to total CSF volume for the SC (R=‐0.24; p=0.043), CA (R=0.27; p=0.034), 3V (R=‐0.28; p=0.018), left LV (R=‐0.39; p=0.002), and right LV (R=‐0.35; p=0.009) (Figure 2). In preliminary analysis, RMS velocities were higher in Aβ+ compared to Aβ‐ for the SC (p=0.048) and right LV (p=0.024) (Figure 3). Conclusion 4D CSF flow MRI is feasible for the assessment of CSF dynamics throughout the ventricular system. Total CSF volume was related to RMS velocities in 5/8 ROIs, highlighting the influence of CSF morphology on CSF dynamics. Finally, Aβ status was related to RMS velocity in 2/8 ROIs, indicating that altered CSF dynamics could play a role in incipient AD.

Background Cognitive 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. Method Participants 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). Result Mean(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). Conclusion WMH 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.

Background Cognitive 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 Aβ 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. Method Participants 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). Result Mean(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). Conclusion WMH 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.