scholarly journals PM343. Functional magnetic resonance imaging study on neural aspects of cognitive and emotional empathy in autism spectrum disorder

2016 ◽  
Vol 19 (Suppl_1) ◽  
pp. 25-26
Autism ◽  
2021 ◽  
pp. 136236132110020
Author(s):  
Bruno Direito ◽  
Susana Mouga ◽  
Alexandre Sayal ◽  
Marco Simões ◽  
Hugo Quental ◽  
...  

Autism spectrum disorder is characterized by abnormal function in core social brain regions. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback. Following up the demonstration of neuromodulation in healthy participants, in this repeated-measure design clinical trial, 15 autism spectrum disorder patients were enrolled in a 5-session training program of real-time functional magnetic resonance imaging neurofeedback targeting facial emotion expressions processing, using the posterior superior temporal sulcus as region-of-interest. Participants were able to modulate brain activity in this region-of-interest, over multiple sessions. Moreover, we identified the relevant clinical and neural effects, as documented by whole-brain neuroimaging results and neuropsychological measures, including emotion recognition of fear, immediately after the intervention and persisting after 6 months. Neuromodulation profiles demonstrated subject-specificity for happy, sad, and neutral facial expressions, an unsurprising variable pattern in autism spectrum disorder. Modulation occurred in negative or positive directions, even for neutral faces, in line with their often-perceived ambiguity in autism spectrum disorder. Striatal regions (associated with success/failure of neuromodulation), saliency (insula/anterior cingulate cortex), and emotional control (medial prefrontal cortex) networks were recruited during neuromodulation. Recruitment of the operant learning network is consistent with participants’ engagement. Compliance, immediate intervention benefits, and their persistence after 6 months pave the way for a future Phase IIb/III, randomized controlled clinical trial, with a larger sample that will allow to conclude on clinical benefits from neurofeedback training in autism spectrum disorder (NCT02440451). Lay abstract Neurofeedback is an emerging therapeutic approach in neuropsychiatric disorders. Its potential application in autism spectrum disorder remains to be tested. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback in targeting social brain regions in autism spectrum disorder. In this clinical trial, autism spectrum disorder patients were enrolled in a program with five training sessions of neurofeedback. Participants were able to control their own brain activity in this social brain region, with positive clinical and neural effects. Larger, controlled, and blinded clinical studies will be required to confirm the benefits.


2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.


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