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Background White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. Methods and Results We defined areas of stable normal‐appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1‐year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross‐sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross‐sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006–0.017]; −0.002 [95% CI, −0.008 to 0.003] and −0.003 [95% CI, −0.009 to 0.004]); in progressing and stable WMHs, FA decreased (−0.022 [95% CI, −0.027 to −0.017] and −0.009 [95% CI, −0.011 to −0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050–0.063], 0.058 [95% CI, 0.050 –0.066]; stable WMHs, 0.054 [95% CI, 0.045–0.063], 0.049 [95% CI, 0.039–0.058]); and in stable normal‐appearing white matter, MD increased (0.004 [95% CI, 0.003–0.005]), whereas FA and T1 slightly decreased and increased (−0.002 [95% CI, −0.004 to −0.000] and 0.005 [95% CI, 0.001–0.009]). Conclusions Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.

Introduction: White matter hyperintensities (WMH) are related to cognitive decline, particularly processing speed and executive functioning (EF). However, all cognitive domains might be affected. WMH can regress and this might have positive consequences for cognition, but the effects of WMH regression on cognition are unknown. This study aims to assess whether changes in global cognition, processing speed and EF are related to progressing and regressing WMH. Methods: We recruited mild, non-disabling, ischemic stroke patients with sporadic small vessel disease. They underwent MRI and cognitive assessment within 3 months post-stroke and 1 year later. Cognitive assessment included MoCA (global cognition; score 0-30), trail making test (TMT) A (processing speed; seconds) and TMT B (EF; seconds). WMH volumes are reported as % intracranial volume (ICV). WMH volume change (%ICV) is defined as WMH volume difference between baseline and 1 year. We calculated quintiles (Q) of WMH volume change to group volume change. We performed three linear mixed models, with MoCA, TMT-A and B as outcomes, to assess relationships over time with quintile of WMH volume change. Predictors in addition to quintile are age, sex, premorbid intelligence (NART), stroke severity (NIHSS). Extra predictor for TMT-B analysis is the TMT-A time to correct for speed. Results: 202 participants had WMH change volumes available (mean age: 66.03 years [SD=11.15), 33% female]. Q1 reflect greatest WMH volume reduction and Q5 greatest volume increase, while Q3 is lowest overall volume change (Figure 1). MoCA score increased over time (Figure 2). Older age (std. B [std. 95%CI]: -0.303 [-0.400, -0.206]), worse NART (-0.463 [-0.557, -0.368]), increasing NIHSS (-0.208 [-0.286, -0.130]) and more WMH increase (Q5; -0.513 [-0.814, -0.211]) predict lower MoCA. Older age (0.257 [0.135, 0.380]), worse NART (0.259 [0.139, 0.378]) and higher NIHSS (0.152 [0.078, 0.226]) predict worse TMT-A time (Figure 3A). Older age (0.224 [0.132, 0.316]), worse NART (0.295 [0.207, 0.383]), worse TMT-A (0.469 [0.384, 0.555]), worsening WMH volume (Q2; 0.382 [0.115, 0.0.649] and Q5; 0.425 [0.150, 0.699]) predict worse TMT-B time (Figure 3B). Discussion: More WMH progression independently predicts worse global cognition and EF but not processing speed. WMH regression (Q1) was not related to worsening or improving global cognition, processing speed and EF. Cognitive consequences of WMH regression might be comparable to stable WMH (Q3).

Introduction: Factors associated with incident infarcts after stroke are unclear. We aimed to determine whether subsequent incident infarcts continue to develop one year post-stroke and how incident infarcts relate to baseline imaging features, vascular risks, and cognitive outcomes. Methods: We recruited patients with non-disabling stroke. After diagnostic MRI, we repeated MRI at 3-6 monthly intervals for 12 months, visually assessing incident infarcts on DWI or FLAIR. We used logistic regression to determine associations with incident infarcts, including baseline vascular risks, SVD score, and index stroke subtype. We quantified 7-level ordinal cognitive outcome status at one year, using MoCA/telephone MoCA and modified Rankin Scale.[1,2] We used ordinal regression to determine whether cognitive outcomes associated with incident infarcts and baseline age, mRS, MoCA, and WMH volume. Results: We recruited 229 participants, mean age 65.9 (SD 11.1) years; 77/229 (33.6%) female; 130/229 (56.8%) index lacunar stroke. From baseline to one-year MRI, we detected 117 incident infarcts in n=57/229 participants at 80 visits. Most were small subcortical infarcts: 86/117 (73.5%) infarcts in n=38/57 (66%). N=39 participants had incident infarcts at one visit; n=14 at two visits; n=3 at three visits, and n=1 at four visits. Nineteen participants had multiple incident infarcts at a single visit. Baseline summary SVD score was the strongest predictor of incident infarcts (aOR 1.74, 95%CI 1.29-2.41, Figure 1). At one year, 10/218 (4.6%) participants met criteria for single-domain and 62/218 (28.4%) for multi-domain neurocognitive disorder; 15/218 (6.8%) for mild dementia; 2/218 (0.9%) for moderate dementia; none severe; 3/229 (1.3%) had died. Participants’ odds of impaired one-year cognition increased for every one-unit increment in baseline mRS (aOR=2.03 [1.30-3.10]) and decreased for every one-unit increment in baseline MoCA (aOR=0.78 [0.71-0.85]). For participants with incident small subcortical infarcts, the odds of impaired cognition were 23% higher than for incident cortical infarcts, though not statistically significant (aOR 1.23 [0.55-2.1])(Figure 2). Discussion: In a mild stroke population, incident infarcts, mostly small subcortical, occur in one quarter and associate with worse baseline SVD. Minor neurocognitive disorder occurs in one third and associates with baseline mRS, MoCA, and trends towards incident small subcortical rather than cortical infarcts.

