Journal of Cerebral Blood Flow & Metabolism

Background: Positron emission tomography (PET) myocardial perfusion imaging (MPI) provides complementary information on perfusion, myocardial blood flow and ventricular function. While these markers are often considered collectively during interpretation, their quantitative integration with imaging and clinical data into a unified predictive framework remains limited. We developed a multimodal artificial intelligence framework that combines PET polar maps with quantitative imaging and clinical features to improve obstructive coronary artery disease (CAD) detection. Methods: We retrospectively analyzed the multicenter REFINE PET registry. Among 38,682 PET MPI studies from 14 sites, 2,833 patients without known prior CAD underwent invasive coronary angiography within 180 days. Obstructive CAD was defined as >=50% left main stenosis or >=70% stenosis in other major epicardial coronary arteries. We developed a two-stage contrastive learning framework to learn multimodal PET representations from studies without angiographic labels and transfer them to supervised CAD prediction. In Stage 1, PET image and tabular encoders were pretrained on 12,225 PET MPI studies from eight development sites using 15-channel PET polar maps, quantitative PET perfusion, flow and gated functional measures, and clinical variables. In Stage 2, the pretrained encoders and a lightweight classification head were fine-tuned in 968 angiography-labeled patients, using lower encoder learning rates to limit overfitting. The model was externally validated for angiographically defined obstructive CAD detection in 1,865 patients from six independent sites and compared with standard PET MPI metrics. Results: The prevalence of obstructive CAD was 60% in the training cohort (66% male, median age of 70 years [63, 77]), and 55% in the external validation cohort (64% male, median age of 67 years [60-74]). In external validation, the AI model achieved an AUC of 0.85 (95% confidence interval (CI), 0.83-0.87) for obstructive CAD detection and outperformed conventional quantitative PET metrics (all P < 0.001). At a specificity matched to visual summed stress score, the AI model achieved higher sensitivity (89% [95% CI, 87-91] versus 85% [95% CI, 82-87]) and negative predictive value (81% [95% CI, 77-84] versus 73% [95% CI, 69-77]; both p<0.001). The overall net reclassification improvement was 8.9% (95% CI, 4.2-13.6%; p = 0.001). Conclusions: Multimodal contrastive pretraining improved obstructive CAD detection from PET imaging beyond conventional perfusion-based scoring in independent multisite external validation.

Purpose: Attenuation correction (AC) of PET images is essential for accurate quantification. Brain PET studies comprising simultaneous EEG (PETEEG) may suffer from metal artifacts in CT images (CTEEG), or improper correction when electrodes are not present in the CT (CT0). As these influences are not well-characterized, we aim to compare metal artifact reduction (MAR) techniques for CTEEG images, and evaluate differences between attenuated-corrected PETEEG using CT0 and CTEEG with MAR, synthetically placed electrodes (CTEEG-synth) and extended Hounsfield unit (HU) range. Methods: 19 healthy participants underwent two total-body PET/CT scans with [18F]FDG, with and without 32 EEG scalp electrodes, respectively. We evaluated five MARs to reduce streaks caused by the EEG electrodes in the CTEEG. Finally, CT0, CTEEG with (CTEEG-iMAR-Ext) and without extended HU range (CTEEG-iMAR) and CTEEG-synth were used to perform attenuation correction of PETEEG. We compared our results to PET0/CT0 scan using relative differences. Results: CTEEG and CTEEG-iMAR showed the smallest differences to CT0. PETEEG/CTEEG-iMAR-Ext exhibited the lowest differences to PET0/CT0 (average bias across all regions of -0.46%), followed by similar performance of PETEEG/CTEEG-iMAR (-0.73%) and PETEEG/CTEEG (-0.76%). Conversely, PETEEG/CT0 demonstrated the largest average differences (-1.81%), with values reaching -2.71% in the parietal lobe. These differences were consistent across subjects, yielding significant effects in most of the brain (pFWE < 0.05). CTEEG-synth performed not as good as CTEEG (-1.21%). Conclusions: CTEEG with extended HU range is most suitable for attenuation correction of PETEEG images, with MAR correction offering little additional improvement.

Functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) both rely on the phenomenon of neurovascular coupling (NVC) to probe brain activity through their sensitivity to cerebral blood oxygenation. However, the relationship between fNIRS chromophores (oxy- and deoxyhaemoglobin, HbO and HbR), and fMRI (Blood Oxygen Level Dependent and Arterial Spin Labeling, BOLD and ASL) measurements, and whether this relationship remains consistent across subjects and physiological conditions, has only been partially characterised.. We acquired concurrent continuous-wave fNIRS and gradient-echo (GE) and spin-echo (SE) BOLD-ASL fMRI in healthy adults (n = 10) during visual stimulation. By applying calibrated fMRI methodology, we examined the relationships between fNIRS-derived haemoglobin modulations and fMRI-derived modulations in macrovascular (GE-) and microvascular (SE-) BOLD signals, cerebral blood flow (CBF), and oxygen metabolism (CMRO2). Group-level results showed strong temporal cross-modal agreement, with HbO and HbR tightly mirroring all fMRI signal time-courses (|r| > 0.8). A quantitative analysis of trial-by-trial modulations revealed distinct state-dependent behaviours: HbO maintained a stable relationship with the fMRI-derived metrics across conditions, whereas cross-modal relationships between HbR and fMRI-derived metrics substantially strengthened at higher flow-metabolism coupling (FMC), the ratio of CBF to CMRO2 change, an index of the strength of NVC. Both HbO and HbR were more strongly associated with GE-BOLD than with SE-BOLD. These findings provide a rigorous physiological grounding for fNIRS signal interpretation, demonstrating its utility as a surrogate marker for specific haemodynamic and metabolic parameters.

Background. Dynamic susceptibility contrast MRI with capillary-function post-processing exports a relative maximum cerebral metabolic rate of oxygen, formed from blood flow and a transit-time-derived extraction term. The share each contributes to an observed contrast is unquantified. Methods. In a retrospective single-centre cohort with untreated glioblastoma, six perfusion maps normalised to normal-appearing white matter were sampled in automatically segmented enhancing tumour and peritumoral brain. The paired compartment contrast in the oxygen-metabolism index was partitioned into flow, extraction and residual terms and examined against tumour-core volume. Results. Of 131 patients, 122 were analysable. Flow-linked maps were about twice as high in enhancing tumour, the transit and extraction maps only modestly (all q < 0.05). Flow accounted for 92.6% (95% CI 85.9-98.8) of the contrast and extraction for 6.6% (0.7-12.9). Across volume tertiles the flow share rose from 67.8% to 104.0%, a gradient arising peritumorally: every map changed with volume there, none in enhancing tumour. Conclusion. The compartment contrast in the oxygen-metabolism index is largely accounted for by blood flow and varies with lesion size, that dependence originating peritumorally. It should be read within the complete perfusion panel, not as independent metabolic evidence.

Introduction: A new method that corrects for time-dependent bias in standardized-uptake value ratios (SUVRs) was adapted and optimized for [11C]PiB (PiB) amyloid-beta (A{beta}) PET, across low-to-high A{beta} loads, relying only on PET data collected during the SUVR time-window. This modeling approach was evaluated in cross-sectional and longitudinal cohorts for earlier and shorter SUVR time-windows (30-45 min, 45-60 min) than commonly applied, to enable higher throughput imaging. Methods: The SUVR correction (SUVRc) approach was optimized and tested on separate cross-sectional (n=88), and longitudinal (36 participants, two time-points, 72 images) cohorts from the Harvard Aging Brain Study. The cross-sectional cohort spanned low, intermediate and high levels of cortical A{beta} pathology and the longitudinal images included two cohorts with low (5-10%) and high levels (~40%) of A{beta} change. SUVR and SUVRc were compared against SRTM DVR (0-60 min) to quantify A{beta} burden through Pearson's and Lin's correlations, difference plots and longitudinal change. Results: The mean regional bias in PiB SUVR (5-15%, depending on time-window and A{beta} burden) was significantly reduced to < 3% by SUVRc (corrected p < 0.05) in the cross-sectional cohorts for all time-windows, along with reductions in bias variability. SUVRc also showed higher Pearson's correlation (r) and Lin's concordance (LCC) with DVR across time-windows (r=0.98, LCC=0.99 at 30-45 min and 45-60 min) compared to uncorrected SUVR (r=0.96, LCC=0.95 at 30-45 min, r=0.97, LCC=0.92 at 45-60 min). Bland-Altman plots confirmed better agreement between SUVRc and DVR (mean bias at 30-45 min: 0.02 for SUVRc, 0.10 for SUVR; mean bias at 45-60 min: 0.01 for SUVRc, 0.17 for SUVR). Longitudinal DVR changes were more accurately represented by SUVRc, compared to uncorrected SUVR. Conclusions: SUVRc for [11C]PiB PET enables more accurate quantification of A{beta} burden than SUVR in cross-sectional and longitudinal studies (relative to SRTM DVR), while enabling imaging at earlier and shorter time-windows. The improved accuracy would be beneficial in better quantifying amyloid re-emergence post anti-amyloid therapy and could be used for kinetic harmonization across time-windows and radiotracers.

