Journal Article

FZJ-2018-04790

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A lack of consistent brain alterations in insomnia disorder: An activation likelihood estimation meta-analysis

Tahmasian, M. ; Noori, K. ; Samea, F. ; Zarei, M. ; Spiegelhalder, K. ; Eickhoff, S.FZJ* ; van Someren, E. ; Khazaie, H. (Corresponding author) ; Eickhoff, C.FZJ*

2018
Elsevier Kidlington, Oxford [u.a.]

This record in other databases:      

Please use a persistent id in citations: http://hdl.handle.net/2128/21653  doi:10.1016/j.smrv.2018.07.004

Abstract: Insomnia disorder is a prevalent sleep disorder, which affects about 10% of general population. However, its neural mechanisms are poorly understood. Recently, several structural and functional neuroimaging studies have been conducted in patients with insomnia disorder, but these studies have yielded diverse findings. Here, we aimed to identify consistent patterns of abnormal brain alterations in insomnia disorder by performing a quantitative coordinate-based meta-analysis. Following the preferred reporting for systematic reviews and meta-analyses statement, we searched PubMed database and used reference tracking and finally retrieved 19 eligible studies (six task-based functional magnetic resonance imaging, eight resting-state functional magnetic resonance imaging, three voxel-based morphometry, and two positron emission tomography). We extracted peak coordinates from these studies and tested for convergence using the activation likelihood estimation method. Using this method, we found no significant convergent evidence for combination of structural atrophy and functional disturbances across previous studies (p = 0.914). Inconsistencies across these studies might be related to heterogonous clinical populations, the explorative nature of these studies in combination with small sample sizes, different experimental designs, and various preprocessing and statistical approaches. Future neuroimaging studies on insomnia disorder should include larger well-characterized samples, as well as standard imaging and analysis protocols.

Note: This study was supported by Kermanshah University of Medical Sciences (No. 94526). Simon B. Eickhoff is supported by the Deutsche Forschungsgemeinschaft (EI 816/11-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 7202070 (HBP SGA1) and under Grant Agreement No. 785907 (HBP SGA2).

---

Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)
2. Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Research Program(s):

1. 572 - (Dys-)function and Plasticity (POF3-572) (POF3-572)
2. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
3. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
4. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)

Appears in the scientific report 2018

---

Database coverage:
; ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

---