The Workflow Designer is a prototype web-based application allowing drag-and-drop creating, editing, and running workflows from a predefined library of methods. Moreover, any workflow can be exported or imported in JSON format to ensure reusability and local execution of exported JSON configurations. The application is primarily focused on electroencephalographic signal processing and deep learning workflows.

Currently, the entire Workflow Designer system (server, workflow system and methods) is based on Java. The aim of this project is to transfer backend technologies from Java to Python and allow executing workflow blocks (methods) implemented in Python, using e.g. MNE for EEG signal processing, or TensorFlow for deep learning. Just like in the current version, each block has inputs, outputs (can be streams, arrays, files, etc.) and parameters that can be configured using a GUI.  After the system is transformed, develop a few deep learning workflow-related blocks to demonstrate the functionality of the system.

The objectives I wish to achieve for EEG and DL workflow are:

1. Rewrite all the models/algorithms in python. I.e. re-writing:

   1.  Neural network models and classifiers in python using Keras (TensorFlow). 

   2. Preprocessing, Low/High pass filter, epoch extraction, averaging filter, etc.

   3. Feature extraction algorithms (wavelet transform)

   4. Data visualization algorithms

2. Rewriting the server in python(Flask):

   1. User login/logout/sign-in/sign-up

   2. Blocks and their functional APIs

   3. Drag and drop UI 

3. New feature development and bug fixing

   1. Extra deep learning models

   2. GPU integration

   3. Proper documentation

Recent advances in the field of computer vision and deep learning has shown great promise in their ability to decipher images and derive inferences involving classifications, detection of objects and approximation of certain values with high accuracy. 

Pre-trained models like YOLONet and ResNet are now being used in various industries where they help make our lives easier. But these kinds of models are not yet being used for microscopic images on a large scale. With the right model architecture and training approaches, it is possible to get pre-trained models which would help in the research efforts of many. These pre-trained models, combined with a GUI would act as a community tool which would help speed up the classification of thousands of microscopic images and gain inferences from them.

The top priorities of this proposal are:

• Train a deep learning model(s) from the image dataset(s) provided. 

• In the process of training, develop a data augmentation pipeline which can be used on the cellular image datasets (even on the cellular images which are not involved in this project) to help build a model robust enough for its purpose. 

• Make the trained model portable so that it can be easily integrated into a GUI backend.

Neuroscience data comes in multiple different data formats and structures. These differences provide a major technical barrier to sharing data between labs or even within labs. Often the organization and naming conventions of neuroscience data structures further obscures how to understand and analyze the data unless already intimately familiar with a specific data structure. The Neurodata Without Borders (NWB 1 ) Initiative provides neurophysiology datasets in a standardized HDF5 format that employs domain knowledge to alleviate the burden of different data formats and structures across multiple experimental paradigms. In addition, the NWB Initiative provides tools for handling, visualizing and analyzing NWB formatted data. 

This proposed project aims to contribute to NWB Showcase made available at NWB Explorer 2 on the Open Source Brain repository 3 . The proposed project will deliver multiple converted datasets to be viewed at the NWB Explorer and will integrate tutorials and analysis examples for select converted datasets.

In any open-source organization, one of the key factors to grow is how many people are involved in it. I have introduced OpenWorm in many conferences and meetups and everyone was interested in contributing to it. Many new members have also joined OpenWorm Slack in recent days. 

To remove this barrier I have identified a few major factors listed below.

• We have many models, projects but no commonplace to access it.

• Social Reach is also not so good.

• “People coming from outside find difficulty in knowing what’s going on in the organization” (Anonymous user).

The OpenDevoCell Integration is going to be a great initiative to solve this barrier, it not only helps the other researchers to see and appreciate our work but it would also help to organize this organization functioning in the coming days. OpenDevoCell is going to be the one-stop portal so that everyone from any part of the world can access our work in an effective manner. Many biologists need ML in their work nowadays, but they don’t know much about ML, which is kind of a barrier in their work. We would try to solve these problems for the biologist who has the same research interests as ours.

In this project, we are integrating some projects which are being developed in past year GSoC projects. I am also fortunate to work in the Digital Bacillaria project, so I know in the depth of how that project works and how to implement it in a web portal. 

Also, the idea of deploying a python library also is a very good initiative. It will make it even easier to use our models as you can just import our library and get all our data in your python code to do your research.

Image registration is the process of finding a transformation that aligns one image to another. DIPY currently supports several numerical optimization-based techniques for image registration. Even though these methods perform well, they are limited by their slow registration speeds. The goal of this project is to develop deep learning-based methods that can achieve image registration in one-shot resulting in much faster registration speeds. In this project, I propose to develop deep neural networks (DNNs) for MRI registration using thin-plate splines, free-form deformations, and affine transformations. I also plan to extend the implementation of thin-plate splines to use cases other than image registration.

“LORIS (Longitudinal Online Research and Imaging System), a web-based data and project management software for neuroimaging research studies”

( https://mcin.ca/technology/loris/ ). It is a very convenient tool for researchers conducting neuroimaging research studies, or any clinical studies that involves multiple costly measurements (especially longitudinal studies), are often statistically underpowered because of the difficulty to get data from enough subjects ( e.g. because of the difficulty to recruit subjects, the measurements are very time consuming or subjects are dropping from the study). The obvious solution to increase the datasets’ size is to collect data from multiple sites, but using data with multiple sources needs a very high level of care to make the data collected compatible. Subjects in clinical studies can have a high degree of variability, so every detail must be tracked with caution in an effort to explain this variability. Additionally, multi-center studies involve a high number of people, which alsoincreases variability.