Introduction: Anesthesia is known to modulate glymphatic clearance and cerebrospinal fluid (CSF) transport in rodents, but how these effects translate to a larger, gyrencephalic brain is unknown. With its anatomical similarity to the human brain, the pig offers a valuable translational model for examining anesthesia-dependent CSF-to-brain transport. Methods: We used dynamic in vivo SPECT/CT imaging for six hours following cisterna magna injection of [99mTc]-DTPA to quantify CSF-to-brain tracer transport in pigs under two anesthesia regimens: ketamine/dexmedetomidine (K/D, n=5) which previously has been shown in rodents to enhance glymphatic influx relative to GABAergic anesthesia, and propofol (PRO, n=5). Brain and CSF spaces were delineated using a data-driven non-negative matrix factorization approach, and tracer kinetics were quantified using a one-tissue compartment model. Results: Brain influx could be stably estimated from 2 hours post-injection. Hierarchical sub-division of the brain parenchyma identified two kinetically distinct components with different anatomical distributions: a surface component, located ventrally and within the interhemispheric fissure, showed faster kinetics than the anatomically deeper and lateral-dorsal component. Consistent with rodent findings, K/D-anesthetized pigs showed 62% (p=0.002) greater brain tracer accumulation than PRO-anesthetized pigs. However, while the brain influx rates did not differ substantially (p=0.047), a 52% higher cumulative CSF tracer concentration (p=0.047) could account for most of the difference by providing greater tracer availability for brain entry. Conclusions: In the larger gyrencephalic pig brain, we found higher brain tracer accumulation under K/D anesthesia compared to PRO anesthesia. A significant portion of this difference is readily explained by higher CSF retention, likely driven by a slower CSF turnover. This underscores the necessity of dynamic CSF tracer concentration measurements when assessing CSF-brain influx, a factor we suggest that future glymphatic studies should take into account.

Objectives: To develop and externally validate a wall-focused deep learning framework for identifying composite unstable intracranial aneurysm phenotypes on dual-phase high-resolution vessel wall imaging (HR-VWI), and to visualize model attention on the aneurysm wall surface. Methods: This retrospective multicenter study included patients with intracranial aneurysms who underwent both non-contrast and contrast-enhanced HR-VWI. Center 1 was used for model development and patient-level five-fold out-of-fold assessment, whereas Centers 2 and 3 served as independent external validation cohorts. For each aneurysm, dual-phase local wall patches and larger spatial context patches were generated. The Wall-Constrained Encoding Network (WCE-Net) extracted mask-constrained local wall features, and a transfer-learning U-Net with Nested Transformers (UNesT) branch extracted spatial context information. Branch outputs were fused by logit-level stacking. Model performance was evaluated using discrimination, calibration, and decision curve analysis. Three-dimensional gradient-weighted class activation mapping (Grad-CAM) responses were projected onto the reconstructed aneurysm wall surface and compared with HR-VWI surface signal intensity. Results: A total of 629 patients with 773 aneurysms were included. The final fusion model achieved areas under the receiver operating characteristic curves (AUCs) of 0.908, 0.857, and 0.855 in Center 1, external Center 2, and external Center 3, respectively. Corresponding Brier scores were 0.119, 0.153, and 0.150. Surface Grad-CAM showed partial spatial overlap between model-attention hotspots and high-signal HR-VWI regions. Conclusions: Dual-phase wall-focused local-context fusion showed feasibility for identifying composite unstable intracranial aneurysm phenotypes across centers. Surface Grad-CAM provided anatomically referenced visualization of model attention.

