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![Severity of MRI-detected white matter hyperintensity. Total burden of white matter disease varies significantly among asymptomatic adults and patients with known cerebrovascular disease. In age-matched individuals, white matter hyperintensity (WMH) volume may vary from mild to very severe ( upper panel ). Using validated semi-automated volumetric protocol, WMH volume can be quantified ( lower panel , in red , WMH maps are derived from contiguous supratentorial axial T2-FLAIR MRI slices using previously published method [10]) with a high degree of accuracy and precision.](profile/Aurauma-Chutinet/publication/260107838/figure/fig1/AS:297309255553024@1447895458284/Severity-of-MRI-detected-white-matter-hyperintensity-Total-burden-of-white-matter.png)
Severity of MRI-detected white matter hyperintensity. Total burden of white matter disease varies significantly among asymptomatic adults and patients with known cerebrovascular disease. In age-matched individuals, white matter hyperintensity (WMH) volume may vary from mild to very severe ( upper panel ). Using validated semi-automated volumetric protocol, WMH volume can be quantified ( lower panel , in red , WMH maps are derived from contiguous supratentorial axial T2-FLAIR MRI slices using previously published method [10]) with a high degree of accuracy and precision.
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Opinion statement:
White matter disease is commonly detected on brain MRI of aging individuals as white matter hyperintensities (WMH), or 'leukoaraiosis." Over the years, it has become increasingly clear that the presence and extent of WMH is a radiographic marker of small cerebral vessel disease and an important predictor of the lifelong risk of...
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Context 1
... are primarily two radiographic approaches to the assessment of WMH severity ( Fig. 1). First, the visual rating scales that are used to measure white matter lesions (WMLs) on CT or MRI are based on the location and severity of white matter disease. MRI-based scales, such as the Fazekas scale, rate both periventricular hyperintensity (PVH) and deep white matter locations of WML (score 0 – 3) [5]. The Scheltens scale adds the location, size, and number of WML in PVH (score 0 – 6), WMH (score 0 – 24), basal ganglia hyperintensities (BG) (score 0 – 30), and infratentorial foci of hyperintensities (ITF) (score 0 – 24) [6]. The Rotterdam Scan Study (RSS) scale rates WML in the periventricular region (score 0 – 9) and subcortical WML [7]. The Age-Related White Matter Changes (ARWMC) scale for rating WMH on CT and MRI include the location, size, and number of WMLs (score 0 – 3) and basal ganglia lesions (score 0 – 3) in five different regions (frontal, parieto-occipital, temporal, basal ganglia, and infratentorial) of the bilateral hemispheres [8]. Second, a volumetric approach to WMH analysis has been based on the various semi-automated protocols, using analytical software such as Sparc 5 (SUN, Palo Alto, CA) [9] or MRIcro (University of Nottingham School of Psychology, Nottingham, UK; www.mricro.com), which quantify WMH volume (WMHv) on axial FLAIR of either the entire brain or just the supratentorial region [10] (Fig. 1). Albeit more labor-intense, volumetric methods provide more accuracy and reliability in assessing WMH severity, especially when evaluating WMH progression, as compared to the visual rating scales [11, 12]. Some WMH progression scales, such as the Rotterdam Progression and Schmidt Progression scales, are also sensitive, consistent, and relate to volumetric volume change [12]. Clinical application of different scales may depend on their sensitivity for a specific functional domain. For example, in the Leukoaraiosis And DISability (LADIS) study, visual rating scale and volumetric methods demonstrated greater WMH burden in subjects with impaired physical performance and cognition as compared to normal controls [13]. However, volumetric assessment was more sensitive than visual scores in detecting memory symptoms [9]. Many studies have used WMHv to evaluate the association between leukoaraiosis and risk of stroke, cognitive impairment, dementia, mortality, stroke severity, and other functional disabilities. Six large prospective population-based studies provided pivotal evidence of correlation between WMH and risk of first-ever stroke (Table 1). The Atherosclerosis Risk in Communities (ARIC) study followed up 1,684 persons in four U.S. communities for 4.7 years