scholarly journals Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

2018 ◽  
Vol 83 (7) ◽  
pp. 579-588 ◽  
Author(s):  
Nicolas Traut ◽  
Anita Beggiato ◽  
Thomas Bourgeron ◽  
Richard Delorme ◽  
Laure Rondi-Reig ◽  
...  
2017 ◽  
Author(s):  
Nicolas Traut ◽  
Anita Beggiato ◽  
Thomas Bourgeron ◽  
Richard Delorme ◽  
Laure Rondi-Reig ◽  
...  

AbstractCerebellar volume abnormalities have been often suggested as a possible endophenotype for autism spectrum disorder (ASD). We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature, and an analysis of the Autism Brain Imaging Data Exchange (ABIDE) cohort. Our meta-analysis sought to determine a combined effect size of ASD diagnosis on different measures of the cerebellar anatomy, as well as the effect of possible factors of variability across studies. We then analysed the cerebellar volume of 328 patients and 353 controls from the ABIDE project. The meta-analysis of the literature suggested a weak but significant association between ASD diagnosis and increased cerebellar volume (p=0.049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies in the literature were generally underpowered, however, the number of statistically significant findings was larger than expected. Although we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex and IQ were important sources of cerebellar volume variability, however, independent of autism diagnosis.


2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Adriana Di Martino ◽  
David O’Connor ◽  
Bosi Chen ◽  
Kaat Alaerts ◽  
Jeffrey S. Anderson ◽  
...  

2013 ◽  
Vol 19 (6) ◽  
pp. 659-667 ◽  
Author(s):  
A Di Martino ◽  
C-G Yan ◽  
Q Li ◽  
E Denio ◽  
F X Castellanos ◽  
...  

2019 ◽  
Author(s):  
Yafeng Zhan ◽  
Jianze Wei ◽  
Jian Liang ◽  
Xiu Xu ◽  
Ran He ◽  
...  

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.


2012 ◽  
Vol 142 (1-3) ◽  
pp. 200-205 ◽  
Author(s):  
Ming Li ◽  
Yi Wang ◽  
Xue-bin Zheng ◽  
Masashi Ikeda ◽  
Nakao Iwata ◽  
...  

2010 ◽  
Vol 62 (2) ◽  
pp. 183-196 ◽  
Author(s):  
Jessica Albrecht ◽  
Rainer Kopietz ◽  
Johannes Frasnelli ◽  
Martin Wiesmann ◽  
Thomas Hummel ◽  
...  

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