BACKGROUND: Cerebral ischemia following subarachnoid hemorrhage (SAH) has traditionally been considered transient because functional alterations of the cerebral microcirculation are thought to be self-limiting. However, we identified a previously unrecognized vasculopathy, perivascular fibrosis of the cerebral microcirculation (PFCM), characterized by excessive type I collagen deposition after SAH. This study investigated the mechanisms underlying PFCM and its subsequent effects on cerebral hemodynamics. METHODS: In vivo SAH was modeled in mice by autologous blood injection, whereas oxygenated hemoglobin (OxyHb) exposure was used to mimic SAH in vitro. Pericyte-deficient mice (Pdgfr{beta}+/-) and pericyte-specific vestigial-like family member 3 (VGLL3) conditional knockout mice (Vgll3{Delta}PC) were generated. Pericyte contractility was measured by nanoindentation and traction force microscopy. Molecular mechanisms were examined using Western blotting, immunofluorescence, CUT&Tag, RNA-seq, transmission electron microscopy, and molecular docking. PFCM, impaired dilation of the cerebral microcirculation, and cerebral autoregulation were assessed by two-photon imaging, transcranial Doppler with continuous blood pressure monitoring, super-resolution ultrasound imaging, and photoacoustic imaging. RESULTS: After SAH, mice developed long-term cerebral autoregulation dysfunction marked by impaired dilation of the cerebral microcirculation, with the abnormality being most evident within the relatively lower blood pressure range. The marked reduction in PFCM in Pdgfr{beta}+/- mice indicated that pericytes were the principal cellular contributors. Mechanistically, OxyHb-induced cytoskeletal remodeling in vitro increased pericyte contractility and promoted nuclear translocation of SAH-upregulated VGLL3. This was followed by increased genomic occupancy, Col1a1 transcriptional activation, and type I collagen deposition. Pericyte-specific VGLL3 knockout abolished PFCM and, consequently, significantly alleviated long-term cerebral autoregulation dysfunction. CONCLUSIONS: Our findings identify PFCM mediated by pericytic VGLL3 as a novel vasculopathy leading to long-term cerebral autoregulation dysfunction after SAH.

Functional ultrasound imaging (fUSI) measures cerebral blood-volume responses, so anesthesia protocols developed for BOLD fMRI may not preserve its signal. We screened three fMRI-derived anesthesia regimens for visually evoked fUSI in the cat visual cortex. Isoflurane-ketamine-medetomidine produced the strongest and most consistent responses, but the standard intramuscular medetomidine bolus suppressed the signal in one sensitive cat. Pharmacokinetic modeling guided replacement of this bolus with controlled intravenous dosing, restoring the visual response while maintaining physiological stability. The same weight-based regimen produced robust responses in the other animals. In a direct within-session test, response strength was equivalent in recordings separated by more than three hours. Across 464 recordings from 51 sessions in three cats, it showed no temporal drift during follow-up extending to 23 months. Visually evoked activation also remained clear relative to a small awake dataset, although equivalence was not established. PK-guided control of medetomidine exposure therefore enables stable, repeated fUSI of the cat visual cortex over hours to years.

Background: Stroke remains a leading cause of mortality and long-term disability worldwide, with high residual vascular risk among survivors despite optimal secondary prevention. The contribution of inherited polygenic risk to this residual vulnerability remains unclear. Methods: We analyzed 2,701 stroke survivors (mean age 59.8{+/-}7.1 years, 61.7% male) from the UK Biobank. Stroke- and coronary artery disease (CAD)-polygenic risk scores (metaGRS), comprising approximately 3.2 million and 1.7 million genetic variants, respectively, were derived from large-scale genome-wide association studies using penalized regression. The primary outcome was major adverse cardiovascular events (MACE), while secondary outcomes included recurrent stroke and vascular risk factor control. metaGRS associations with incident MACE and recurrent stroke were tested using Cox models, whereas associations with baseline risk-factor control were assessed using logistic regression. Mediation analyses quantified indirect effects of metaGRS to MACE via HbA1c, LDL cholesterol, and blood pressure. Results: Over 12 years, 731 MACE events (27.1%) and 351 recurrent stroke events (13.0%) occurred. CAD-metaGRS was independently associated with future MACE (age- and sex-adjusted HR per SD increment 1.15, 95%CI 1.07-1.24; p<0.001), whereas higher stroke- and CAD metaGRS were both associated with poorer glycemic control. A higher CAD-metaGRS was also associated with poorer lipid control. Mediation analyses identified glycemic regulation as a significant pathway linking polygenic risk to recurrent vascular events. Conclusions: Polygenic risk scores for cardiovascular disease are associated with recurrent vascular events and vascular risk factor control among stroke survivors, pointing to potentially actionable insights in secondary prevention that merit further investigation.

