The neural and neurocomputational bases of recovery from post-stroke aphasia

Stefaniak, James D.; Halai, Ajay D.; Ralph, Matthew A. Lambon

doi:10.1038/s41582-019-0282-1

Cited by 132 publications

(126 citation statements)

References 158 publications

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“…Note these same demand-related areas were also found in the easy condition if the statistical threshold was reduced. This fits with the notion that there is an intrinsic broader network that can support function but, to save energy, its level of activation is titrated against performance demand (which we refer to as 'variable neuro-displacement' (29)). This mechanism is analogous to variable displacement in combustion engines where cylinder function is down-regulated or switched off to save energy but are re-engaged when increased performance is needed (30).…”

Section: Discussionsupporting

confidence: 70%

The neural bases of resilient cognitive systems: Evidence of variable neuro-displacement in the semantic system

Jung

Rice

Ralph

2019

Preprint

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The purpose of this study was to initiate exploration of an equally-important research goal: what are the neurocomputational mechanisms that make these cognitive systems "well engineered" and thus resilient across a range of performance demands and to mild levels of perturbation or even damage? We achieved this aim by investigating the neural dynamics of the semantic network with two task difficulty manipulations. We found that intrinsic resilience-related mechanisms were observed in both the domain-specific semantic representational system and the parallel executive control networks. Functional connectivity between these regions was also increased and these increases were related to better semantic task performance. Our results suggest that higher cognitive functions are made resilient by flexible, dynamic changes (variable neuro-displacement) across both domain-specific and multi-demand networks. Our findings provide strong evidence that the compensatory functional alterations in the impaired brain might reflect intrinsic mechanisms of a well-engineered neural system.

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Section: Discussionsupporting

confidence: 70%

The neural bases of resilient cognitive systems: Evidence of variable neuro-displacement in the semantic system

Jung

Rice

Ralph

2019

Preprint

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“…We specifically chose larger ROIs (similar to Mirman et al, 2018), because small ROIs are unlikely to be accurately identified in patients who generally have much larger lesions than focal fMRI activation areas. Further, even assuming that fMRI properly delineates the size and location of specific functional areas, in the chronic stage of stroke recovery, these functional areas are likely to be altered by neural reorganization (Kiran, Meier, & Johnson, 2019;Stefaniak, Halai, & Ralph, 2019).…”

Section: Simulations With Synthetic Behavioral Datamentioning

confidence: 99%

An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping

Ivanova

Herron

Dronkers

et al. 2020

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Acknowledgements:We are grateful to Brian Curran and Dr. And Turken for initial assistance with collection of neuroimaging data used in the current project. We value Dr. Robert Knight's contribution to the anatomical accuracy of lesion reconstructions over the years. We thank Dr.Stephen Wilson for developing the original VLSM software. We also extend our thanks to the developers of LESYMAP package (Dr. Dorian Pustina and others) for making their lesion database publicly available. As always, we are in debt to all the individuals with aphasia who have participated in multiple studies over the years and make this kind of work possible.

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“…This observation is in line with other studies, illustrating a pivotal role of left and right frontal, precentral, central and parietal areas in general (Table 2). (Stefaniak et al, 2019) The connectomic configuration of the language network is confirmed by the left and right distribution of category I & II errors, showing a left hemispheric frontal and perisylvian distribution as well as presenting right frontal and precentral error susceptibility to rTMS. A recent study focused on the results of MEG in patients with tumors in the language area of the dominant hemisphere where semantic properties shifted to the right hemisphere.…”

Section: Discussionmentioning

confidence: 90%

Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

Wang

Fekonja

Dreyer

et al. 2020

Preprint

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Repetitive TMS (rTMS) allows to non-invasively and transiently disrupt local neuronal functioning. Its potential for mapping of language function is currently explored. Given the inter- individual heterogeneity of tumor impact on the language network and resulting rTMS derived functional mapping, we propose to use machine learning strategies to classify potential patterns of functional reorganization. We retrospectively included 90 patients with left perisylvian glioma tumors, world health organization (WHO) grade II-IV, affecting the language network. All patients underwent navigated rTMS language mappings. The severity of aphasia was assessed preoperatively using the Berlin Aphasia Score (BAS), which is adapted to the Aachener Aphasia Test (AAT). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each cortical area by automated anatomical labeling parcellation (AAL) and used support vector machine (SVM) as a classifier for significant areas in relation to aphasia. 29 of 90 (32.2%) patients suffered from aphasia. Univariate analysis revealed 11 perisylvian AVOIs' ERs (eight left, three right hemispheric) that were significantly higher in the aphasic than non-aphasic group (p < 0.05), depicting a broad, bihemispheric language network. After feeding the significant AVOIs into the SVM model, it showed that additional to age (w = 2.95), the ERs of right Frontal_Inf_Tri (w = 2.06) and left SupraMarginal (w = 2.05) and Parietal_Inf (w= 1.80) contributed more than other features to the model. The model's sensitivity was 89.7%, the specificity was 82.0%, the overall accuracy was 81.1% and AUC was 88.7%. Our results demonstrate an increased vulnerability of the right inferior frontal gyrus to rTMS in patients suffering from aphasia due to left perisylvian gliomas. This confirms a functional relevant involvement of the right frontal area in relation to aphasia. While age as a feature improved our SVM model the most, the tumor location feature didn't affect the SVM model. This finding indicates that general tumor induced network disconnection is relevant to aphasia and not necessarily related to specific lesion locations. Additionally, our results emphasize the decreasing potential for neuroplasticity with age.

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The neural and neurocomputational bases of recovery from post-stroke aphasia

Cited by 132 publications

References 158 publications

The neural bases of resilient cognitive systems: Evidence of variable neuro-displacement in the semantic system

The neural bases of resilient cognitive systems: Evidence of variable neuro-displacement in the semantic system

An empirical comparison of univariate versus multivariate methods for the analysis of brain-behavior mapping

Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

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