and found that people with WML had a higher 5-year cumulative incidence of clinical stroke than people without WML (6.8 % vs. 1.4 %; RR 3.4; 95 %.CI, 1.5 – 7.7), independent of common stroke risk factors [14]. The Rotterdam Scan Study in the Netherlands, which followed 1,077 participants for an average of 4.2 years, demonstrated that WML in both periventricular and subcortical locations significantly increased the risk of stroke [15]. The Cardiovascular Health Study (CHS) included 3,293 persons in four U.S. communities followed for an average of 7 years, and showed that the risk of stroke increased proportionally to the WMH grade increase, independent of conventional stroke risk factors [16]. A prospective cohort study in Shimane, Japan, demonstrated that marked PVH and subcortical white matter lesion (SWML) burden independently increased the risk of stroke in 2,684 subjects followed for an average of 6.3 years [17]. The Three-City (3C) study in Dijon, France, which followed 1,643 persons for an average 4.9 years, showed a significant increase in the risk of stroke with increasing WML [18]. And finally, the Framingham Offspring Study, which systematically evaluated 2,177 persons during 5.6 years of follow-up, demonstrated that greater WMHv was associated with an increased risk of stroke (HR 2.28, 95 % CI, 1.02 – 5.13), independent of vascular risk factors [19]. The meta-analysis of these six large population-based cohort studies demonstrated a significant association of WMH with risk of stroke (HR 3.1, 95 % CI, 2.3 – 4.1, p G 0.001) [20 • ]. Furthermore, a pooled population-based analysis of the ARIC and CHS, which included 4,872 stroke-free individuals followed for a median of 13 years, demonstrated that higher WMH grade on baseline MRI is a significant predictor of spontaneous intracerebral hemorrhage (ICH) (p for trend G 0.0001) [21]. Many prospective studies have evaluated WMH and risk of stroke in high-risk populations (Table 1). A study in 89 Japanese participants who had clinical lacunar infarction and who were followed up for a mean of 51 months found that extensive WMH at baseline was a significant predictor of stroke risk (RR 1.60; 95 % CI, 1.02 – 2.54, p G 0.05) [22]. A study in 121 American patients with lobar ICH demonstrated that CT-based evidence of white matter damage nearly quadrupled the risk of ...
Context 2
... are primarily two radiographic approaches to the assessment of WMH severity ( Fig. 1). First, the visual rating scales that are used to measure white matter lesions (WMLs) on CT or MRI are based on the location and severity of white matter disease. MRI-based scales, such as the Fazekas scale, rate both periventricular hyperintensity (PVH) and deep white matter locations of WML (score 0 – 3) [5]. The Scheltens scale adds the location, size, and number of WML in PVH (score 0 – 6), WMH (score 0 – 24), basal ganglia hyperintensities (BG) (score 0 – 30), and infratentorial foci of hyperintensities (ITF) (score 0 – 24) [6]. The Rotterdam Scan Study (RSS) scale rates WML in the periventricular region (score 0 – 9) and subcortical WML [7]. The Age-Related White Matter Changes (ARWMC) scale for rating WMH on CT and MRI include the location, size, and number of WMLs (score 0 – 3) and basal ganglia lesions (score 0 – 3) in five different regions (frontal, parieto-occipital, temporal, basal ganglia, and infratentorial) of the bilateral hemispheres [8]. Second, a volumetric approach to WMH analysis has been based on the various semi-automated protocols, using analytical software such as Sparc 5 (SUN, Palo Alto, CA) [9] or MRIcro (University of Nottingham School of Psychology, Nottingham, UK; www.mricro.com), which quantify WMH volume (WMHv) on axial FLAIR of either the entire brain or just the supratentorial region [10] (Fig. 1). Albeit more labor-intense, volumetric methods provide more accuracy and reliability in assessing WMH severity, especially when evaluating WMH progression, as compared to the visual rating scales [11, 12]. Some WMH progression scales, such as the Rotterdam Progression and Schmidt Progression scales, are also sensitive, consistent, and relate to volumetric volume change [12]. Clinical application of different scales may depend on their sensitivity for a specific functional domain. For example, in the Leukoaraiosis And DISability (LADIS) study, visual rating scale and volumetric methods demonstrated greater WMH burden in subjects with impaired physical performance and cognition as compared to normal controls [13]. However, volumetric assessment was more