Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale

Cheng, Bastian; Chen, Ji; Königsberg, Alina; Mayer, Christine M.; Rimmele, Leander; Patil, Kaustubh R.; Gerloff, Christian; Thomalla, Götz; Eickhoff, Simon B.

doi:10.1016/j.ebiom.2022.104425

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…However, the lesion coverage in our study is comparable or even superior to many other studies on stroke outcomes and lesion anatomy (e.g. Kuceyeski et al, 2016 , Cheng et al, 2023 , Ernst et al, 2018 ) and our results should be representative of a large majority of cerebral strokes (compare to Ng et al, 2007 ).…”

Section: Discussionsupporting

confidence: 77%

“…The mRS score three months after stroke represents a general functional outcome measure, which is affected by many non-anatomical clinical and demographic factors. On the other hand, the acute NIHSS score can be traced back to a smaller set of cognitive or motor domains that, in parts, can be localised to specific lesion locations ( Cheng et al, 2023 ). Besides, the impact of time since stroke might explain the difference in prediction performance for NIHSS 24 h and mRS 3 months post-stroke.…”

Section: Discussionmentioning

confidence: 99%

“…Holism as a proxy: Cognitive brain functions are often anatomically better understood on the level of large-scale networks ( Siegel et al, 2016 , Griffis et al, 2019 ), which should also be true for general stroke impairment or outcome ( Cheng et al, 2023 ). If models with spatial lesion information struggle to represent the damage of such networks due to any reason – such as small sample sizes or the high-dimensional, multi-collinear structure of spatial imaging data – lesion size, as a holistic measure of brain damage, may serve as a possible rough proxy to explain critical impairment of functional large-scale networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stroke lesion size – Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

Sperber,

Gallucci,

Mirman

et al. 2023

NeuroImage: Clinical

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Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stroke lesion size – Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

Sperber,

Gallucci,

Mirman

et al. 2023

NeuroImage: Clinical

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“…We agree that the low dimensionality of post-stroke deficits is clinically relevant and that it provides insights into the organisation of the human brain. The findings of Corbetta and colleagues 10 stand out through the numerous follow-up studies as probably the best current…”

Section: A C C E P T E D Mmentioning

confidence: 89%

Reply: The correlation of behavioural deficits post-stroke: a trivial issue?

Sperber

Gallucci

Umarova

2023

Brain

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We thank Pini and colleagues 1 for commenting on our recent article 2 . We are grateful for the opportunity to further discuss the mechanisms underlying the associational structure of poststroke cognitive deficits. In our recent paper, we have shown that the anatomy of stroke lesions can pivotally impact the dimensionality of post-stroke cognitive deficits. Using real stroke lesion maps, we simulated cognitive profiles with single-centre localised cognitive functions. We found a low-dimensional structure of simulated cognitive deficits even though a latent cognitive structure never existed. The typical stroke lesion anatomy alone was sufficient to create such a low-dimensional structure. Therefore, we concluded that the low dimensionality of post-stroke cognitive deficits observed in a variety of previous studies likely reflects the mixed associations imposed by the cognitive architecture and the lesion anatomy. Our findings thus challenged the

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“…10 What is a subscore in a stroke severity assessment battery controlled for the total test score? 37 What is focal cortical thickness controlled for average brain-wide cortical thickness? 38 Even if such covariates – at least situationally – improve statistical analysis, we still face the issue that causal relationships become even more difficult to grasp than they already are in large-scale neurobiological data.…”

Section: Discussionmentioning

confidence: 99%

Control without cause: How covariate control biases our insights into brain architecture and pathology

Sperber,

Gallucci,

Arnold

et al. 2024

Preprint

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Inferential analysis of normal or pathological brain imaging data - as in brain mapping or the identification of neurological imaging markers - is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We found that the impact of covariate control varies and can be strong, but it does not necessarily improve the precision of results. Instead, it systematically shifts results towards the inversed associations between imaging features and the covariate. This effect of covariate control can bias results and, as shown in another experiment, can even create effects out of nothing. The widespread use of covariate control in the statistical analysis of clinical brain imaging data - and, likely, other biological high-dimensional data as well - may not generally improve statistical results, but it may just change them. Therefore, covariate control constitutes a problematic degree of freedom in the analysis of brain imaging data and may often not be justified at all.

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Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale

Cited by 8 publications

References 40 publications

Stroke lesion size – Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

Stroke lesion size – Still a useful biomarker for stroke severity and outcome in times of high-dimensional models

Reply: The correlation of behavioural deficits post-stroke: a trivial issue?

Control without cause: How covariate control biases our insights into brain architecture and pathology

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