scholarly journals Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuhui Du ◽  
Zening Fu ◽  
Ying Xing ◽  
Dongdong Lin ◽  
Godfrey Pearlson ◽  
...  

AbstractSchizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.

Neurology ◽  
2006 ◽  
Vol 67 (4) ◽  
pp. 632-636 ◽  
Author(s):  
H. Petropoulos ◽  
S. D. Friedman ◽  
D.W.W. Shaw ◽  
A. A. Artru ◽  
G. Dawson ◽  
...  

2011 ◽  
Vol 68 (4) ◽  
pp. 409 ◽  
Author(s):  
Esther Via ◽  
Joaquim Radua ◽  
Narcis Cardoner ◽  
Francesca Happé ◽  
David Mataix-Cols

2020 ◽  
Vol 13 (9) ◽  
pp. 1489-1500 ◽  
Author(s):  
Kaitlin K. Cummings ◽  
Katherine E. Lawrence ◽  
Leanna M. Hernandez ◽  
Emily T. Wood ◽  
Susan Y. Bookheimer ◽  
...  

2017 ◽  
Vol 47 (6) ◽  
pp. 568-578 ◽  
Author(s):  
Alessia Giuliano ◽  
Irene Saviozzi ◽  
Paolo Brambilla ◽  
Filippo Muratori ◽  
Alessandra Retico ◽  
...  

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e25-e25
Author(s):  
Sarah MacEachern ◽  
Deepthi Rajashekar ◽  
Pauline Mouches ◽  
Nathan Rowe ◽  
Emily Mckenna ◽  
...  

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.


2020 ◽  
Vol 27 ◽  
pp. 102343 ◽  
Author(s):  
Christopher J. Hyatt ◽  
Vince D. Calhoun ◽  
Brian Pittman ◽  
Silvia Corbera ◽  
Morris D. Bell ◽  
...  

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