sensitive than visual scores in detecting memory symptoms [9]. Many studies have used WMHv to evaluate the association between leukoaraiosis and risk of stroke, cognitive impairment, dementia, mortality, stroke severity, and other functional disabilities. Six large prospective population-based studies provided pivotal evidence of correlation between WMH and risk of first-ever stroke (Table 1). The Atherosclerosis Risk in Communities (ARIC) study followed up 1,684 persons in four U.S. communities for 4.7 years and found that people with WML had a higher 5-year cumulative incidence of clinical stroke than people without WML (6.8 % vs. 1.4 %; RR 3.4; 95 %.CI, 1.5 – 7.7), independent of common stroke risk factors [14]. The Rotterdam Scan Study in the Netherlands, which followed 1,077 participants for an average of 4.2 years, demonstrated that WML in both periventricular and subcortical locations significantly increased the risk of stroke [15]. The Cardiovascular Health Study (CHS) included 3,293 persons in four U.S. communities followed for an average of 7 years, and showed that the risk of stroke increased proportionally to the WMH grade increase, independent of conventional stroke risk factors [16]. A prospective cohort study in Shimane, Japan, demonstrated that marked PVH and subcortical white matter lesion (SWML) burden independently increased the risk of stroke in 2,684 subjects followed for an average of 6.3 years [17]. The Three-City (3C) study in Dijon, France, which followed 1,643 persons for an average 4.9 years, showed a significant increase in the risk of stroke with increasing WML [18]. And finally, the Framingham Offspring Study, which systematically evaluated 2,177 persons during 5.6 years of follow-up, demonstrated that greater WMHv was associated with an increased risk of stroke (HR 2.28, 95 % CI, 1.02 – 5.13), independent of vascular risk factors [19]. The meta-analysis of these six large population-based cohort studies demonstrated a significant association of WMH with risk of stroke (HR 3.1, 95 % CI, 2.3 – 4.1, p G 0.001) [20 • ]. Furthermore, a pooled population-based analysis of the ARIC and CHS, which included 4,872 stroke-free individuals followed for a median of 13 years, demonstrated that higher WMH grade on baseline MRI is a significant predictor of spontaneous intracerebral hemorrhage (ICH) (p for trend G 0.0001) [21]. Many prospective studies have evaluated WMH and risk of stroke in high-risk populations (Table 1). A study in 89 Japanese participants who had clinical lacunar infarction and who were followed up for a mean of 51 months found that extensive WMH at baseline was a significant predictor of stroke risk (RR 1.60; 95 % CI, 1.02 – 2.54, p G 0.05) [22]. A study in 121 American patients with lobar ICH demonstrated that CT-based evidence of white matter damage nearly quadrupled the risk of recurrent ICH (HR 3.7, 95 % CI, 1.1-12.3, p =0.02) after 2.7 years of follow-up [10]. A study of 81 Swedish patients with lacunar infarction found that severity of WML was a predictor of recurrent stroke (OR 1.7, 95 % CI, 1.2-2.7), in long-term follow-up (5 years) [23]. Similarly, extensive WML was associated with recurrent stroke in a study of 228 Chinese patients with stroke ( p =0.0001) [24]. The Amsterdam Vascular Medicine Group in the Netherlands reported that patients ( n =230) with confirmed atherosclerotic disease – including recent myocardial infarction (MI), ischemic stroke (IS), or peripheral arterial disease (PAD) – and evidence of PVH on neuroimaging had a higher recurrent ischemic stroke rate at 3.5 years compared to those without PVH (18 % vs. 5 %, p =0.001) [25]. A study in 266 Japanese patients with ischemic stroke or ICH found that the subgroup of patients with advanced WMH but no microbleeds had the highest recurrence rate of ischemic stroke of the four patient subgroups (10.5 % in 1-year and 17.4 % in 2-year follow-up, HR 10.7, 95 % CI, 2.6 – 43.7) [26]. Finally, combined data analyses demonstrate convincingly that WMH severity is linked to the risk of recurrent stroke. A meta-analysis from three studies in high-risk populations reported HR 7.4, 95 % CI, 2.4 – 22.9, p =0.001, whereas pooled data from six ...
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1.1 Objective
In CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), white matter hyperintensities (WMH) are considered to result from hypoperfusion. We hypothesized that in fact the burden of WMH results from the combination of several regional populations of WMH with different mechanisms and clini...
