We live in an era in which incredible scientific discoveries are happening. Neuroscientists can now trace connections and structures of the entire human brain. They can detect normal and abnormal activities of brain areas in just a fraction of second thanks to artificial neural networks and create computer chips that help the brain reach its potential abilities.
If your goal is to pair science with technology, there is a place to materialize all the brilliant and creative ideas, BCINEUROTECH.
Every day, our teams work to solve some of the challenging brain science questions in the history of humankind, evermore to put our knowledge and ideas to make the world a better place for patients. To do this, we become explorers more than just cientists. We will pioneer to translate our discoveries into effective treatments. Being a scientist at BCINEUROTECH means living years in the future and discovering transformative medicines for those who need them.
Our Recent Researchs
An MRI-based Deep Learning Model to Predict Parkinson’s Disease Stages
Parkinson’s disease (PD) is amongst the relatively prevalent neurodegenerative disorders with its course of progression classified as prodromal, stage1, 2, 3 and sever conditions. With all the shortcomings in clinical setting, it is often challenging to identify the stage of PD severity and predict its progression course. Therefore, there appear to be an ever-growing need need to use supervised and unsupervised artificial intelligence and machine learning methods on clinical and paraclinical datasets to accurately diagnose PD, identify its stage and predict its course. In today’s neuro-medicine practices, MRI-related data are regarded beneficial in detecting various pathologies in the brain. In addition, the field has recently witnessed a growing application of deep learning methods in image processing often with outstanding results. Here, we applied Convolutional Neural Networks (CNN) to propose a model helping to distinguish different stages of PD. The results showed that our current MRI-based CNN model may potentially be employed as a suitable method for the distinction of PD stages at a high accuracy rate (0.94).
A Convolutional Neural Network Model to Differentiate Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder Based on the Resting State fMRI Data
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.
onclusion: 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.