Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Song, Jung Han; Yoon, Narae; Jang, Soo Min; Lee, Ga Young; Kim, Bung Nyun

doi:10.5765/jkacap.200021

Cited by 8 publications

(5 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, deep learning frameworks complement, not replace the physician's regular diagnosis of medical disorders. ASD has been diagnosed via deep learning [26], [27].Among deep learning architectures, autoencoders [28] enable lowdimensional embedding of a high-dimensional input via an encoder-decoder block. We employ a Generative Adversarial Model (GAN)-based encoder-decoder framework for sMRIbased ASD detection.…”

Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

View full text Add to dashboard Cite

Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (cognitive tests) is challenging due to wide variations amongst individuals. Since no effective treatment exists, prompt and reliable ASD diagnosis can enable the effective preparation of treatment regimens. This paper proposes structural Magnetic Resonance Imaging (sMRI)-based ASD diagnosis via an outlier detection approach. To learn spatio-temporal patterns in structural brain connectivity, a Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Given a stack of three adjacent slices as input, the GAN generator reconstructs the next three adjacent slices; the GAN discriminator then identifies ASD sMRI scan reconstructions as outliers. This model is compared against two other baselines-a simpler UNet and a sophisticated Self-Attention GAN. Axial, Coronal and Sagittal sMRI slices from the multi-site ABIDE II dataset are used for evaluation. Extensive experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data. Furthermore, longitudinal data (two scans per subject over time) achieve 17-28% higher accuracy than cross-sectional data (one scan per subject). Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Heinsfeld and others looked analysed brain imaging data from ASD patients from a global multi-site database called ABIDE (Autism Brain Imaging Data Exchange). Their study's objective was to use deep learning algorithms to identify autism spectrum disorder individuals from a large brain imaging dataset purely based on their brain activity patterns [18,19]. By reaching 70% accuracy in identifying ASD vs. control patients in the dataset, the researchers enhanced the state-of-the-art.…”

Section: Related Workmentioning

confidence: 99%

Conditional Generative Adversarial Network Approach for Autism Prediction

Raja¹,

Kannimuthu²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.

show abstract

“…Earlier contributions used classical ML methods (such as with MRI/fMRI [29,[32][33][34][35]). More recently, deep learning methods have demonstrated a considerable advantage over classical approaches due to their ability to rely on hidden representations of extracted features in MRI/fMRI [36]. Other non-invasive methods have been coupled with ML for ASD.…”

Section: Introductionmentioning

confidence: 99%

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

et al. 2023

View full text Add to dashboard Cite

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

show abstract

Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Cited by 8 publications

References 59 publications

Outlier-Based Autism Detection Using Longitudinal Structural MRI

Outlier-Based Autism Detection Using Longitudinal Structural MRI

Conditional Generative Adversarial Network Approach for Autism Prediction

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

Contact Info

Product

Resources

About