Poststroke cognitive outcome is better accounted for by white matter abnormalities automated segmentation than visual analysis - 14/07/24

Doi : 10.1016/j.neurol.2024.06.004

B. Lawson ^a,^b, J. Martin ^a,^b, A. Aarabi ^a, E. Ouin ^b, S. Tasseel-Ponche ^a,^c, M. Barbay ^a,^b, D. Andriuta ^a,^b, M. Roussel ^a,^b, O. Godefroy ^a,^b,^⁎
the
GRECogVASC study group^¹
The list of the GRECogVASC study group can be found at the end of the article, in the Appendix A section.
The GRECogVASC study group (investigators of the present paper)
O. Godefroy ^d, M. Roussel ^d, M. Barbay ^d, S. Canaple ^d, C. Lamy ^d, C. Leclercq ^d, A. Arnoux ^d, S. Despretz-Wannepain ^e, P. Despretz ^f, H. Berrissoul ^g, C. Picard ^h, M. Diouf ^h, G. Loas ⁱ, H. Deramond ^j, H. Taillia ^k, A.-E. Ardisson ^k, C. Nédélec-Ciceri ^l, C. Bonnin ^l, C. Thomas-Anterion ^m, F. Vincent-Grangette ⁿ, J. Varvat ^m, V. Quaglino ^o, H. Beaunieux ^p, C. Moroni ^q, A. Martens-Chazelles ^r, S. Batier-Monperrus ^s, C. Monteleone ^t, V. Costantino ^t, E. Theunssens ^u
^d Department of Neurology and Laboratory of Functional Neurosciences, University Hospital of Amiens, Amiens, France
^e Department of Neurology, University Hospital of Amiens, Amiens, France
^f University of Lille, Inserm CIC-IT 807, Lille, France
^g University Hospital of Amiens, Amiens, France
^h Department of Biostatistics and Medical Research, University Hospital of Amiens, Amiens, France
ⁱ Department of Psychiatry and Laboratory of Functional Neurosciences, University Hospital of Amiens, Amiens, France
^j Department of Radiology, University Hospital of Amiens, Amiens, France
^k Department of Neurology, Val-de-Grâce Hospital, Paris, France
^l Department of Neurology, Hospital of La Rochelle, La Rochelle, France
^m Department of Neurology, University Hospital of Saint-Étienne, Saint-Étienne, France
ⁿ Department of Neurology and CMRR, University Hospital of Saint-Étienne, Saint-Étienne, France
^o Department of Psychology and Laboratory CRPPCO, University of Amiens, Amiens, France
^p Department of Psychology, University of Caen, Caen, France
^q Department of Psychology, University of Lille, Lille, France
^r Department of Neurology, Hospital of Creil, Creil, France
^s Department of Neurology, Hospital of Epinal, Epinal, France
^t Department of Neurology, Hospital of Ajaccio, Ajaccio, France
^u Institut de logopédie, Haute École de Liège, Liège, Belgium

^a Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne University of Picardie, Amiens, France
^b Departments of Neurology, Amiens University Hospital, Amiens, France
^c Departments of Physical Medicine and Rehabilitation, Amiens University Hospital, Amiens, France

^⁎Corresponding author. Department of Neurology, Amiens University Hospital, Amiens, France.Department of Neurology, Amiens University HospitalAmiensFrance

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Sunday 14 July 2024

Abstract

Background and aims

The association between white matter abnormalities (WMA) and cognitive decline previously reported in poststroke patients has been mainly documented using visual scales. However, automated segmentation of WMA provides a precise determination of the volume of WMA. Nonetheless, it is rarely used in the stroke population and its potential advantage over visual scales is still unsettled. The objective of this study was to examine whether automated segmentation of WMA provides a better account than the visual Fazekas and Wahlund scales of the decline in executive functions and processing speed in stroke patients.

Methods

The analyses were conducted on the 358 patients of the GRECogVASC cohort with an MRI performed at six months poststroke in the Amiens center. WMA were visually analyzed using the Fazekas (subcortical abnormalities) and Wahlund scales. Segmentation was performed using LST (3.0.3). Following preliminary studies to determine the optimal segmentation threshold, we examined the relationship between cognitive status and WMA volume computed at each threshold using receiver operating characteristic (ROC) curves. Finally, we assessed the ability of both Fazekas and Wahlund visual scores and WMA volume to account for cognitive scores by using a bivariate Pearson correlation analysis, comparing correlation coefficients with the Fisher transformation and repeating correlation analysis after adjustment for the lesion volume.

Results

Increasing the threshold led to an underestimation of WMA (P=0.0001) (significant for a threshold ≥0.2) and an improvement in correct rejection of signal changes in the stroke cavity (P=0.02) (significant for a threshold ≤0.5), susceptibility artifacts (P=0.002) (significant for a threshold ≤0.6), and corticospinal degeneration (P=0.03) (significant for a threshold ≤0.5). WMA volume decreased with increasing threshold (P=0.0001). Areas under the curve (AUC) did not differ according to the threshold (processing speed: P=0.85, executive cognitive functions: P=0.7). Correlation coefficients between cognitive scores and WMA were higher for WMA volume than the Fazekas (processing speed: Z=−3.442, P=0.001; executive functions: Z=−2.751, P=0.006) and Wahlund scores (processing speed: Z=−3.615, P=0.0001; executive functions: Z=−2.769, P=0.006). Adjustment for lesion volume did not alter the correlations with WMA volume (processing speed: r=−0.327 [95%CI: −0.416; −0.223], P=0.0001; executive functions: r=−0.262 [95%CI: −0.363; −0.150], P=0.0001).

Conclusion

This study shows that WMA volume assessed by automated segmentation provides a better account of cognitive disorders than visual analysis. This should favor its wider use to refine imaging determinants of poststroke cognitive disorders.

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Keywords : WMH, MRI, Control functions, Executive functions, Dementia, Mild cognitive impairment, Infarct, Hemorrhage, Stroke