scholarly journals A Convolutional Neural Network Model to Differentiate Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder Based on the Resting State fMRI Data

2021 ◽  
Author(s):  
Azadeh Mozhdehfarahbakhsh ◽  
Amirsaeid Moloodi ◽  
Prasun Chakrabarti ◽  
KS Jagannatha Rao ◽  
Babak Kateb ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Resting State ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Hyperactivity Disorder ◽  
The Brain

Background and Objectives: Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are the two most common neurodevelopmental disorders often with overlapping symptoms. Misdiagnosis of these disorders is the leading cause of a variety of problems including inappropriate interventions and improper treatment outcome. Over the last few years, resting state functional magnetic Resonance imaging (rs-fMRI) has received clinical attention among other beneficial brain scan techniques to extract functional connectivity in the brain. However, extracting useful information by human observation is prone to errors. Material and Methods: The above unmet need prompted us to design the present investigation to construct a convolutional neural network model with 12 layers architecture in rsFMRI data aiming to differentiate the two conditions. The rs-fMRI data was collected from the ADHD-200 and ABIDE to feed into a convolutional neural network. Over the preprocessing phase, we have removed undesirable data and coordinated the remaining to MSDL atlas to recruit 39 regions of the brain. Results: Ultimately, out results obtained a 0.92 accuracy, an AUC of 0.97 and loss of 0.17 in classification and discrimination of ADHD and ASD. Conclusion: Though cross-validity with larger datasets is deemed required, the results obtained from the present investigation suggest that convolutional neural network may serve as a beneficial tool to differentiate ADHD and ASD from relatively small fMRI datasets. This further highlights the potential application of deep neural networks for serving the above purpose.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

Attention Deficit Hyperactivity Disorder in Autism Spectrum Disorder

2020 ◽  
pp. 158-176
Author(s):  
Karen Bearss ◽  
Aaron J. Kaat
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Spectrum Disorder ◽  
Adhd Symptoms ◽  
Functional Impairments ◽  
Hyperactivity Disorder ◽  
Delayed Recognition

This chapter will review the available evidence on individuals with co-occurring diagnoses of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). This chapter contends that children diagnosed with both disorders (ASD+ADHD) are a subset of the ASD population that is at risk for delayed recognition of their ASD diagnosis, poor treatment response, and poorer functional outcomes compared to those with ASD without ADHD. Specifically, the chapter highlights the best estimates of the prevalence of the comorbidity, the developmental trajectory of people with co-occurring ASD and ADHD, how ADHD symptoms change across development, overlapping genetic and neurobiological risk factors, psychometrics of ADHD diagnostic instruments in an ASD population, neuropsychological and functional impairments associated with co-occurring ASD and ADHD, and the current state of evidence-based treatment for both ASD and ADHD symptoms. Finally, the chapter discusses fruitful avenues of research for improving understanding of this high-risk comorbidity so that mechanism-to-treatment pathways for ADHD in children with ASD can be better developed.

scholarly journals Medications for attention-deficit/hyperactivity disorder in individuals with or without coexisting autism spectrum disorder: analysis of data from the Swedish prescribed drug register

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Viktoria Johansson ◽  
Sven Sandin ◽  
Zheng Chang ◽  
Mark J. Taylor ◽  
Paul Lichtenstein ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Clinical Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Prescribing Patterns ◽  
Adhd Medication ◽  
Hyperactivity Disorder ◽  
Drug Register

Abstract Background Clinical studies found that medication for attention-deficit/hyperactivity disorder (ADHD) is effective in coexisting autism spectrum disorder (ASD), but current research is based on small clinical studies mainly performed on children or adolescents. We here use register data to examine if individuals with ADHD and coexisting ASD present differences in the prescribing patterns of ADHD medication when compared to individuals with pure ADHD. Methods Data with information on filled prescriptions and diagnoses was retrieved from the Swedish Prescribed Drug Register and the National Patient Register. We identified 34,374 individuals with pure ADHD and 5012 individuals with ADHD and coexisting ASD, aged between 3 and 80 years. The first treatment episode with ADHD medications (≥ 2 filled prescriptions within 90 days) and daily doses of methylphenidate during a 3-year period was measured. Odds ratios (ORs) were calculated for the likelihood of being prescribed ADHD medication in individuals with and without ASD and Wilcoxon rank-sum test was used to compare group differences in dose per day. Results Individuals with ADHD and coexisting ASD were less likely to start continuous treatment with ADHD medication (ADHD 80.5%; ADHD with ASD 76.2%; OR, 0.80; 95% confidence interval, 0.75-0.86), were less likely to be prescribed methylphenidate, and were more commonly prescribed second line treatments such as dexamphetamine, amphetamine, or modafinil. No group difference was observed for atomoxetine. In adults with ADHD and coexisting ASD, methylphenidate was prescribed in lower daily doses over three years as compared to individuals with pure ADHD. Conclusions The findings indicate that there are differences in the medical treatment of individuals with or without ASD. If these differences are due to different medication responses in ASD or due to other factors such as clinicians’ perceptions of medication effects in patients with ASD, needs to be further studied.

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