Cerebral microbleeds (CMBs) are increasingly recognized neuroimaging findings, occurring with cerebrovascular disease, dementia, and aging. CMBs are associated with subsequent hemorrhagic and ischemic stroke, and also with an increased risk of cognitive deterioration and dementia. They occur in the setting of impaired small vessel integrity due to...
Cardiovascular risk factors are implicated in cerebrovascular disease, resulting in cognitive impairment. This study investigated the relationship between objective cardiac markers and cerebral changes in older Indian adults with and without dementia. Dementia patients with major electrocardiographic (EKG) abnormalities were 8.19 times more likely...
Citations
... Finally, recent meta-analysis found that WMH volume estimation was not comparable between studies due to the lack of standardization in the definition of WMH and the high technical variability in assessment (Melazzini et al., 2021). Despite the lack of evidence for WMH volume references intervals, the literature strongly supports the role of WMH as a biomarker of longstanding cerebrovascular disease, and its implication in the pathophysiology of stroke and cognitive impairment or dementia (Chutinet and Rost, 2014). With the intention of advancing in the search for possible thresholds for this imaging marker we have calculated and detailed reported our findings and procedure, based on a referenced method for defining biomarker cut points for brain aging (Jack et al., 2017), to facilitate further replication. ...
Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer’s disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.
... WML may also reflect inflammatory diseases such as multiple sclerosis, tumors, or vascular lesions. Accurate detection of these lesions has the potential to inform the diagnosis of the underlying disorders by characterizing their appearance and distribution, which in turn guides prognostication of the disease course and opportunities for earlier clinical intervention 13,14 . Precise delineation of their boundaries and quantification of disease burden in drug development research 15 and future clinical settings may also shed light on the underlying disease patterns OPEN ...
White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation ( r = 0.895, p -value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention.
... Fluid Attenuation Inversion Recovery (FLAIR) MRI is preferred for WML analysis (Badji and Westman, 2020;Wardlaw et al., 2013;Ghafoorian et al., 2016;Chutinet and Rost, 2014), since the high signal from the cerebrospinal fluid (CSF) in T2 is suppressed, thus highlighting white matter disease (Lao et al., 2008). This is due to increased water content secondary to ischemia and demyelination are much more robustly seen in FLAIR than with T1/T2 (Gorelick et al., 2011). ...
A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how ”structured” or ”damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.
... White matter hyperintensities, also called leukoaraiosis, are seen in brain MRI in elderly patients, due to ischemic abnormalities in deep white matter [5]. These white matter lesions may be seen as patchy, punctate, or confluent lesions [6]. ...
... (Figs. 3,4,and 5). ...
Cerebral metastases are serious complications of systemic tumors. Ischemic lesions are the main differential diagnosis of cerebral metastasis. The aim of this study is to evaluate the efficiency of signal intensity ratio (SIR) in non-contrast-enhanced fluid attenuation inversion recovery (FLAIR) sequence in the detection of cerebral metastasis. Patients with brain metastasis and evaluated with brain MRI in our institution, between 2015 and 2017, were included in this study. Brain metastases were detected in contrast-enhanced fat-saturated T1-weighted images. In pre-contrast FLAIR sequences, signal intensities (SI) from metastasis, perilesional white matter, and, if present, ischemic lesion were measured. In all patients, normal white matter SI was recorded, and SI ratio (SIR = SI lesion/SI reference white matter) was calculated for standardization. A total of 59 patients with brain metastasis detected in contrast-enhanced brain MRI were included in this study. A significant difference was detected between mean metastasis SIR and mean ischemic SIR (P = 0.001); mean perilesional SIR and mean ischemic SIR (p = 0.033); minimum metastasis SIR and minimum perilesional SIR (P = 0.012); and maximum metastasis SIR and maximum ischemic SIR (p = 0.04). SIR of hyperintense lesions in FLAIR sequences may be helpful in differentiating brain metastasis from ischemic WM lesions, especially in patients contrast media is contraindicated.
... W hite matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter. 1 Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer's disease and vascular dementia, and increased risk of stroke. 2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research. 4,5 Although the extent of WMHs can be visually scored, 6 the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. ...
BACKGROUND AND PURPOSE: Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.
... W hite matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter. 1 Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer's disease and vascular dementia, and increased risk of stroke. 2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research. 4,5 Although the extent of WMHs can be visually scored, 6 the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. ...
Background and purpose:
Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.