Another major contribution of LORIS is to make data available to researchers that wish to conduct neuroimaging research downstream of data collection. Indeed, neuroscience is very interdisciplinary ( e.g. psychology, medicine, biology, bioinformatics, statistics, machine learning) and not all researchers involved in the process should have to collect their own data to do what they do best. As a computer scientist and neurobiologist, I have been on both sides of this research cycle. From my experience, it is common for researchers to collect their own data to answer a very specific question, when the data from other studies might have been adequate to answer the question if it had it open sourced data. Thus, LORIS should help to reduce useless redundancy of studies, or at least put them together to make better studies.

The REST API is an easy way to securely access, retrieve and manipulate the sensitive data about the subjects stored in LORIS. This data contained in Loris should be easily accessible to the researchers allowed to use it, but such information is very personal to the subjects, so the security of these actions on Loris’ database is of foremost importance for ensuring the subjects confidentiality. The REST API is already a work in progress, so I will be implementing endpoints for the modules not already accessible via the API.

LORIS is a web application used to manage as well as host, and visualize data with a focus on neuroscience and genomics (among other fields). Services are split into different modules offered to researchers. Frontend is written in javascript, allowing it to:

• Display on multiple browsers and platforms

• The use of Reactjs, a component-based framework designed for creating UIs

• The use of D3, a javascript library for data visualization

 Given LORIS' scope, there is room for extensibility: particularly for visualization. The objective of this project is to offer end users new visualizations for time-series data. This would allow better interpretation of data shared online through LORIS and paves the path for more comprehensive analytical tools with regards to the data being visualized. This alo requires the data uploaded to meet certain formats or to convert it to certain formats. Along with a visualization tool will be the need to properly validate the uploaded data.

LORIS is an open-source framework that facilitates data sharing for neuroscience labs and sites. It hosts both frontend and backend services that help facilitate data sharing and manipulation among researchers. The codebase includes many modules that perform different functionalities. Therefore, this type of service requires automated testing to ensure that all the moving parts are working correctly.

Both unit and integration tests are necessary for a large project like this to run smoothly so that any bugs can be caught and fixed early and efficiently. This is especially important for projects like this that work with data manipulation and therefore require careful attention to detail. Improving the testing database and the test datasets is also a very important part of this project since the automated tests cannot be relied upon if the datasets being used to test them do not reflect the real world. This will need to be a big focus of the project. Finally, creating documentation that can help future developers and users test LORIS themselves and write their own tests is integral to the continuation of this work.

Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) depicts changes in deoxyhemoglobin concentration consequent to task-induced or spontaneous modulation of neural metabolism. An increase in neural activity corresponds to a local increase in oxygenated blood supply. Attributing the contrasting magnetic properties of oxygenated Hemoglobin and deoxygenated Hemoglobin, fMRI measures these alterations in the relative composition of local blood supply. It is non-invasive as it does not employ radiation, making it a virtually zero-risk procedure. It also enjoys relatively low cost, widespread availability, and a good temporal to spatial resolution tradeoff, making it the predominant choice for measuring brain activity [1].

fMRI is an indirect measure of neural activity. The resulting BOLD signal attributes to the underlying neural activity as well as the Hemodynamic Response Function (HRF). Hence, variability in the HRF can be confused with variability in the neural activity. Several studies have established that HRF varies across subjects as well as across brain regions for a particular subject. This makes it necessary to individually estimate the resting state HRF (rsHRF) across different regions of a brain. An effective methodology for the same has been suggested by Wu et.al[2]. It is based on point process theory and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape. This has been implemented in the rsHRF toolbox.

The Virtual Brain (TVB) is a neuroinformatics platform for the simulation of the dynamics of large-scale brain networks with biologically realistic connectivity. It aims to bridge the gap between the various levels (microscopic, mesoscopic and macroscopic levels) of brain modeling. It is foremost a scientific simulation platform and provides all means necessary to generate, manipulate and visualize connectivity and network dynamics. Its simulation toolkits facilitate the top-down modeling approach to whole-brain dynamics[3].

TVB also allows simulation of BOLD activity, however, it suffers from the drawback of considering a standard model of HRF across subjects as well as across brain regions of a particular subject. To improve this, we propose to first estimate the rsHRF of all the voxels from fMRI input data, and then average these values over the regions used in TVB. These values can consequently be utilized for simulating BOLD signal in a subject- and region-specific way. This could be a valuable addition to TVB.

Brian is a spiking neural networks simulator, which provides desirable syntactic sugar and flexibility to allow a wide variety of models without compromising rapidity. With a plethora of different sets of governing equations, neuronal models with complex biophysical properties, synapses with plasticity and parameters, a mechanism to derive generalized platform-independent model description becomes inevitable. Further, the model description mechanism helps in easy access and reproduction of the models, and thereby exasperating reference to the platform-specific source code can be avoided. Currently, Brian uses `brian2tools.nmlexport` package to elegantly export Brian models to NeuroML, with minimal changes. However, this model description mechanism is only confined to the NeuroML/LEMS framework, and cannot be extended easily to various other generalized model descriptors and human-readable formats. Also, the `nmlexport` package is currently limited to specific components and incompatible with key components like Synapses, Network input, etc. Therefore, the project proposes an idea to create a generalized basic framework, which can coherently describe Brian models in a standard format. The standard format shall act as the foundation for exporting Brian models to NeuroML/LEMS format, human-readable like LaTeX typesetting, ModelView description and also shall be flexible enough to extend with various other model descriptors or frameworks. The proposed idea would substantially enhance the interfacing functionality of Brian models with other standard model descriptors and thereby helps numerous users and research communities.