% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Petersen:1024693,
author = {Petersen, Marvin and Coenen, Mirthe and DeCarli, Charles
and De Luca, Alberto and van der Lelij, Ewoud and Barkhof,
Frederik and Benke, Thomas and Chen, Christopher P. L. H.
and Dal-Bianco, Peter and Dewenter, Anna and Duering, Marco
and Enzinger, Christian and Ewers, Michael and Exalto, Lieza
G. and Fletcher, Evan F. and Franzmeier, Nicolai and Hilal,
Saima and Hofer, Edith and Koek, Huiberdina L. and Maier,
Andrea B. and Maillard, Pauline M. and McCreary, Cheryl R.
and Papma, Janne M. and Pijnenburg, Yolande A. L. and
Rubinski, Anna and Schmidt, Reinhold and Smith, Eric E. and
Steketee, Rebecca M. E. and van den Berg, Esther and van der
Flier, Wiesje M. and Venkatraghavan, Vikram and Vernooij,
Meike W. and Venketasubramanian, Narayanaswamy and Wolters,
Frank J. and Xin, Xu and Horn, Andreas and Patil, Kaustubh
R. and Eickhoff, Simon B. and Thomalla, Götz and Biesbroek,
J. Matthijs and Biessels, Geert Jan and Cheng, Bastian},
title = {{E}nhancing {C}ognitive {P}erformance {P}rediction through
{W}hite {M}atter {H}yperintensity {D}isconnectivity
{A}ssessment: {A} {M}ulticenter {L}esion {N}etwork {M}apping
{A}nalysis of 3,485 {M}emory {C}linic {P}atients},
reportid = {FZJ-2024-02366},
year = {2024},
abstract = {Introduction: White matter hyperintensities of presumed
vascular origin (WMH) are associated with cognitive
impairment and are a key imaging marker in evaluating
cognitive health. However, WMH volume alone does not fully
account for the extent of cognitive deficits and the
mechanisms linking WMH to these deficits remain unclear. We
propose that lesion network mapping (LNM), enabling the
inference of brain networks disconnected by lesions,
represents a promising technique for enhancing our
understanding of the role of WMH in cognitive disorders. Our
study employed this approach to test the following
hypotheses: (1) LNM-informed markers surpass WMH volumes in
predicting cognitive performance, and (2) WMH contributing
to cognitive impairment map to specific brain
networks.Methods $\&$ results: We analyzed cross-sectional
data of 3,485 patients from 10 memory clinic cohorts within
the Meta VCI Map Consortium, using harmonized test results
in 4 cognitive domains and WMH segmentations. WMH
segmentations were registered to a standard space and mapped
onto existing normative structural and functional brain
connectome data. We employed LNM to quantify WMH
connectivity across 480 atlas-based gray and white matter
regions of interest (ROI), resulting in ROI-level structural
and functional LNM scores. The capacity of total and
regional WMH volumes and LNM scores in predicting cognitive
function was compared using ridge regression models in a
nested cross-validation. LNM scores predicted performance in
three cognitive domains (attention and executive function,
information processing speed, and verbal memory)
significantly better than WMH volumes. LNM scores did not
improve prediction for language functions. ROI-level
analysis revealed that higher LNM scores, representing
greater disruptive effects of WMH on regional connectivity,
in gray and white matter regions of the dorsal and ventral
attention networks were associated with lower cognitive
performance.Conclusion: WMH-related brain network
disconnectivity significantly improves the prediction of
current cognitive performance in memory clinic patients
compared to WMH volume as a traditional imaging marker of
cerebrovascular disease. This highlights the crucial role of
network effects, particularly in attention-related brain
regions, improving our understanding of vascular
contributions to cognitive impairment. Moving forward,
refining WMH information with connectivity data could
contribute to patient-tailored therapeutic interventions and
facilitate the identification of subgroups at risk of
cognitive disorders.},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5253 -
Neuroimaging (POF4-525)},
pid = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5253},
typ = {PUB:(DE-HGF)25},
doi = {10.1101/2024.03.28.24305007},
url = {https://juser.fz-juelich.de/record/1024693},
}