Vascular cognitive impairment and dementia (VCID) is increasingly recognized as a major contributor to cognitive decline; however, the mechanisms through which vascular dysfunction drives innate immune dysregulation remain poorly understood. In this study, we explore the impact of VCID on the cerebral innate immune landscape, focusing on innate lymphoid cells (ILCs) and neutrophils, two key players in neuroinflammation and brain immune homeostasis. Using a murine model of VCID induced by bilateral common carotid artery stenosis (BCAS) with modifications in C57BL/6 mice, we investigated innate immune cell distribution, polarization, and functional profiles using flow cytometry and immunofluorescence staining. Our findings reveal a compartment-specific shift in ILC populations, with a reduction of ILC2s in the meninges and concurrent expansion in the choroid plexus, accompanied by altered cytokine production. Furthermore, VCID drove a marked shift in neutrophil polarization toward a pro-inflammatory N1-like phenotype in both the meninges and choroid plexus. Critically, immunofluorescence staining of hippocampal brain sections confirmed that activated N1-like neutrophils, characterized by elevated IL-1{beta} and MPO and reduced IL-10, infiltrate the hippocampal parenchyma in VCID, suggesting a spatially progressive innate immune response spanning from CNS border compartments to brain tissue. These results identify a novel innate immune signature in VCID, compartment-specific ILC redistribution, pro-inflammatory neutrophil polarization at CNS borders, and parenchymal neutrophil infiltration in the hippocampus, which may collectively amplify neuroinflammation and accelerate cognitive decline, identifying potential therapeutic targets for vascular-related dementia.

Dynamic positron emission tomography (PET) with long axial field-of-view (LAFOV) scanners enables multi-organ imaging and kinetic quantification beyond static (late-phase) imaging; however, the long times typically required for dynamic acquisitions remain clinically impractical. This study evaluates a deep learning (DL) framework to enable abbreviated dynamic PET acquisitions, comparing single-time-window (STW, early dynamic data only) and dual-time-window (DTW, early dynamic data plus a late 5-min static frame) protocols with early dynamic scan durations of 5-30 min and dose levels ranging from 360 MBq to 18 MBq. Seventeen 60-min dynamic [18F]FDG datasets were first motion-corrected using a staggered FALCON pipeline and then used to train and test a spatiotemporal DL model for autoregressive frame prediction. Performance was assessed across the full quantitative workflow, from DL-predicted frames and time-activity curves to organ-based kinetic modeling and voxel-wise parametric imaging in multiple tissues and two patient cohorts. DTW protocols consistently outperformed STW, better preserving late-phase kinetics. For a 15-min early dynamic scan, adding a late 5-min scan reduced mean absolute Ki difference from 23% (STW) to 17% (DTW) in the liver and from 26% to 15% in the thalamus. DTW + DL further reduced errors to [&le;]10% in the liver, thalamus, and breast lesion, and 16% in muscle. Our recommended protocol, 15-min early dynamic scan plus a 5-min late scan with DL, remained robust to up to a 5-fold dose reduction (~74 MBq). Overall, these findings support DL-enabled abbreviated, low-dose dynamic LAFOV PET as a clinically feasible approach for accurate kinetic quantification

BackgroundSingle-voxel magnetic resonance spectroscopy (MRS) is a non-invasive method for measuring clinically and cognitively relevant metabolites. Reliable measurements require precise voxel placement across sessions and participants. We developed a scanner-console-based approach to improve voxel placement precision. MethodsIn a crossover design (n=7; six sessions each), we compared test-retest reliability of three voxel placement methods in a reference benchmark (left parietal cortex) and a technically challenging region (left ventromedial prefrontal cortex). Methods included (1) conventional anatomy-based placement, (2) mask-guided real-time positioning (MGRP), and (3) semiautomated session-locked voxel repositioning (SSVR). Resting-state MRS data were acquired using PRESS and MEGA-PRESS. Within-subject reliability of voxel placement and metabolite concentrations, namely, total N-acetylaspartate (tNAA), total Creatine (tCr), GABA (gamma-aminobutyric acid), and Glx (glutamate + glutamine) are reported using the coefficient of variation (CV), the intraclass correlation coefficient (ICC), minimal detectable change (MDC), and the spatial overlap. ResultsSSVR markedly improved voxel placement reliability, increasing spatial overlap (up to 88%) and achieving near-perfect geometric reproducibility (ICC = 0.99) compared to conventional anatomy-based placement and MGRP. SSVR improved tissue composition consistency and reduced metabolite variability in the technically challenging region (variability reduction of [~]70% tCr, [~]59% tNAA, and [~]51% Glx) while further refining already stable measurements in the benchmark region (tNAA from [~]15% to [~]10%). ConclusionBoth MGRP and SSVR improved voxel placement and metabolite measurement reproducibility compared with conventional anatomy-based placement. SSVR further enhanced within-subject reproducibility across repeated sessions, particularly in the technically challenging region, providing a robust approach for longitudinal single-voxel MRS studies.

