Yawu Liu

Accurate alignment of hippocampal anatomy across individuals remains challenging due to complex and highly variable folding patterns that are not well captured by conventional volumetric approaches. HippUnfold introduced a surface-based representation of the hippocampus, but key components-including coordinate estimation and inter-subject correspondence-were defined in the volumetric domain, making them susceptible to topological errors and interpolation artifacts. Here, we introduce a surface-intrinsic formulation of hippocampal unfolding in which geometry, intrinsic coordinates, and correspondence are defined directly on subject-specific surface manifolds. Intrinsic anterior-posterior and proximal-distal coordinates are computed by solving Laplace equations on the surface, and correspondence is established through surface-based resampling in unfolded space, replacing inverse volumetric warping. Relative to the original HippUnfold approach, this formulation improves test-retest consistency, subject identifiability, and mesh quality, while better preserving subject-specific gyral and sulcal morphology. Surface representations show reduced distortion between folded and unfolded spaces and eliminate misplaced or outlier vertices associated with volumetric warping. These improvements translate to enhanced sensitivity in a clinical application, improving lateralization of temporal lobe epilepsy. These results demonstrate that a surface-intrinsic formulation provides a principled and robust foundation for hippocampal unfolding, enabling topology-preserving alignment and more accurate characterization of inter-individual variability in health and disease.

Background: Timely identification of early recurrence (≤ 6 months) may improve prognosis of glioblastoma (GBM), but conventional MRI has shown limited accuracy in this setting. Purpose: Risk‐assessment models and a nomogram were constructed by integrating SyMRI metrics, clinical–pathological variables, and cMRI features, including contrast‐enhanced T1‐weighted imaging and fluid‐attenuated inversion recovery (FLAIR) findings. Study Type: Retrospective observational study. Population: Seventy‐eight patients with GBM (median age, 59 years; 44 [56.4%] males). Field Strength/Sequence: 3 T; pre‐ and post‐contrast three‐dimensional T1‐weighted imaging, FLAIR, and SyMRI. Assessment: Histogram‐based quantitative metrics were extracted from SyMRI maps using subregions defined on fused FLAIR and contrast‐enhanced T1‐weighted images. All candidate clinical–pathological variables, cMRI features, and SyMRI‐derived metrics were entered directly into least absolute shrinkage and selection operator (LASSO) regression for variable selection. Risk‐assessment models and a nomogram were constructed. Statistical Tests: Multivariable logistic regression, multicollinearity assessment using variance inflation factors, receiver operating characteristic curve analysis with DeLong test, stratified 10‐fold cross‐validation, leave‐one‐out cross‐validation, nested 5‐fold cross‐validation, calibration curves, and decision curve analysis. A two‐sided p < 0.05 was considered statistically significant. Results: Multivariate analysis identified reduced T2 entropy (< 2.113) in enhancement‐corresponding regions (odds ratio [OR] = 0.08), thick linear or nodular residual cavity wall enhancement (OR = 5.28), corpus callosum involvement (OR = 5.08), and O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation (OR = 0.19) as variables associated with early recurrence. The integrated model achieved the highest performance (AUC = 0.864). Nested cross‐validation showed moderate internal validation performance, with an AUC of 0.724 (0.722–0.726). Data Conclusion: Histogram‐based pre‐radiotherapy SyMRI metrics, particularly T2 entropy, were associated with early GBM recurrence. The integrated model achieved the highest apparent performance; nested internal validation showed moderate performance. External validation in larger multicenter cohorts is required before clinical implementation. Evidence Level: 3. Technical Efficacy: Stage 2.

Background: Cardiovascular risk factors are associated with cognitive decline and dementia. Timely estimation and targeting of cardiovascular risk burden may contribute towards the prevention of cognitive decline. The cardiovascular risk scores of the European Society of Cardiology’s, SCORE (for adults up to 64 years) and SCORE‐OP (for older adults aged 65 years or more), are commonly used tool for estimation of 10‐year CVD mortality and may reflect the impact of cardiovascular risk burden on brain pathology if validated against neuroimaging biomarkers.Method: This study investigated associations between (i) baseline SCORE/SCORE‐OP and baseline neuroimaging biomarkers (brain magnetic resonance imaging [MRI] and Pittsburgh Compound B‐positron emission tomography [PiB‐PET] measures) (ii) change in SCORE/SCORE‐OP and change in neuroimaging measures during the 2‐year Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER). FINGER targeted at‐risk older adults, aged 60‐77 years, from the general population. Participants were randomized to either multidomain intervention (diet, exercise, cognitive training, and vascular risk management) or control group (general health advice). Neuroimaging (MRI and PiB‐PET) data from baseline and 2‐year visits were used. A total of 112 participants had repeated brain MRI measures (hippocampus, total grey matter, and white matter lesion volumes, and Alzheimer’s disease signature cortical thickness). Repeated PiB‐PET scans were available for 39 participants.Result: Preliminary results suggest that at baseline higher SCORE/SCORE‐OP, indicating increased risk of CVD mortality was significantly associated with lower hippocampus volume (estimate = ‐0.010; p = 0.004), lower total grey matter volume (estimate = ‐0.016; p = 0.007), higher white matter lesion volume (estimate = 0.048; p = 0.015), and lower AD signature cortical thickness (‐0.004; p = 0.048) but not with PiB‐PET. No significant associations were found for change in neuroimaging measures and baseline SCORE/SCORE‐OP or change in SCORE/SCORE‐OP during the 2‐year follow‐up.Conclusion: Cardiovascular risk scores need to be validated against different brain pathology markers in diverse populations. This will enable more precise identification and estimation of dementia risk profiles, guiding at‐risk persons to precise intervention strategies, good for both, heart, and brain.

