***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background and Purpose — White matter hyperintensity ( WMH ) , or leukoaraiosis , is a radiologic finding generally assumed to reflect diseased small cerebral vasculature . WMH has significant functional impact through its relation to cognitive decline and risk of ischemic and hemorrhagic stroke . Accumulating evidence suggests that some manifestations of small-vessel disease such as intracerebral hemorrhage are associated with low levels of cholesterol . We sought to determine the relation between hyperlipidemia and WMH severity in patients with acute ischemic stroke ( AIS ) . Methods — We analyzed 2 independent , hospital-based AIS cohorts . Demographic and clinical data were collected prospect ively . WMH was measured using semiautomated volumetric image analysis and a semiquantitative visual grading scale . Univariate and multivariable regression analyses were used to assess the relation between WMH severity and study variables . Results — A total of 631 and 504 subjects in the first and second cohorts , respectively , were included . In univariate analyses , advancing age and hypertension were associated with severity of WMH ( P<0.001 ) in both cohorts . In the multivariable analysis , after controlling for age , sex , and significant risk factors in the univariate and age-adjusted analyses , patients with a history of hyperlipidemia had less severe WMH in both cohorts ( P<0.01 ) . Conclusions — Results from 2 independent cohorts demonstrate that AIS patients with a history of hyperlipidemia have less severe WMH at the time of stroke . These data support the hypothesis that hyperlipidemia may play a relatively protective role in cerebral small-vessel disease Objectives Extracellular vesicles ( EVs ) and their protein levels have been identified as a potential risk marker for the development of vascular disease . In the present study , we assessed whether levels of four previously identified EV proteins ( cystatin C , serpin G1 , serpin F2 and CD14 ) are associated with cerebral white matter lesions ( WMLs ) and brain atrophy . Design Cohort study ; cross-sectional and prospect i ve . Setting Single centre , secondary and tertiary setting . Participants 1309 patients with manifest vascular disease from the Second Manifestations of ARTerial disease-MR ( SMART-MR ) study , of which 994 had successful brain MRI and EV protein level measurements . Outcomes WML and brain parenchymal fraction ( BPF ) , as parameter for brain atrophy , at baseline and follow-up . Statistical methods The relationship between EV protein levels and WML volume ( expressed as log transformed percentage of intracranial volume ) and BPF ( expressed percentage of intracranial volume ) on 1.5 T brain MRI was assessed with multivariable linear regression modelling . Subsequently , the relationship between baseline EV protein levels and progression of atrophy and WML was analysed in 534 patients , in whom a follow-up MRI was obtained after 4 years . Results Higher EV-cystatin C and EV-CD14 were significantly associated with larger WML volume ( linear regression coefficient ( 95 % CI ) 0.10 log % /SD ( 0.04 to 0.17 ) and 0.14 log % /SD ( 0.07 to 0.20 ) , respectively . Higher EV-CD14 was associated with more brain atrophy ( –0.14%/SD ; –0.27 to –0.01 ) . Baseline EV-CD14 was significantly associated with increase of WMLs ( 0.11 log % /SD ( 0.04 to 0.18 ) ) . No relationship with EV-serpins was observed at baseline or at follow-up . Conclusions EV proteins cystatin C and CD14 are related to cerebral WMLs and the progression of brain atrophy in patients with manifest vascular disease , potentially identifying EVs in the aetiology of structural brain changes Background and Purpose — Growth differentiation factor-15 ( GDF-15 ) and soluble (s)ST2 are markers of cardiac and vascular stress . We investigated the associations between circulating concentrations of these biomarkers and incident stroke and sub clinical vascular brain injury in a sample from the Framingham Offspring cohort . Methods — We followed 3374 stroke- and dementia-free individuals ( mean age , 59.0±9.7 years ; 53 % women ) attending the Framingham Offspring sixth examination cycle 11.8±3.0 years for incident stroke . A sub sample of 2463 individuals underwent brain magnetic