BACKGROUND

More than 1.5 Million children in the United States are affected by Autism spectrum disorder (ASD), which is a heterogeneous neurodevelopmental disorder that is characterized by impaired social interactions, limited communication (including verbal and non-verbal), and/or exhibiting repeated or limited interests or activities. Individuals with ASD have numerous challenges in daily life, and they often develop comorbidities such as depression, anxiety disorder, or attention deficit hyperactivity disorder (ADHD), which may further complicate the diagnostic processes especially for young children. Although some symptoms are generally recognized between 1 or 2 years of age, numerous children are not formally diagnosed with ASD until they are much older. ASD is currently diagnosed via a psychiatric diagnostic process, which is based exclusively on behavioral observation of symptomology. This process is prone to misdiagnosis, especially in the presence of co-occurring and/or alternative diagnoses.

BRIEF SUMMARY

In view of the issues with the current diagnosis process for autism spectrum disorder (ASD), there is a need in the art for a more quantitative diagnosis. In order to address this, embodiments of the subject invention provide novel and advantageous systems and methods for diagnosing ASD using only functional magnetic resonance imaging (fMRI) data. Advanced and scalable machine learning infrastructure can be used to identify reliable biomarkers of ASD in order to classify patients with ASD from among a group of typical control subjects using only fMRI. A sparse autoencoder (SAE) can be used, resulting in optimized extraction of features that can be used for classification. These features can then be fed into a deep neural network (DNN), which results in superior classification of fMRI brain scans more prone to ASD. The model used with systems and methods of embodiments of the subject invention can be trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error.

In an embodiment, a system for diagnosing ASD (e.g., in a patient or a plurality of patients (i.e., classifying patients from among a group as either having ASD or not)) can comprise a processor and a (non-transitory) machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps: receiving fMRI data (e.g., from a database, from a server or other computing device, or from an MRI machine that is a part of the system or with which the system is in operable communication) comprising a plurality of brain scans obtained from a plurality of patients, respectively; using a SAE on the fMRI data to extract testing patterns and generate a testing feature vector; providing the testing feature vector as a testing input to a DNN; and running the DNN on the testing feature vector to determine which brain scans of the plurality of brain scans are indicative of ASD, said brain scans that are indicative of ASD thereby resulting in a diagnosis of ASD in the corresponding patients, of the plurality of patients, from which said brain scans were obtained. The DNN can comprise two hidden layers, a softmax output layer, and/or an input layer. The instructions when executed can further performing the following step(s): training the SAE prior to using the SAE to extract testing patterns and generate the testing feature vector; and/or training the DNN prior to running the DNN on the testing feature vector. The training of the SAE and the training of the DNN can be performed simultaneously. The training of the SAE and the training of the DNN can comprise running a predetermined number of iterations of training on the SAE and the DNN (see alsoFIG. 2B), each iteration comprising: i) extracting time series from the fMRI data; ii) calculating Pearson's correlations on the time series; iii) selecting ¼ smallest and ¼ largest average pairwise correlations of the Pearson's correlations to generate a training feature vector and a SAE loss; iv) providing the training feature vector as a training input to the DNN; v) running the DNN on the training feature vector to generate a prediction loss; vi) combining the SAE loss and the prediction loss to generate a total loss; vii) providing the total loss to the SAE and the DNN; and viii) returning to step i). The predetermined number of iterations can be, for example, at least 30. The DNN can (alternatively) be a pre-trained DNN. The SAE can comprise a cost function comprising a mean squared error, a weight decay, and a sparsity term. The training of the SAE (prior to using the SAE to extract testing patterns and generate the testing feature vector) can comprise minimizing the cost function of the SAE.

In another embodiment, a method for diagnosing ASD can comprise: receiving (e.g., by a processor) fMRI data (e.g., from a database, from a server or other computing device, or from an MM machine in operable communication with the processor) comprising a plurality of brain scans obtained from a plurality of patients, respectively; using (e.g., by the processor) a SAE on the fMRI data to extract testing patterns and generate a testing feature vector; providing (e.g., by the processor) the testing feature vector as a testing input to a DNN; and running (e.g., by the processor) the DNN on the testing feature vector to determine which brain scans of the plurality of brain scans are indicative of ASD, said brain scans that are indicative of ASD thereby resulting in a diagnosis of ASD in the corresponding patients, of the plurality of patients, from which said brain scans were obtained. The DNN can comprise two hidden layers, a softmax output layer, and/or an input layer. The method can further comprise: training (e.g., by the processor) the SAE prior to using the SAE to extract testing patterns and generate the testing feature vector; and/or training (e.g., by the processor) the DNN prior to running the DNN on the testing feature vector. The training of the SAE and the training of the DNN can be performed simultaneously. The training of the SAE and the training of the DNN can comprise running (e.g., by the processor) a predetermined number of iterations of training on the SAE and the DNN (see alsoFIG. 2B), each iteration comprising: i) extracting time series from the fMRI data; ii) calculating Pearson's correlations on the time series; iii) selecting ¼ smallest and ¼ largest average pairwise correlations of the Pearson's correlations to generate a training feature vector and a SAE loss; iv) providing the training feature vector as a training input to the DNN; v) running the DNN on the training feature vector to generate a prediction loss; vi) combining the SAE loss and the prediction loss to generate a total loss; vii) providing the total loss to the SAE and the DNN; and viii) returning to step i). The predetermined number of iterations can be, for example, at least 30. The DNN can (alternatively) be a pre-trained DNN. The SAE can comprise a cost function comprising a mean squared error, a weight decay, and a sparsity term. The training of the SAE (prior to using the SAE to extract testing patterns and generate the testing feature vector) can comprise minimizing the cost function of the SAE.

