Abstract:Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1‐weighted by T2‐weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1‐w/T2‐w ratio value). We selected structural T1‐w and T2‐… Show more
“…This is a finding consistent with converging evidence that AD involves a remyelination process in the gray matter as a homeostatic response to myelin breakdown [23]. There have been in vivo and ex vivo findings of increased intracortical myelin content [24–26] and disorganized myeloarchitecture [24, 27] in AD patients compared to normal controls. The studies have further noted a visibly inhomogeneous band of hypointense T2*‐weighted MRI signals across the cortex, which blurred the cortical laminae and was highly associated with myelin content.…”
Background and purpose
Texture analysis of magnetic resonance imaging (MRI) brain scans have been proposed as a promising tool in the early diagnosis of Alzheimer’s disease (AD), but its biological correlates remain unknown. In this study, we examined the relationship between MRI texture features and AD pathology.
Methods
The study included 150 participants who had a 3.0T T1‐weighted image, amyloid‐β positron emission tomography (PET), and tau PET within 3 months of each other. In each of six brain regions (hippocampus, precuneus, and entorhinal, middle temporal, posterior cingulate and superior frontal cortices), linear regression analyses adjusting for age and sex was performed to examine the effects of regional amyloid‐β and tau burden on regional texture features. We also compared neuroimaging measures based on pathological severity using ANOVA.
Results
In all regions, tau burden (p < 0.05), but not amyloid‐β burden, were associated with a certain texture feature that varied with the region’s cytoarchitecture. Specifically, autocorrelation and cluster shade were associated with tau burden in allocortical and periallocortical regions, whereas entropy and contrast were associated with tau burden in neocortical regions. Mean signal intensity of each region did not show any associations with AD pathology. The values of the region‐specific textures also varied across groups of varying pathological severity.
Conclusions
Our results suggest that textures of T1‐weighted MRI reflect changes in the brain that are associated with regional tau burden and the local cytoarchitecture. This study provides insight into how MRI texture can be used for detection of microstructural changes in AD.
“…This is a finding consistent with converging evidence that AD involves a remyelination process in the gray matter as a homeostatic response to myelin breakdown [23]. There have been in vivo and ex vivo findings of increased intracortical myelin content [24–26] and disorganized myeloarchitecture [24, 27] in AD patients compared to normal controls. The studies have further noted a visibly inhomogeneous band of hypointense T2*‐weighted MRI signals across the cortex, which blurred the cortical laminae and was highly associated with myelin content.…”
Background and purpose
Texture analysis of magnetic resonance imaging (MRI) brain scans have been proposed as a promising tool in the early diagnosis of Alzheimer’s disease (AD), but its biological correlates remain unknown. In this study, we examined the relationship between MRI texture features and AD pathology.
Methods
The study included 150 participants who had a 3.0T T1‐weighted image, amyloid‐β positron emission tomography (PET), and tau PET within 3 months of each other. In each of six brain regions (hippocampus, precuneus, and entorhinal, middle temporal, posterior cingulate and superior frontal cortices), linear regression analyses adjusting for age and sex was performed to examine the effects of regional amyloid‐β and tau burden on regional texture features. We also compared neuroimaging measures based on pathological severity using ANOVA.
Results
In all regions, tau burden (p < 0.05), but not amyloid‐β burden, were associated with a certain texture feature that varied with the region’s cytoarchitecture. Specifically, autocorrelation and cluster shade were associated with tau burden in allocortical and periallocortical regions, whereas entropy and contrast were associated with tau burden in neocortical regions. Mean signal intensity of each region did not show any associations with AD pathology. The values of the region‐specific textures also varied across groups of varying pathological severity.
Conclusions
Our results suggest that textures of T1‐weighted MRI reflect changes in the brain that are associated with regional tau burden and the local cytoarchitecture. This study provides insight into how MRI texture can be used for detection of microstructural changes in AD.
“…At the mean age of our homozygote group (55 years), approximately 50% of these individuals display abnormal levels of amyloid biomarkers, as compared with only 10% of non-carriers and about 20% of carriers of a single ε4 allele (Jansen et al, 2015). Besides, T1w/T2w ratio might show a positive association with Aβ accumulation (Bartzokis et al, 2007), according to recent results (Yasuno et al, 2017; Pelkmans et al, 2019). Still, such impact, as it was suggested, would come in a direction contrary to the one we find for the APOE-ε4 status, therefore pinpointing the strength of the effect in our dataset regardless of the possible underlying amyloid charge.…”
Section: Discussionmentioning
confidence: 57%
“…It was initially proposed for cortical mapping of myelin, then was extended to the whole brain (Ganzetti et al, 2014) and met a variety of applications with special emphasis in developmental studies (Lee et al, 2015; Soun et al, 2017; Lebel and Deoni, 2018), neurodegenerative diseases e.g. MS (Righart et al, 2017), schizophrenia (Iwatani et al, 2015), Alzheimer's disease (Yasuno et al, 2017; Pelkmans et al, 2019) and the healthy brain (Shafee et al, 2015).…”
The apolipoprotein E gene (APOE) ε4 allele has a strong and manifold impact on cognition and neuroimaging phenotypes in cognitively normal subjects, including alterations in the white matter (WM) microstructure. Such alterations have often been regarded as a reflection of potential thinning of the myelin sheath along axons, rather than pure axonal degeneration. Considering the main role of APOE in brain lipid transport, characterizing the impact of APOE on the myelin coating is therefore of crucial interest, especially in healthy APOE-ε4 homozygous individuals, who are exposed to a twelve-fold higher risk of developing Alzheimer's disease (AD), compared to the rest of the population.We examined T1w/T2w ratio maps in 515 cognitively healthy middle-aged participants from the ALFA study (ALzheimer and FAmilies) cohort, a single-site population-based study enriched for AD risk (68 APOE-ε4 homozygotes, 197 heterozygotes, and 250 non-carriers). Using tract-based spatial statistics, we assessed the impact of age and APOE genotype on this ratio taken as an indirect descriptor of myelin content.Healthy APOE-ε4 carriers display decreased T1w/T2w ratios in extensive regions in a dose-dependent manner. These differences were found to interact with age, suggesting faster changes in individuals with more ε4 alleles.These results obtained with T1w/T2w ratios, confirm the increased vulnerability of WM tracts in APOE-ε4 healthy carriers. Early alterations of myelin content could be the result of the impaired function of the ε4 isoform of the APOE protein in cholesterol transport. These findings help to clarify the possible interactions between the APOE-dependent non-pathological burden and age-related changes potentially at the source of the AD pathological cascade.
“…It is known that the ratio between the signal intensity of the T1- and T2-weighted images is known to reflect cortical myelin content 32 . A recent study has shown that cortical T1/T2 signal intensity ratio significantly differed between AD patients and normal controls 33 . Although we did not include the T1-weighted and T2 FLAIR signal intensity ratio, including features from both sequences might have increased the ability of the prediction model to reflect the microstructural alterations more accurately.…”
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
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