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Structural parameters are superior to eigenvector centrality in detecting progressive supranuclear palsy with machine learning & multimodal MRI

. 2024 Aug 15 ; 10 (15) : e34910. [epub] 20240725

Status PubMed-not-MEDLINE Language English Country England, Great Britain Media electronic-ecollection

Document type Journal Article

Links

PubMed 39170550
PubMed Central PMC11336336
DOI 10.1016/j.heliyon.2024.e34910
PII: S2405-8440(24)10941-3
Knihovny.cz E-resources

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized initially by falls and eye movement impairment. This multimodal imaging study aimed at eliciting structural and functional disease-specific brain alterations. T1-weighted and resting-state functional MRI were applied in multi-centric cohorts of PSP and matched healthy controls. Midbrain, cerebellum, and cerebellar peduncles showed severely low gray/white matter volume, whereas thinner cortical gray matter was observed in cingulate cortex, medial and temporal gyri, and insula. Eigenvector centrality analyses revealed regionally specific alterations. Multivariate pattern recognition classified patients correctly based on gray and white matter segmentations with up to 98 % accuracy. Highest accuracies were obtained when restricting feature selection to the midbrain. Eigenvector centrality indices yielded an accuracy around 70 % in this comparison; however, this result did not reach significance. In sum, the study reveals multimodal, widespread brain changes in addition to the well-known midbrain atrophy in PSP. Alterations in brain structure seem to be superior to eigenvector centrality parameters, in particular for prediction with machine learning approaches.

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Höglinger G.U., et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Movement Disorders. 2017;32:853–864. PubMed PMC

Albrecht F., Bisenius S., Neumann J., Whitwell J., Schroeter M.L. Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy - a double-validation whole-brain meta-analysis. Neuroimage Clin. 2019;22 doi: 10.1016/j.nicl.2019.101722. PubMed DOI PMC

Gardner R.C., et al. Intrinsic connectivity network disruption in progressive supranuclear palsy. Ann. Neurol. 2013;73:603–616. PubMed PMC

Whitwell J.L., et al. Disrupted thalamocortical connectivity in PSP: a resting-state fMRI, DTI, and VBM study. Parkinsonism & related disorders. 2011;17:599–605. PubMed PMC

Bharti K., et al. Abnormal resting-state functional connectivity in progressive supranuclear palsy and corticobasal syndrome. Front. Neurol. 2017;8:248. PubMed PMC

Rosskopf J., et al. Intrinsic functional connectivity alterations in progressive supranuclear palsy: differential effects in frontal cortex, motor, and midbrain networks. Movement Disorders. 2017;32:1006–1015. doi: 10.1002/mds.27039. PubMed DOI

Bian J., Wang X., Hao W., Zhang G., Wang Y. The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis. Front. Aging Neurosci. 2023;15 doi: 10.3389/fnagi.2023.1199826. PubMed DOI PMC

Mueller K., Lepsien J., Möller H.E., Lohmann G. Commentary: cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Front. Hum. Neurosci. 2017;11 doi: 10.3389/fnhum.2017.00345. PubMed DOI PMC

Flandin G., Friston K.J. Analysis of family-wise error rates in statistical parametric mapping using random field theory. Hum. Brain Mapp. 2019;40:2052–2054. doi: 10.1002/hbm.23839. PubMed DOI PMC

Gaser C., Dahnke R. CAT-a computational anatomy toolbox for the analysis of structural MRI data. HBM. 2016;2016:336–348. PubMed PMC

Power J.D., et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320–341. doi: 10.1016/j.neuroimage.2013.08.048. PubMed DOI PMC

Lohmann G., et al. LIPSIA—a new software system for the evaluation of functional magnetic resonance images of the human brain. Computerized medical imaging and graphics. 2001;25:449–457. PubMed

Perron O. Zur Theorie der Matrices. Math. Ann. 1907;64:248–263. doi: 10.1007/BF01449896. DOI

Goelman G., Gordon N., Bonne O. Maximizing negative correlations in resting-state functional connectivity MRI by time-lag. PLoS One. 2014;9 PubMed PMC

Murphy K., Birn R.M., Handwerker D.A., Jones T.B., Bandettini P.A. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage. 2009;44:893–905. PubMed PMC

Schrouff J., et al. PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics. 2013;11:319–337. PubMed PMC

Whitwell J.L., et al. Radiological biomarkers for diagnosis in PSP: where are we and where do we need to be? Movement Disorders. 2017;32:955–971. PubMed PMC

Worker A., et al. Cortical thickness, surface area and volume measures in Parkinson's disease, multiple system atrophy and progressive supranuclear palsy. PLoS One. 2014;9 PubMed PMC

Agosta F., et al. Tracking brain damage in progressive supranuclear palsy: a longitudinal MRI study. J. Neurol. Neurosurg. Psychiatr. 2018;89:696–701. PubMed

Hillary F.G., et al. Hyperconnectivity is a fundamental response to neurological disruption. Neuropsychology. 2015;29:59. PubMed

Ballarini T., et al. Disentangling brain functional network remodeling in corticobasal syndrome - a multimodal MRI study. Neuroimage Clin. 2020;25 doi: 10.1016/j.nicl.2019.102112. PubMed DOI PMC

Mueller K., et al. Disease-specific regions outperform whole-brain approaches in identifying progressive supranuclear palsy: a multicentric MRI study. Front. Neurosci. 2017;11 doi: 10.3389/fnins.2017.00100. PubMed DOI PMC

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