The CAIDE (Cardiovascular Risk Factors, Aging and Dementia) Risk Score is a validated tool estimating dementia risk. It was previously associated with imaging biomarkers. However, associations between dementia risk scores (including CAIDE) and dementia-related biomarkers have not been studied in the context of an intervention. This study investigated associations between change in CAIDE score and change in neuroimaging biomarkers (brain magnetic resonance imaging [MRI] and Pittsburgh Compound B-positron emission tomography [PiB-PET] measures) during the 2-year Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (post-hoc analyses). FINGER targeted at-risk older adults, aged 60–77 years, from the general population. Participants were randomized to either multidomain intervention (diet, exercise, cognitive training, and vascular risk management) or control group (general health advice). Neuroimaging (MRI and PiB-PET) data from baseline and 2-year visits were used. A toal of 112 participants had repeated brain MRI measures (hippocampal, total gray matter, and white matter lesion volumes, and Alzheimer’s disease signature cortical thickness). Repeated PiB-PET scans were available for 39 participants. Reduction in CAIDE score (indicating lower dementia risk) during the intervention was associated with less decline in hippocampus volume in the intervention group, but not the control group (Randomization group × CAIDE change interaction β coefficient = −0.40, p = .02). Associations for other neuroimaging measures were not significant. The intervention may have benefits on hippocampal volume in individuals who succeed in improving their overall risk level as indicated by a reduction in CAIDE score. This exploratory finding requires further testing and validation in larger studies.

The importance of early interventions in Alzheimer’s disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aβ positive. Compared with the Aβ negative group, the Aβ positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65–0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71–0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aβ positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.

Aims: To investigate the relationship between peripheral leukocyte dynamics and the outcome of large hemispheric infarction (LHI) patients.Methods: Patients with acute LHI admitted to the neuro‐intensive care unit of Xuanwu Hospital from 2013 to 2017 were prospectively enrolled and followed up for 6 months after LHI.Results: A total of 84 LHI patients were included, 38 patients suffered brain herniation and 20 patients died from stroke. Compared to patients with benign course, LHI patients with fatal outcome showed larger infarcts and more severe brain edema (P < .01), as well as increased WBC and neutrophil counts throughout the first week after stroke (P < .05). Correlation analysis revealed that neutrophil counts on D2 after LHI positively correlated with infarct and edema volumes measured from CT/MRI (R² = 0.22 and R² = 0.15, P < .01) and negatively correlated with Glasgow Coma Scale (ρ = −0.234, P < .05). Patients with D2 neutrophils > 7.14 × 10⁹/L had higher risk of brain herniation [odds ratio (OR) = 7.5, 95% CI: 2.0‐28.1, P = .001], and patients with D2 neutrophils > 7.79 × 10⁹/L had a higher risk of death (OR = 5.8, 95% CI: 1.2‐27.0, P = .015).Conclusion: Early peripheral neutrophil count after stroke relates to infarct size and the fatal outcome of LHI patients, which might help guiding acute LHI management such as reduction of intracranial pressure and potential antiinflammatory therapy in the future.

Background: The dural venous sinuses (DVS), in general, are frequently asymmetrical and display far more anatomical variations than arterial systems. A comprehensive study of the anatomy and variants of the DVS can help surgeons in the preoperative evaluation and management as well as minimizing possible complications in the following treatment. Methods: The current review was designed to provide a general overview of the normal anatomy and notable variants of the cerebral venous system as surveyed from the available literature. The pros and cons of different multimodal imaging methods for investigating DVS are also outlined. Finally, cases of various pathological entities are illustrated from our clinical practice. Conclusion: There are many anatomical variations and lesions involving the DVS. MRI examination can provide essential information both on anatomical variation and morphological or functional change of the offending DVS in most circumstances. Multimodal non-invasive venography protocols may become a feasible alternative to the classical digital subtraction angiography and would improve the diagnostic accuracy in future studies.

BACKGROUND AND PURPOSE: The relationship between brain β-amyloid and regional atrophy is still incompletely understood in elderly individuals at risk of dementia. Here, we studied the associations between brain β-amyloid load and regional GM and WM volumes in older adults who were clinically evaluated as being at increased risk of cognitive decline based on cardiovascular risk factors. MATERIALS AND METHODS: Forty subjects (63–81 years of age) were recruited as part of a larger study, the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability. Neuroimaging consisted of PET using ¹¹C Pittsburgh compound-B and T1-weighted 3D MR imaging for the measurement of brain β-amyloid and GM and WM volumes, respectively. All subjects underwent clinical, genetic, and neuropsychological evaluations for the assessment of cognitive function and the identification of cardiovascular risk factors. RESULTS: Sixteen subjects were visually evaluated as showing cortical β-amyloid (positive for β-amyloid). In the voxel-by-voxel analyses, no significant differences were found in GM and WM volumes between the samples positive and negative for β-amyloid. However, in the sample positive for β-amyloid, increases in ¹¹C Pittsburgh compound-B uptake were associated with reductions in GM volume in the left prefrontal (P = .02) and right temporal lobes (P = .04). CONCLUSIONS: Our results show a significant association between increases in brain β-amyloid and reductions in regional GM volume in individuals at increased risk of cognitive decline. This evidence is consistent with a model in which increases in β-amyloid incite neurodegeneration in memory systems before cognitive impairment manifests.