BackgroundOverweight and obese individuals often exhibit lower mortality rates or better prognoses than lean or normal-weight individuals with stroke and other diseases, a phenomenon called the "obesity paradox". Carotid atherosclerosis is the primary cause of ischemic stroke, and body weight serves as a reliable surrogate for adiposity in mice. MethodsPhenotypic and genetic connections of carotid atherosclerosis with body weight were evaluated in 299 F2 mice derived from BALB/cJ and LP/J Apoe knockout (Apoe-/-) mice. F2 mice were fed a Western diet for 12 weeks. Atherosclerotic lesion sizes in left carotid arteries, body weight, coat color, plasma lipids, glucose, small dense LDL ApoB, and malondialdehyde were measured, and 11,000 single nucleotide polymorphism (SNP) markers were genotyped. ResultsCarotid lesion sizes inversely correlated with body weight in both sexes. Genome-wide scans identified two significant quantitative trait loci (QTLs) for carotid atherosclerosis on chromosomes (Chr) 6 and 15 in an additive sex model, and five QTLs on Chr 6, 7, 12, 13, and 15 in an interactive sex model. Adjusting for body weight variation downgraded Chr 15 QTL (Cath5) in both models, whereas other QTLs upgraded in the additive sex model and downgraded in the interactive sex model. Human syntenic region of Cath5 associated with carotid intima-medial thickness (cIMT) and waist-to-hip ratio (WHR). ConclusionsThese findings indicate that the obesity paradox in carotid atherosclerosis is partially driven by shared genetic components that exert opposing effects on adiposity and plaque development and act through sex-dependent mechanisms.