resonance imaging and neuropsychological testing ≈4.0±1.7 years after the sixth examination . Results — After adjustment for traditional cardiovascular risk factors , B-type natriuretic peptide , high-sensitivity C-reactive protein , and urine albumin levels , higher stress biomarker levels were associated cross-sectionally with lower brain volumes ( & bgr ; coefficients for intracranial volume comparing fourth [ Q4 ] versus first biomarker [ Q1 ] quartiles : −0.71 % for GDF-15 ; P=0.002 and 0.47 % for sST2 ; P=0.02 ) and worse performance on the visual reproduction test ( & bgr ; coefficients for Q4 versus Q1 : −0.62 for GDF-15 ; P=0.009 and −0.40 for sST2 ; P=0.04 ) . Higher GDF-15 concentrations were also associated with greater log-transformed white-matter hyperintensity volumes ( & bgr ; for Q4 versus Q1=0.19 ; P=0.01 ) . Prospect ively , a total of 203 ( 6 % ) individuals  developed incident stroke/transient ischemic attack during follow-up . After multivariable adjustment , sST2 remained significantly associated with stroke/transient ischemic attack , hazard ratio for Q4 versus Q1 of 1.76 , 95 % confidence interval of 1.06 to 2.92 , and P=0.03 . Conclusions — Circulating GDF-15 and sST2 are associated with sub clinical brain injury and cognitive impairment . Higher sST2 concentrations are also associated with incident stroke , suggesting potential links between cardiac stress biomarkers and brain injury Accurate automated segmentation of age-related white matter hyperintensity ( WMH ) is desirable for topological studies and those involving large sample s. We assessed the accuracy of a novel automated method for segmentation of WMH on magnetic resonance imaging ( MRI ) in a r and omly selected population -based sample of older people aged > 60 years . The method combined morphological segmentation and statistical classifiers . Validation of this method was performed against expert manual segmentation in a sample of 30 scans , and against semi-automated segmentation in 202 scans . Its performance was also compared with those of other known methods derived from simple thresholding or Gaussian mixture modelling . Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation , with an intraclass correlation coefficient ( ICC ) of 0.90 ( 95 % confidence interval [ CI ] , 0.80 - 0.95 ) for WMH volume and median similarity index ( SI ) of 0.58 ( interquartile range [ IQR ] 0.50 - 0.65 ) . The method also showed similarly high levels of agreement with semi-automated segmentation , with ICC 0.92 ( 95 % CI 0.89 - 0.93 ) and median SI 0.56 ( IQR 0.49 - 0.66 ) . Its best performance was observed for the highest tertile of WMH volume . Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study Objective : Our aim was to test the association of vascular risk factor exposure in midlife with progression of MRI markers of brain aging and measures of cognitive decline . Methods : A total of 1,352 participants without dementia from the prospect i ve Framingham Offspring Cohort Study were examined . Multivariable linear and logistic regressions were implemented to study the association of midlife vascular risk factor exposure with longitudinal change in white matter hyperintensity volume ( WMHV ) , total brain volume ( TBV ) , temporal horn volume , logical memory delayed recall , visual reproductions delayed-recall ( VR-d ) , and Trail-Making Test B-A ( TrB-A ) performance a decade later . Results : Hypertension in midlife was associated with accelerated WMHV progression ( p < 0.001 ) and worsening executive function ( TrB-A score ; p = 0.012 ) . Midlife diabetes and smoking were associated with a more rapid increase in temporal horn volume , a surrogate marker of accelerated hippocampal atrophy ( p = 0.017 and p = 0.008 , respectively ) . Midlife smoking also predicted a more marked decrease in total brain volume ( p = 0.025 ) and increased risk of extensive change in WMHV ( odds ratio = 1.58 [ 95%confidence interval 1.07–2.33 ] , p = 0.021 ) . Obesity in midlife was associated with an increased risk of being in the top quartile of change in executive function ( 1.39 [ 1.02–1.88 ] , p = 0.035 ) and increasing waist-to-hip ratio was associated with marked decline in TBV ( 10.81 [ 1.44–81.01 ] , p = 0.021 ) . Longitudinal changes in brain structure were significantly correlated with