Mapping the intersection of language and reading: the neural bases of the primary systems hypothesis

Woollams, Anna M.; Halai, Ajay D.; Ralph, Matthew A. Lambon

doi:10.1007/s00429-018-1716-z

Cited by 27 publications

(23 citation statements)

References 77 publications

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“…The fact that the same underlying dimensions were found for PPA as well as PSA indicates that these dimensions might reflect core ‘primary systems’ for language activities ( Patterson and Lambon Ralph, 1999 ; Ueno et al , 2014 ; Woollams et al , 2018 ). Past work has associated these primary systems with different brain areas: phonological processing and working memory with posterior superior temporal lobe and supra-marginal gyrus ( Paulesu et al , 1993 ); semantic representation with anterior temporal lobe (ATL) ( Patterson et al , 2007 ; Lambon Ralph et al , 2017 ); speech programming and fluency with premotor cortex and key underpinning white matter pathways ( Basilakos et al , 2014 ); and executive functions with frontoparietal networks ( Jurado and Rosselli, 2007 ; Marek and Dosenbach, 2018 ).…”

Section: Discussionmentioning

confidence: 64%

Graded, multidimensional intra- and intergroup variations in primary progressive aphasia and post-stroke aphasia

Ingram

Halai

Pobric

et al. 2020

Brain

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Language impairments caused by stroke (post-stroke aphasia, PSA) and neurodegeneration (primary progressive aphasia, PPA) have overlapping symptomatology, nomenclature and are classically divided into categorical subtypes. Surprisingly, PPA and PSA have rarely been directly compared in detail. Rather, previous studies have compared certain subtypes (e.g. semantic variants) or have focused on a specific cognitive/linguistic task (e.g. reading). This study assessed a large range of linguistic and cognitive tasks across the full spectra of PSA and PPA. We applied varimax-rotated principal component analysis to explore the underlying structure of the variance in the assessment scores. Similar phonological, semantic and fluency-related components were found for PSA and PPA. A combined principal component analysis across the two aetiologies revealed graded intra- and intergroup variations on all four extracted components. Classification analysis was used to test, formally, whether there were any categorical boundaries for any subtypes of PPA or PSA. Semantic dementia formed a true diagnostic category (i.e. within group homogeneity and distinct between-group differences), whereas there was considerable overlap and graded variations within and between other subtypes of PPA and PSA. These results suggest that (i) a multidimensional rather than categorical classification system may be a better conceptualization of aphasia from both causes; and (ii) despite the very different types of pathology, these broad classes of aphasia have considerable features in common.

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Section: Discussionmentioning

confidence: 64%

Graded, multidimensional intra- and intergroup variations in primary progressive aphasia and post-stroke aphasia

Ingram

Halai

Pobric

et al. 2020

Brain

View full text Add to dashboard Cite

show abstract

“…The fact that the same underlying dimensions were found for PPA as well as PSA indicates that these dimensions might reflect core "primary systems" for language activities (Patterson and Lambon Ralph, 1999;Ueno et al, 2014;Woollams et al, 2018). Past work has associated these primary systems with different brain areas: phonological processing and working memory with posterior superior temporal lobe and supra-marginal gyrus (Paulesu et al, 1993); semantic representation with anterior temporal lobe (ATL) (Lambon Patterson et al, 2007); speech programming and fluency with premotor cortex and key underpinning white matter pathways (Basilakos et al, 2014); and executive functions with frontoparietal networks (Jurado and Rosselli, 2007;Marek and Dosenbach, 2018).…”

Section: Discussionmentioning

confidence: 70%

Graded, multi-dimensional intragroup and intergroup variations in primary progressive aphasia and post-stroke aphasia

Ingram

Halai

Pobric

et al. 2019

Preprint

Self Cite

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Language impairments caused by stroke (post-stroke aphasia) and neurodegeneration (primary progressive aphasia) have overlapping symptomatology, nomenclature and are classically divided into categorical subtypes. Surprisingly, primary progressive aphasia (PPA) and post-stroke aphasia (PSA) have rarely been directly compared in detail. Rather previous studies have compared certain subtypes (e.g., semantic variants) or have focussed on a specific cognitive/linguistic task (e.g., reading). This study assessed a large range of linguistic and cognitive tasks across the full spectra of PSA and PPA. We applied varimax-rotated principal component analysis to explore the underlying structure of the variance in the assessment scores. Similar phonological, semantic and fluencyrelated components were found for PSA and PPA. A combined principal component analysis across the two aetiologies revealed graded intragroup and intergroup variations on all four extracted components. Classification analysis was employed to test, formally, whether there were any categorical boundaries for any subtypes of PPA or PSA. Semantic dementia proved to form a true diagnostic category (i.e., within group homogeneity and distinct between group differences), whereas there was considerable overlap and graded variations within and between other subtypes of PPA and PSA. These results suggest that (a) a multi-dimensional rather than categorical classification system may be a better conceptualisation of aphasia from both causes, and (b) despite the very different types of pathology, these broad classes of aphasia have considerable features in common.

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“…Finally, in terms of research and clinical applications, a direct test of the usefulness of the automatic stroke lesion segmentation model would involve applying it in studies of lesion-deficit relationships. As noted in the Introduction, hand-segmentation of brain lesions by human experts is the current gold standard, although some studies have begun relying entirely on computer-generated lesion segmentations (Tyler, Marslen-Wilson, Stamatakis, 2005, Woollams, Halai, Ralph, 2018). Human inter-rater reliability presumably also represents the upper limit for inter-rater reliability between model-based and human expert-generated lesion segmentations.…”

Section: Discussionmentioning

confidence: 99%

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

Xue

Farhat

Boukrina

et al. 2020

NeuroImage: Clinical

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Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by eliminating the laborious step of having a human expert manually segment the lesion on each brain scan. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We trained and tested our method on datasets from three sources: Medical College of Wisconsin (MCW), Kessler Foundation (KF), and the publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset. To promote wide applicability, lesions were included from both subacute (< 5 weeks) and chronic (> 3 months) phases post stroke, and were of both hemorrhagic and ischemic etiology. Cross-study validation results (with independent training and validation datasets) were obtained to compare with previous methods based on naive Bayes, random forests, and three recently published convolutional neural networks. Model performance was quantified in terms of the Dice coefficient, a measure of spatial overlap between the modelidentified lesion and the human expert-identified lesion, where 0 is no overlap and 1 is complete overlap. Training on the KF and MCW images and testing on the ATLAS images yielded a mean Dice coefficient of 0.54. This was reliably better than the next best previous model, UNet, at 0.47. Reversing the train and test datasets yields a mean Dice of 0.47 on KF and MCW images, whereas the next best UNet reaches 0.45. With all three datasets combined, the current system compared to previous methods also attained a reliably higher cross-validation accuracy. It also achieved high Dice values for many smaller lesions that existing methods have difficulty identifying. Overall, our system is a clear improvement over previous methods for automating stroke lesion segmentation, bringing us an important step closer to the inter-rater accuracy level of human experts.

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Mapping the intersection of language and reading: the neural bases of the primary systems hypothesis

Cited by 27 publications

References 77 publications

Graded, multidimensional intra- and intergroup variations in primary progressive aphasia and post-stroke aphasia

Graded, multidimensional intra- and intergroup variations in primary progressive aphasia and post-stroke aphasia

Graded, multi-dimensional intragroup and intergroup variations in primary progressive aphasia and post-stroke aphasia

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

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