Purpose: Respiratory motion remains a major source of quantitative bias in PET and becomes increasingly relevant for high-sensitivity long axial field-of-view (LAFOV) PET/CT. Although numerous respiratory motion correction (MoCo) methods have been proposed, their quantitative accuracy cannot be established clinically because a patient-specific motion-free reference is fundamentally unavailable in vivo. This study combined clinical PET imaging with a digital twin, a realistic representation of both the PET/CT system and the patient, to objectively validate respiratory MoCo against a corresponding motion-free reference. Methods: Twenty patients (10 [18F]FDG with predominantly pulmonary lesions and 10 [18F]SiFAlin-TATE with predominantly hepatic lesions; total 135 lesions) were analyzed. The digital twin combined a validated LAFOV PET/CT simulation model with an anatomically realistic phantom containing 14 lung and liver lesions, two patient-derived respiratory patterns, and respiratory motion amplitudes of 2 and 3 cm, generating patient-like datasets with corresponding motion-free references. Data-driven and image-based MoCo were evaluated using lesion morphology, SUVmean, SUVmax, and metabolic tumor volume (MTV). Results: In patients, data-driven MoCo produced larger SUVmean increases than image-based MoCo for liver (48.1{+/-}18.9% vs. 17.0 {+/-} 12.0%; p<0.01), lower-lung (32.5{+/-}21.2% vs. 16.3{+/-}15.6%, p=0.06), and upper-lung lesions (28.4{+/-}32.0% vs. 10.4 {+/-} 17.2%; p<0.01), with similar findings for SUVmax and larger MTV reductions. Simulation revealed marked motion-induced SUVmean underestimation before correction, particularly in liver (-31.2{+/-}6.8%) and lower lung (-15.5{+/-}13.9%). Relative to the motion-free reference, data-driven MoCo most accurately recovered hepatic uptake (4.3{+/-}11.7% vs. -10.0 {+/-} 9.2%; p=0.01) but overestimated pulmonary uptake (lower lung: 19.8{+/-}16.3% vs. -1.6 {+/-} 10.2%; p=0.02). SUVmax showed the same regional behavior, whereas image-based MoCo yielded MTV estimates closer to the reference. Quantitative recovery was largely independent of respiratory pattern, while larger motion amplitudes mainly affected image-based MoCo. Conclusion: Combining clinical PET with a realistic digital twin and corresponding motion-free ground truth enabled objective validation of respiratory MoCo beyond conventional clinical evaluation. Larger correction-induced quantitative changes should not be equated with greater quantitative accuracy. Instead, MoCo performance was region- and metric-dependent, highlighting the value of ground-truth-based validation for developing and benchmarking respiratory motion correction and quantitative PET on LAFOV PET/CT systems.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Traditional MRI-markers of cSVD are mainly detectable in older adults, but diffusion MRI (dMRI) measures of white matter microstructure can capture changes predisposing to cSVD earlier in life. In this study, we conducted large genomics and omics explorations of diffusion kurtosis imaging (DKI) dMRI markers, to better characterize the underlying biological mechanisms and explore their clinical relevance in relation to cognition, established cSVD MRI-markers and dementia. We conducted a genome-wide association study (GWAS) of DKI markers in the population-based Rhineland Study (N=5 930). We identified four genome-wide significant loci associated with DKI markers at chr3p25.1 (LINC00620-WNT7A), chr5q14.2 (VCAN), chr5q14.3 (VCAN-AS1) and chr8q24.21 (CCDC26), and 11 additional suggestive loci. Lead SNPs at chr5q14.3 and chr17q25.1 were associated with white matter hyperintensity volume, chr3p25.1 with white matter perivascular spaces, and chr7p11.2 with Alzheimer disease. Using a transcriptome-wide association study, we identified 17 genes with genetically determined expression associated with DKI markers, including 14 at the chr17q21.31 suggestive GWAS locus. Finally, we identified eight proteins associated with DKI markers in GWAS suggestive loci. Of these, MAD1L1, EGFR and GFAP were also associated with cognitive decline, and MAD1L1 with white matter hyperintensity volume. In conclusion, leveraging omics data, our study identified novel molecular determinants of DKI markers, providing important novel insights into life course determinants of cSVD, a leading cause of stroke and dementia.

BackgroundEfferocytosis, the phagocytic clearance of apoptotic and damaged cells, promotes inflammation resolution and tissue repair following ischemic stroke. This study investigated temporal changes in efferocytosis and phagocytosis-related transcriptional programs during acute experimental stroke, examined the effects of aging on these responses, and assessed whether similar immune signatures are present in human ischemic stroke. MethodsPublicly available transcriptomic datasets from murine transient middle cerebral artery occlusion (tMCAO; GSE104036 and GSE112348), permanent middle cerebral artery occlusion (pMCAO; GSE137482), and human peripheral blood after ischemic stroke (GSE16561) were analyzed using OmicSoft/Ingenuity-style pathway analysis. Functional validation included in vivo assessment of efferocytosis after tMCAO and in vitro phagocytosis assays using bone marrow-derived macrophages from young and aged mice. ResultsBoth acute tMCAO models exhibited robust inflammatory activation together with sustained activation of phagocyte-related pathways during the first 24 hours after stroke. Human peripheral blood demonstrated similar inflammatory and phagocytic signatures, supporting translational relevance. Increased efferocytosis at 24 hours after tMCAO was associated with neuroprotection. Although both young and aged mice activated phagocytosis-related pathways after pMCAO, aged mice showed reduced phagosome formation. Consistent with these findings, macrophages from aged mice exhibited enhanced inflammatory responses and impaired uptake of apoptotic cells. ConclusionsA conserved post-stroke immune response characterized by inflammatory activation and phagocyte-mediated clearance was identified across murine and human datasets. Efficient efferocytosis was associated with neuroprotection, whereas aging impaired apoptotic cell clearance and promoted a pro-inflammatory macrophage phenotype, highlighting efferocytosis as a potential therapeutic target for ischemic stroke.

