Abnormal degree centrality and functional connectivity in Down syndromea resting-state fMRI study

  1. Cristina Cañete-Massé 1
  2. Maria Carbó-Carreté 1
  3. Maribel Peró-Cebollero 1
  4. Shi-Xian Cui 2
  5. Chao-Gan Yan 2
  6. Joan Guàrdia-Olmos 1
  1. 1 Universitat de Barcelona
    info

    Universitat de Barcelona

    Barcelona, España

    ROR https://ror.org/021018s57

  2. 2 CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
Revista:
International journal of clinical and health psychology

ISSN: 1697-2600

Año de publicación: 2023

Volumen: 23

Número: 1

Páginas: 111-120

Tipo: Artículo

DOI: 10.1016/J.IJCHP.2022.100341 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International journal of clinical and health psychology

Resumen

Background/Objective Neuroimaging studies have shown brain abnormalities in Down syndrome (DS) but have not clarified the underlying mechanisms of dysfunction. Here, we investigated the degree centrality (DC) abnormalities found in the DS group compared with the control group, and we conducted seed-based functional connectivity (FC) with the significant clusters found in DC. Moreover, we used the significant clusters of DC and the seed-based FC to elucidate differences between brain networks in DS compared with controls. Method The sample comprised 18 persons with DS (M = 28.67, SD = 4.18) and 18 controls (M = 28.56, SD = 4.26). Both samples underwent resting-state functional magnetic resonance imaging. Results DC analysis showed increased DC in the DS in temporal and right frontal lobe, as well as in the left caudate and rectus and decreased DC in the DS in regions of the left frontal lobe. Regarding seed-based FC, DS showed increased and decreased FC. Significant differences were also found between networks using Yeo parcellations, showing both hyperconnectivity and hypoconnectivity between and within networks. Conclusions DC, seed-based FC and brain networks seem altered in DS, finding hypo- and hyperconnectivity depending on the areas. Network analysis revealed between- and within-network differences, and these abnormalities shown in DS could be related to the characteristics of the population.

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