The impact of white matter hyperintensities on speech perception

Stadio, Arianna Di; Messineo, Daniela; Ralli, Massimo; Roccamatisi, Dalila; Musacchio, Angela; Ricci, Giampietro; Greco, Antonio

doi:10.1007/s10072-020-04295-8

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…Central SNHL is caused by the alteration of the superior auditory pathways (cochlear nuclei, auditory cortex). These structures can be affected by vascular damage [31,32] that alters the signal transmission. Brain atrophy, which could be a consequence of vascular damage or aging is another potential cause of central SNHL [24].…”

Section: Definition Of Hearing Loss and Cognitive Deficit In Elderly Populationmentioning

confidence: 99%

“…Both ARHL and cognitive impairment are multifactorial and heterogeneous, with several common risk factors, mainly vascular, such as atherosclerosis, smoking and diabetes, that increase the risk of cardiovascular disease and stroke [31,37,[55][56][57][58][59][60][61], as observed in a population-based survey conducted during 1997 to 1999 and 2002 to 2004 [62]. At the same time, the brain is susceptible to oxidative stress, which also plays an important microcirculatory role in auditory processing [54].…”

Section: Common Pathological Conditionsmentioning

confidence: 99%

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Hearing Loss and Cognitive Impairment: Epidemiology, Common Pathophysiological Findings, and Treatment Considerations

Bisogno

Scarpa

Girolamo

et al. 2021

Life

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In recent years, there has been increasing research interest in the correlation between hearing impairment and cognitive decline, two conditions that have demonstrated a strong association. Hearing loss appears as a risk factor for cognitive impairment, especially among certain populations, notably nursing home residents. Furthermore, hearing loss has been identified as a modifiable age-related condition linked to dementia, and it has been estimated that midlife hearing loss, if eliminated, might decrease the risk of dementia in the general population. Several mechanisms have been suggested to explain the pathologic connections between hearing loss and dementia; however, clear evidence is missing, and the common pathophysiological basis is still unclear. In this review, we discussed current knowledge about the relationship between hearing loss and dementia, and future perspectives in terms of the effects of hearing rehabilitation for early prevention of cognitive decline.

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Section: Definition Of Hearing Loss and Cognitive Deficit In Elderly Populationmentioning

confidence: 99%

Section: Common Pathological Conditionsmentioning

confidence: 99%

Hearing Loss and Cognitive Impairment: Epidemiology, Common Pathophysiological Findings, and Treatment Considerations

Bisogno

Scarpa

Girolamo

et al. 2021

Life

Self Cite

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“…They are a common finding in MR images of older adults (Wardlaw, Valdés Hernández, & Muñoz-Maniega, 2015) and considered as a marker of cerebral small vessel disease (CSVD), which, in turn, is a major cause of morbidity and mortality in older age and triggers cognitive decline (Baker et al, 2012). Even in healthy older adults WMH are associated with reduced cognitive, perceptual and motor abilities (Di Stadio et al, 2020;Gunning-Dixon & Raz, 2000;Pinter et al, 2017). More generally, the presence of WMH increases the risk of global functional loss (Inzitari et al, 2009), stroke, dementia, and death (Debette & Markus, 2010).…”

Section: Introductionmentioning

confidence: 99%

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

Hotz

Deschwanden

Liem

et al. 2021

Human Brain Mapping

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White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r s = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r s = 0.41), and many outlier WMH volumes were detected when exploring withinperson trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r s = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

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“…They are a common finding in MR images of older adults (Wardlaw, Valdés Hernández, & Muñoz-Maniega, 2015) and considered as a marker of cerebrovascular disease (CVD), which, in turn, is a major cause of morbidity and mortality in older age and triggers cognitive decline (Baker et al, 2012). Even in healthy older adults WMH are associated with reduced cognitive, perceptual and motor abilities (Di Stadio et al, 2020;Gunning-Dixon & Raz, 2000;Pinter et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector and BIANCA

Hotz

Deschwanden

Liem

et al. 2020

Preprint

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White matter hyperintensities of presumed vascular origin (WMH) are frequently found in MRIs of patients with various neurological and vascular disorders, but also in healthy elderly subjects. Although automated methods have been developed to replace the challenging task of manually segmenting the WMH, there is still no consensus on which validated algorithm(s) should be used. In this study, we validated and compared three freely available methods for WMH extraction: FreeSurfer, UBO Detector, and the Brain Intensity AbNormality Classification Algorithm, BIANCA (with the two thresholding options: global thresholding vs. LOCally Adaptive Threshold Estimation (LOCATE)) using a standardized protocol. We applied the algorithms to longitudinal MRI data (2D FLAIR, 3D FLAIR, T1w sMRI) of cognitively healthy older people (baseline N = 231, age range 64 - 87 years) with a relatively low WMH load. As a reference for the segmentation accuracy of the algorithms, completely manually segmented gold standards were used separately for each MR image modality. To validate the algorithms, we correlated the automatically extracted WMH volumes with the Fazekas scores, chronological age, and between the time points. In addition, we analyzed conspicuous percentage WMH volume increases and decreases in the longitudinal data between two measurement points to verify the segmentation reliability of the algorithms. All algorithms showed a moderate correlation with chronological age except BIANCA with the 2D FLAIR image input only showed a weak correlation. FreeSurfer fundamentally underestimated the WMH volume in comparison with the gold standard as well as with the other algorithms, and cannot be considered as an accurate substitute for manual segmentation, as it also scored the lowest value in the DSC compared to the other algorithms. However, its WMH volumes correlated strongly with the Fazekas scores and showed no conspicuous WMH volume increases and decreases between measurement points in the longitudinal data. BIANCA performed well with respect to the accuracy metrics - especially the DSC, H95, and DER. However, the correlations of the WMH volumes with the Fazekas scores compared to the other algorithms were weaker. Further, we identified a significant amount of outlier WMH volumes in the within-person change trajectories with BIANCA. The WMH volumes extracted by UBO Detector achieved the best result in terms of cost-benefit ratio in our study. Although there is room for optimization with respect to segmentation accuracy (especially for the metrics DSC, H95 and DER), it achieved the highest correlations with the Fazekas scores and the highest ICCs. Its performance was high for both input modalities, although it relies on a built-in single-modality training dataset, and it showed reliable WMH volume estimations across measurement points.

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The impact of white matter hyperintensities on speech perception

Cited by 11 publications

References 37 publications

Hearing Loss and Cognitive Impairment: Epidemiology, Common Pathophysiological Findings, and Treatment Considerations

Hearing Loss and Cognitive Impairment: Epidemiology, Common Pathophysiological Findings, and Treatment Considerations

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector and BIANCA

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