Optimal blood pressure management after thrombectomy remains uncertain, and individualized autoregulation-based targets typically require continuous neuromonitoring. We developed an angiography-derived autoregulatory metric using intraprocedural data and applied it retrospectively to a single-center cohort of patients who underwent thrombectomy for acute stroke. From 62 patients with 3-month functional outcomes, greater time within the predicted autoregulatory range during the first 24 hours after thrombectomy was independently associated with improved outcome after adjustment for covariates (odds ratio per 10% increase, 1.86; 95% CI, 1.31-2.66; P = .0006). These findings support routine angiography as a potential source of early, patient-specific hemodynamic targets after thrombectomy.

Background Even a highly accurate diagnostic test can yield more false-positive than true-positive findings in low-prevalence settings, which is known as the false positive paradox. Radiologists' unawareness of this paradox may foster automation bias, the tendency to excessively rely on AI outputs. Methods In this prospective, multinational, randomized controlled reader study (DRKS00038740), 34 readers from 10 countries (16 residents, 8 general radiologists or fellows, and 10 neuroradiologists) were randomly assigned to a control group (n = 17) or intervention group (n = 17), stratified by experience level. The intervention group reviewed a short, 3-minute educational video explaining the false positive paradox prior to the reading session. Both groups evaluated 20 TOF-MRA studies with AI-flagged findings (10% true-positive, 90% false-positive). Primary outcomes were acceptance rate of false-positive AI findings and follow-up intensity. These were evaluated using mixed models with crossed random effects for reader and case. Results At baseline, readers vastly overestimated the positive predictive value of AI tools for intracranial aneurysm detection (mean estimate, 62.9%; simulation-based estimate, 15.4% [95% interval, 8.1-28.0%]). The intervention reduced the odds of accepting AI false positives (OR 0.50 [upper 95% confidence bound, 0.95], one-sided p = 0.017), with acceptance probabilities of 12.7% (95% CI, 6.0-25.0%) in the intervention group compared to 22.5% (95% CI, 11.6-39.2%) in the control group. The intervention group exhibited a downward shift in follow-up intensity for false positives (OR 0.47 [upper 95% confidence bound, 0.81]; one-sided p = 0.014), recommending follow-up in 39.2% (120/306) of cases, compared to 54.9% (168/306) in the control group. Conclusion A brief education on the false positive paradox improved trust calibration in AI-assisted intracranial aneurysm detection. Our findings highlight the potential of reader-side cognitive debiasing strategies to improve trust calibration and support safer use of AI in radiology.

Background: Neuropsychiatric comorbidities are highly prevalent in chronic kidney disease (CKD), yet the underlying neural mechanisms remain poorly defined. Methods: We established multiple mouse models of CKD and identified an adenine-induced model as the most suitable platform to study neurobehavioral alterations. Anxiety susceptibility was operationalized as the emergence of anxiety-like behavior after subthreshold unpredictable stress (SUS) and was assessed using the SUS paradigm combined with behavioral assays. Region-focused c-Fos mapping, fiber photometry, and chemogenetic manipulation were used to interrogate neural circuit activity. Pharmacological and genetic approaches were applied to investigate the role of angiotensin II (Ang II) signaling. Finally, hypothalamic paraventricular nucleus (PVN) activation was used to explore brain-to-kidney feedback by using in vivo multiphoton microscopy imaging techniques. Results: CKD mice showed no consistent baseline anxiety-like phenotype across standard assays but developed robust anxiety-like behavior after subthreshold unpredictable stress. Region-focused c-Fos profiling and fiber photometry identified the central amygdala (CeA) as a stress-sensitized limbic node in CKD. Chemogenetic inhibition of CeA GABAergic neurons attenuated anxiety-like behavior, supporting a functional role for CeA activity. Mechanistically, CKD elevated circulating Ang II and enhanced CeA accumulation of peripherally administered FAM-Ang II-associated signal. CeA-specific Agtr1a knockdown attenuated anxiety-like behavior and exaggerated stress evoked CeA calcium responses. Exploratory experiments further showed that sustained PVN glutamatergic activation aggravated early renal injury markers in a mild renal injury model. These findings support a kidney-to-brain model in which CKD primes CeA stress circuits, while local Ang II AT1R signaling contributes to the behavioral expression of stress-induced anxiety-like behavior, with a potential brain to kidney feedback component. Conclusions: CKD promotes stress-induced anxiety susceptibility through a CeA-centered mechanism involving local Ang II AT1R signaling. These findings identify CeA Ang II AT1R signaling as a potential contributor to CKD-associated stress-related affective vulnerability.