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Verfasst von:Vives-Gilabert, Yolanda [VerfasserIn]   i
 Abdulkadir, Ahmed [VerfasserIn]   i
 Kaller, Christoph P. [VerfasserIn]   i
 Mader, Wolfgang [VerfasserIn]   i
 Wolf, Robert Christian [VerfasserIn]   i
 Schelter, Björn [VerfasserIn]   i
 Klöppel, Stefan [VerfasserIn]   i
Titel:Detection of preclinical neural dysfunction from functional connectivity graphs derived from task fMRI
Titelzusatz:an example from degeneration
Verf.angabe:Yolanda Vives-Gilabert, Ahmed Abdulkadir, Christoph P. Kaller, Wolfgang Mader, Robert C. Wolf, Björn Schelter, Stefan Klöppel
E-Jahr:2013
Jahr:6 October 2013
Umfang:9 S.
Fussnoten:Gesehen am 03.03.2022
Titel Quelle:Enthalten in: Psychiatry research. Neuroimaging
Ort Quelle:Amsterdam : Elsevier, 2016
Jahr Quelle:2013
Band/Heft Quelle:214(2013), 3, Seite 322-330
ISSN Quelle:1872-7506
Abstract:The early, preferably pre-clinical, identification of neurodegenerative diseases is important as treatment will be most successful before substantial neuronal loss. Here, we reasoned that functional brain changes as measured using functional magnetic resonance imaging (fMRI) will precede neurodegeneration. Three independent cohorts of patients with the genetic mutation leading to Huntington's Disease (HD) but without any clinical symptoms and matched controls performed three different fMRI tasks: Sequential finger tapping engaged the motor system, which is primarily affected by HD, whereas a working-memory task and a task aiming to induce irritation represented the range of low- and high-level cognitive functions also affected by HD. Each diagnostic group of every cohort included 11-16 subjects. After segmentation into 76 cortical and 14 subcortical regions, we extracted functional connectivity patterns through pairwise correlation between the signals in the regions. The resulting coefficients were directly embedded as input to a pattern classifier aiming to separate controls from gene mutation carriers. Alternatively, graph-theory measures such as degree and clustering coefficient were used as features for the discrimination. Classification accuracy never outperformed the accuracy of a grouping based on parameter estimates from a general-linear model approach or a grouping based on features extracted from anatomical images as reported in a previous analysis. Despite good within-subject stability between two runs of the same task, a high between-subject variability led to chance-level accuracy. These results indicate that standard graph-metrics are insufficient to detect subtle disease related changes when within-group variability is high. Developing methods that reduce variability related to noise should be the focus of future studies.
DOI:doi:10.1016/j.pscychresns.2013.09.009
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.pscychresns.2013.09.009
 Volltext: https://www.sciencedirect.com/science/article/pii/S0925492713002655
 DOI: https://doi.org/10.1016/j.pscychresns.2013.09.009
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Early detection
 fMRI
 Graph theory
 Neurodegeneration
 Pattern recognition
K10plus-PPN:1794527958
Verknüpfungen:→ Zeitschrift

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