Materials and methods:
Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer's Disease Neuroimaging Initiative-3 (n = 20).
Results:
StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.
Conclusions:
A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.
... In the language domain, white-matter structural connections have been correlated with language deficits mostly in post-stroke aphasia [10][11][12][13] and recently in PPA [14][15][16][17][18][19][20][21] . The degree of white-matter tract disruption has been correlated strongly and repeatedly with language deficits to an even greater extent than grey-matter lesions 22,23 . ...
Background
Transcranial direct current stimulation (tDCS), in conjunction with language therapy, improves language therapy outcomes in primary progressive aphasia (PPA). However, no studies show whether white matter integrity predicts language therapy or tDCS effects in PPA.
Objective
We aimed to determine whether white matter integrity, measured by diffusion tensor imaging (DTI), predicts written naming/spelling language therapy effects (letter accuracy on trained and untrained words) with and without tDCS over the left inferior frontal gyrus (IFG) in PPA.
Methods
Thirty-nine participants with PPA were randomly assigned to tDCS or sham condition, coupled with language therapy for 15 daily sessions. White matter integrity was measured by mean diffusivity (MD) and fractional anisotropy (FA) in DTI scans before therapy. Written naming outcomes were evaluated before, immediately after, 2 weeks, and 2 months posttherapy. To assess tDCS treatment effect, we used a mixed-effects model with treatment evaluation and time interaction. We considered a forward model selection approach to identify brain regions/fasciculi of which white matter integrity can predict improvement in performance of word naming.
Results
Both sham and tDCS groups significantly improved in trained items immediately after and at 2 months posttherapy. Improvement in the tDCS group was greater and generalized to untrained words. White matter integrity of ventral language pathways predicted tDCS effects in trained items whereas white matter integrity of dorsal language pathways predicted tDCS effects in untrained items.
Conclusions
White matter integrity influences both language therapy and tDCS effects. Thus, it holds promise as a biomarker for deciding which patients will benefit from language therapy and tDCS.
... weighted Magnetic Resonance Images (MRI) (11)(12)(13), corresponding to myelin loss and mild gliosis (12). These lesions normally start to develop after 60 years of age (14), and baseline severity of WML is perhaps the most consistent predictor of progression (10). ...
... These lesions normally start to develop after 60 years of age (14), and baseline severity of WML is perhaps the most consistent predictor of progression (10). WML has indicated an increased risk for stroke, and these lesions are also prevalent in patients with hypertension, hypercholesterolemia, or diabetes (10,11,12,13). However, WML are common among patients with brain tumour subjected to CRT, with a significant correlation to the radiation dose and the time interval since completion of CRT (15). ...
... This article is protected by copyright. All rights reserved years in men (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), and median age at investigation was 35 years (18-56) in women, and 36 years in men. Median time since the first operation was 21 years in women, and 23 years in men. ...
Context
White matter lesions (WML) are caused by obstruction of small cerebral vessels associated with stroke risk. Craniopharyngioma (CP) patients suffer from increased cerebrovascular mortality.
Objective
To investigate the effect of reduced HT volume and cranial radiotherapy (CRT) on WML in childhood onset CP patients.
Design
A cross‐sectional study of 41 patients (24 women) surgically treated childhood onset CP in comparison to controls.
Setting
The South Medical Region of Sweden (2.5 million inhabitants).
Methods
With Magnetic Resonance Imaging (MRI) we analysed qualitative measurement of WML based on the visual rating scale of Fazekas and quantitative automated segmentation of WML lesion. Also measurement HT volume and of cardiovascular risk factors were analysed.
Results
Patients had a significant increase in WML volume (ml) (P=0.001) compared to controls. Treatment with cranial radiotherapy (CRT) vs no CRT was associated with increased WML volume (P=0.02) as well as higher Fazekas score (P=0.001). WML volume increased with years after CRT (r = 0.39; P = 0.02), even after adjustment for fat mass and age. A reduced HT volume was associated with increased WML volume (r = ‐ 0.61, P < 0.001) and explained 26% of the variation (r² = 0.26). Altogether 47% of the WML volume was explained by age at investigation, HT volume and CRT. Patients with more WML also had higher cardiovascular risk.
Conclusions
CRT may be associated directly with increased WML volume or indirectly with reduced HT volume associated with higher cardiovascular risk. .Risk factors should be carefully monitored in these patients.
