Distinct Spatiotemporal Patterns of White Matter Hyperintensity Progression
Authors: Chung Jinyong , Park Gilsoon , Ryu Wi-Sun , Schellingerhout Dawid , Kim Hang-Rai , Gwak Dong Seok , Haddad Elizabeth , Jahanshad Neda , Kim Beom Joon , Hong Keun-Sik , Oh Hyerin , Jeong Sang-Wuk , Kim Joon-Tae , Park Man Seok , Choi Kang-Ho , Lee Kyungbok , Park Tai Hwan , Park Sang-Soon , Park Jong-Moo , Kang Kyusik , Yu Kyung-Ho , Oh Mi Sun , Lee Soo Joo , Kim Jae Guk , Cha Jae-Kwan , Kim Dae-Hyun , Lee Jun , Han Moon-Ku , Cho Yong-Jin , Lee Byung-Chul , Bae Hee-Joon , Kim Hosung , Kim Dong-Eog 
Published: February 2025

AbstractWhite matter hyperintensity (WMH), a key imaging marker for brain health and the most prevalent brain abnormality in the general population, has prognostic implications for stroke. However, as a prognostic factor, WMH is hard to apply because it varies in both location and size. Reducing this complexity into a set of recognizable patterns would improve the utility of WMH as a cerebrovascular biomarker. In this paper, we show three distinct spatiotemporal patterns of WMH progression in ischemic stroke patients. We used a multicenter MRI database of 9,719 consecutive patients with acute ischemic stroke, plus the UK Biobank database (n= 36,210 controls). Subtype and Stage Inference (SuStaIn) modeling was performed to simultaneously subtype and stage WMH based on longitudinal inference from the Korean multicenter cross-sectional data, after normalizing WMH volumes of our stroke patients to the age-adjusted WMH volumes of low-risk controls (n= 13,811) from the UK Biobank database. Then, we rigorously characterized demographic profiles, vascular risk factors, and stroke outcomes across three different WMH progression trajectories: fronto-parietal (subtype 1: T1), radial (T2), and temporo-occipital (T3) progression. Subtypes remained consistent between baseline and follow-up (median [interquartile range] years of follow-up: 4.5 [2.8□7.3]) in the majority (> 77%) of patients during WMH progression. T1 showed relatively delayed WMH onset and was enriched for women and hypertension, whereas T3 showed earlier WMH onset and was enriched for men, atrial fibrillation, and coronary artery disease. Hypertension and diabetes mellitus were major risk factors that could accelerate WMH progression in all subtypes. The index stroke, i.e., acute symptomatic infarction, was more likely to be due to small vessel occlusion in T1 and cardioembolism in T3. Regarding post-stroke outcomes, early (< 3 weeks) neurological deterioration by symptomatic hemorrhagic transformation and 3-month unfavorable functional outcome were more frequent in T3, while 1-year ischemic stroke recurrence was more frequent in T1. We also found that our WMH subtyping–staging model and the widely used Fazekas WMH scale were consistent while also offering complementary strengths in profiling stroke risk factors and outcomes. Using our model, high-risk controls (without a history of neurological diseases but with vascular risk factors,n= 22,399) in the UK Biobank were assigned to their most likely WMH subtype and stage, demonstrating spatiotemporal patterns of WMH progression that closely paralleled those observed in stroke patients. Of note, we observed distinct stage distributions between high-risk controls and stroke patients, along with a higher area under the curve in the receiver operating curve analysis for distinguishing stroke patients from high-risk controls, compared to that of WMH volume. In conclusion, we identified distinct spatiotemporal trajectories of WMH progression. This WMH subtyping–staging model can capture demographic and vascular risk factors and may provide useful predictions for stroke outcomes across different WMH subtypes and stages. Further prospective investigation is required to confirm whether our model can predict future stroke occurrence more effectively than WMH volume.Short abstractWhite matter hyperintensity (WMH), a key imaging biomarker for brain health, is common in the general population and has prognostic implications for stroke. Using a multicenter MRI dataset of 9,719 stroke patients plus the UK Biobank (n= 36,210 low- and high-risk controls), we employed Subtype and Stage Inference (SuStaIn) modeling and identified three distinct WMH progression subtypes: fronto-parietal (T1), radial (T2), and temporo-occipital (T3). Longitudinal validation confirmed that this classification was stable in the majority of cases. There were three distinct profiles: i) T1 showed delayed WMH onset and more hypertension, while T3 had more atrial fibrillation and coronary heart disease; ii) T1 and T2 were linked to small vessel occlusion, while T3 was linked to cardioembolism; iii) T1 had higher 1-year ischemic recurrence, while T3 showed a higher incidence of early (< 3 weeks) neurological deterioration by symptomatic hemorrhagic transformation and poorer 3-month outcomes. These results were consistent with and complement total or regional Fazekas scale-related findings. We also observed that i) the spatiotemporal patterns of WMH progression in high-risk controls closely paralleled those observed in stroke patients, and ii) distinct stage distributions between the groups enabled greater discriminatory power, compared to crude WMH volume measures, for distinguishing stroke patients from high-risk controls. In conclusion, this new WMH subtyping–staging model can reliably capture WMH progression, demographic profiles, and vascular risk factors, offering improved predictive power for post-stroke outcomes while showing a potential to predict stroke occurrence.