decline in memory and executive function . Conclusions : Midlife hypertension , diabetes , smoking , and obesity were associated with an increased rate of progression of vascular brain injury , global and hippocampal atrophy , and decline in executive function a decade later Objective Brain tissue segmentation by conventional threshold-based techniques may have limited accuracy and repeatability in older subjects . We present a new multispectral magnetic resonance ( MR ) image analysis approach for segmenting normal and abnormal brain tissue , including white matter lesions ( WMLs ) . Methods We modulated two 1.5 T MR sequences in the red/green colour space and calculated the tissue volumes using minimum variance quantisation . We tested it on 14 subjects , mean age 73.3 ± 10 years , representing the full range of WMLs and atrophy . We compared the results of WML segmentation with those using FLAIR-derived thresholds , examined the effect of sampling location , WML amount and field inhomogeneities , and tested observer reliability and accuracy . Results FLAIR-derived thresholds were significantly affected by the location used to derive the threshold ( P = 0.0004 ) and by WML volume ( P = 0.0003 ) , and had higher intra-rater variability than the multispectral technique ( mean difference ± SD : 759 ± 733 versus 69 ± 326 voxels respectively ) . The multispectral technique misclassified 16 times fewer WMLs . Conclusion Initial testing suggests that the multispectral technique is highly reproducible and accurate with the potential to be applied to routinely collected clinical MRI data Objective To examine the relationship between white matter hyperintensities and headache . Methods White matter hyperintensities burden was assessed semi-quantitatively using Fazekas and Scheltens scales , and by manual and automated volumetry of MRI in a sub- study of the general population -based Nord-Trøndelag Health Study ( HUNT MRI ) . Using vali date d question naires , participants were categorized into four cross-sectional headache groups : Headache-free ( n = 551 ) , tension-type headache ( n = 94 ) , migraine ( n = 91 ) , and unclassified headache ( n = 126 ) . Prospect i ve question naire data was used to further categorize participants into groups according to the evolution of headache during the last 12 years : Stable headache-free , past headache , new onset headache , and persistent headache . White matter hyperintensities burden was compared across headache groups using adjusted multivariate regression models . Results Individuals with tension-type headache were more likely to have extensive white matter hyperintensities than headache-free subjects , with this being the case across all methods of white matter hyperintensities assessment ( Scheltens scale : Odds ratio , 2.46 ; 95 % CI , 1.44–4.20 ) . Migraine or unclassified headache did not influence the odds of having extensive white matter hyperintensities . Those with new onset headache were more likely to have extensive white matter hyperintensities than those who were stable headache-free ( Scheltens scale : Odds ratio , 2.24 ; 95 % CI , 1.13–4.44 ) . Conclusions Having tension-type headache or developing headache in middle age was linked to extensive white matter hyperintensities . These results were similar across all methods of assessing white matter hyperintensities . If white matter hyperintensities treatment strategies emerge in the future , this association should be taken into consideration BACKGROUND AND AIMS Aortic atherosclerosis is an aggregate marker of vascular risk factor exposure and has been associated with intracranial atherosclerosis and stroke . We hypothesized that atherosclerosis of the descending aorta ( DAo ) could be a risk marker for brain aging and injury . METHODS We evaluated 1527 participants ( mean age 59.9 years , 53.5 % women ) in the Framingham Offspring cohort who underwent both aortic and brain MRI . Participants were free of clinical stroke , dementia , or other neurological illness at the time of axial MRI of the thoracic and abdominal DAo and subsequent brain MRI .  We related the prevalence and burden of aortic plaque to total cerebral brain volume ( TCBV ) and white matter hyperintens 
    Output:
    
Most addressed topics were risk factors and secondary clinical conditions .