... Recently, BMD was reported to be associated with microvascular diseases, such as cerebral white matter disease (WMD): a lower BMD indicates a more severe WMD [7,8]. Additionally, WMD is a well-known risk factor for dementia in several studies [9]. Hence, we postulated that a low BMD may be associated with cognitive impairment. ...
Background and objectives: Little is known about the effect of osteoporosis on cognitive function in the acute and recovery phases of stroke. Early bone mineral density assessments during acute stroke may be a useful marker of cognitive function. We evaluated the effect of osteoporosis on cognitive function at the early and recovery phase of ischemic stroke in patients aged >50 years. Materials and Methods: We retrospectively examined consecutive patients with acute stroke hospitalized between 2016 and 2018. Osteoporosis was defined as a T-score <–2.5 for the femoral neck or lumbar spine bone mineral density. The primary outcome was cognitive impairment measured by the Korean Mini-Mental State Examination in the acute phase and recovery phase of ischemic stroke. Results: Of the 260 included subjects (107 men and 153 women), 70 (26.9%) had osteoporosis. Cognitive impairment was more severe in the osteoporosis group than in the non-osteoporosis group (30.5% versus 47.1%, p = 0.001). After the recovery phase of stroke, the proportion of patients with cognitive impairment remained higher in the osteoporosis group. The multivariate analysis revealed a correlation between a low femoral neck bone mineral density and severe cognitive impairment in the acute and recovery phases of stroke (adjusted odds ratio (OR) 4.09, 95% confidence interval (CI) 1.11–15.14 in the acute phase, and adjusted OR 11.17, 95% CI 1.12–110.98 in the recovery phase). Conclusions: Low bone mineral density is associated with poor cognitive function in the acute and recovery phases of stroke.
... These are closely associated with age and risk factors for atherosclerosis and have been associated independently with increased risk of dementia, first ever stroke, recurrent stroke, poor functional stroke outcome, and cardiovascular mortality. 4,5 The pathogenesis of WMLs is multifactorial and suggested to be caused by small artery atherosclerosis leading to chronic cerebral hypoperfusion and white matter ischaemia. 6,7 Previous studies in symptomatic CEA patients suggested an increased periand post-operative stroke and death risk in patients with more extensive WMLs. ...
Objective
Cerebral white matter lesions (WMLs) and lacunar infarcts are surrogates of cerebral small vessel disease (SVD). WML severity as determined by trained radiologists predicts post-operative stroke or death in patients undergoing carotid endarterectomy (CEA). It is unknown whether routine pre-operative brain imaging reports as part of standard clinical practice also predict short and long term risk of stroke and death after CEA.
Methods
Consecutive patients from the Athero-Express biobank study that underwent CEA for symptomatic high degree stenosis between March 2002 and November 2014 were included. Pre-operative brain imaging (computed tomography [CT] or magnetic resonance imaging [MRI]) reports were reviewed for reporting of SVD, defined as WMLs or any lacunar infarcts. The primary outcome was defined as any stroke or any cardiovascular death over three year follow up. The secondary outcome was defined as the 30 day peri-operative risk of stroke or cardiovascular death.
Results
A total of 1038 patients were included (34% women), of whom 659 (63.5%) had CT images and 379 (36.5%) MRI images available. Of all patients, 697 (67%) had SVD reported by radiologists. Patients with SVD had a higher three year risk of cardiovascular death than those without (6.5% vs. 2.1%, adjusted HR 2.52 [95% CI 1.12–5.67]; p = .026) but no association was observed for the three year risk of stroke (9.0% vs. 6.7%, for patients with SVD vs. those without, adjusted HR 1.24 [95% CI 0.76–2.02]; p = .395). No differences in 30 day peri-operative risk were observed for stroke (4.4% vs. 2.9%, for patients with vs. those without SVD; adjusted HR 1.49 [95% CI 0.73–3.05]; p = .28), and for the combined stroke/cardiovascular death risk (4.4% vs. 3.5%, adjusted HR 1.20 [95% CI 0.61–2.35]; p = .59).
Conclusion
Presence of SVD in pre-operative brain imaging reports can serve as a predictor for the three year risk of cardiovascular death in symptomatic patients undergoing CEA but does not predict peri-operative or long term risk of stroke.