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study. We evaluate our framework on paired low- and high-resolution brain MRI structural full scans (i.e., T1-, T2-weighted, and FLAIR sequences are simultaneously input) obtained in clinical and research settings from scanners manufactured by Siemens, Phillips, and GE. We show that the upsampled MRIs are qualitatively faithful to the ground-truth high-quality scans (PSNR = 35.39; MAE = 3.78E−3; NMSE = 4.32E−10; SSIM = 0.9852; mean normal-appearing gray/white matter ratio intensity differences ranging from 0.0363 to 0.0784 for FLAIR, from 0.0010 to 0.0138 for T1-weighted and from 0.0156 to 0.074 for T2-weighted sequences). The automatic raw segmentation of tissues and lesions using the super-resolved images has fewer false positives and higher accuracy than those obtained from interpolated images in protocols represented with more than three sets in the training sample, making our approach a strong candidate for practical application in clinical and collaborative research.

Post-stroke cognitive impairment is common and can have major impact on life after stroke. Peak-width of Skeletonized Mean Diffusivity (PSMD) is a diffusion imaging marker of white matter microstructure and is also associated with cognition. Here, we examined associations between PSMD and post-stroke global cognition in an ongoing study of mild ischemic stroke patients. We studied cross-sectional associations between PSMD and cognition at both 3-months (N = 229) and 1-year (N = 173) post-stroke, adjusted for premorbid IQ, sex, age, stroke severity and disability, as well as the association between baseline PSMD and 1-year cognition. At baseline, (mean age = 65.9 years (SD = 11.1); 34% female), lower Montreal Cognitive Assessment (MoCA) scores were associated with older age, lower premorbid IQ and higher stroke severity, but not with PSMD (βstandardized = −0.116, 95% CI −0.241, 0.009; p = 0.069). At 1-year, premorbid IQ, older age, higher stroke severity and higher PSMD (βstandardized = −0.301, 95% CI −0.434, −0.168; p < 0.001) were associated with lower MoCA. Higher baseline PSMD was associated with lower 1-year MoCA (βstandardized = −0.182, 95% CI −0.308, −0.056; p = 0.005). PSMD becomes more associated with global cognition at 1-year post-stroke, possibly once acute effects have settled. Additionally, PSMD in the subacute phase after a mild stroke could help predict long-term cognitive impairment.

Background: Sporadic cerebral small vessel disease (SVD) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) share clinical and neuroimaging features and possibly vascular dysfunction(s). However few studies have included both conditions, assessed more than one vascular dysfunction simultaneously, or included more than one centre. The INVESTIGATE-SVDs study will assess several cerebrovascular dysfunctions with MRI in participants with sporadic SVD or CADASIL at three European centres. Methods: We will recruit participants with sporadic SVDs (ischaemic stroke or vascular cognitive impairment) and CADASIL in Edinburgh, Maastricht and Munich. We will perform detailed clinical and neuropsychological phenotyping of the participants, and neuroimaging including structural MRI, cerebrovascular reactivity MRI (CVR: using carbon dioxide challenge), phase contrast MRI (arterial, venous and CSF flow and pulsatility), dynamic contrast-enhanced MRI (blood brain barrier (BBB) leakage) and multishell diffusion imaging. Participants will measure their blood pressure (BP) and its variability over seven days using a telemetric device. Discussion: INVESTIGATE-SVDs will assess the relationships of BBB integrity, CVR, pulsatility and CSF flow in sporadic SVD and CADASIL using a multisite, multimodal MRI protocol. We aim to establish associations between these measures of vascular function, risk factors particularly BP and its variability, and brain parenchymal lesions in these two SVD phenotypes. Additionally we will test feasibility of complex multisite MRI, provide reliable intermediary outcome measures and sample size estimates for future trials.

Background White matter hyperintensity (WMH) progression is well documented; WMH regression is more contentious, which might reflect differences in defining WMH change. We compared four existing WMH change definitions in one population to determine the effect of definition on WMH regression.Methods We recruited patients with minor non-disabling ischaemic stroke who underwent MRI 1–3 months after stroke and 1 year later. We assessed WMH volume (in absolute mL and % intracranial volume) and applied four different definitions, including two thresholds (based on SD or mL), percentile and quintile approaches.Results In 198 participants, mean age 65.5 (SD=11.13), baseline WMH volume was 15.46 mL (SD=19.2), the mean net WMH volume change was 0.98 mL (SD=2.84), range −7.98 to +12.84 mL. Proportion regressing/stable/progressing WMH were threshold 1 (SD), 29.8%/55.6%/14.6%; threshold 2(mL), 29.8%/16.7%/53.5%; percentile approach, 28.3%/21.2%/50.5%. The quintile approach includes five groups with quintile 3 reflecting no change (N=40), quintiles 1 and 2 any WMH decrease (N=80) and quintiles 4 and 5 any WMH increase (N=78).Conclusions Different WMH change definitions cause big differences in how participants are categorised; additionally, non-normal WMH distribution precludes use of some definitions. Consistent use of an appropriate definition would facilitate data comparisons, particularly in clinical trials of potential WMH treatments.