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.

ImageJ is an open source Java image processing and analysis library used extensively in biomedical sciences. Active Segmentation is a plugin providing user interface to scientists, allowing them to use Machine Learning algorithms for segmentation and classification tasks. The aim of the Active Segmentation is to provide researchers an extensible toolbox enabling them to select custom filters and machine learning algorithms for their research. Moreover, it provides the support for scientists without strictly technical background (does not require programming skills to apply above mentioned tools). The idea behind this project is to extend active segmentation with modern deep learning methods for image analysis using Deeplearning4j library.

Conversion of large scale cortical models into PyNN/NeuroML involves the conversion of published large scale network models into open, simulator independent and testing them across multiple simulator implementations. In the previous edition of GSOC the large scale network model for the macaque cortex (https://github.com/OpenSourceBrain/MejiasEtAl2016), proposed by Mejias et. al, was successfully converted. In this model, each cortical area is composed of an inferior and a superior layer and the dynamical behavior inside each laminar subcircuit is described by a non-linear firing rate model of Wilson-Cowan type which represents the mean activities of a population of excitatory neurons and a population of inhibitory neurons. A natural extension of this model was proposed in a paper by Joglekar et. al (https://www.ncbi.nlm.nih.gov/pubmed/29576389). Instead of using non-linear firing rate models, the cortical area was simulated as a spiking neuronal network. This was extremely useful to investigate the propagation of activity in the synchronous and asynchronous regime of the network. Although this study was published in 2018, the code is not available in ModelDB. However, it was written in Brian simulator and can be kindly provided by the authors. My goal in this project is to convert this model to PyNN allowing the simulation in several simulators. Besides that, with the firing rate large scale model previously converted it will make possible the full reproducibility of the results published in the paper (https://www.ncbi.nlm.nih.gov/pubmed/29576389). As a secondary goal in this project, I would like to convert the model proposed by Demirtas et. al. (https://doi.org/10.1016/j.neuron.2019.01.017)  that is a large-scale circuit model of human cortex incorporating regional heterogeneity in microcircuit properties inferred from magnetic resonance imaging (MRI) for parametrization across the cortical hierarchy and fitting models to resting-state functional connectivity.

The project aims to provide a robust mechanism for cell tracking using 2D raw image objects. Through the use of Mean Square Distance method the potential object displacements will be calculated. Another set of images will be fed as time lapse protocol. Trajectory estimate will be denoised using different modern filters such as IMM,Weiner and Multiple Channel Linear Correlation Filter.

The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2018. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation. ImageJ is a public domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins.

Cell Tracking has gained importance in recent times due to the growing extensive research in Biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by cellular experiments, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of cell objects in time-lapse microscopy images.

Purpose-The project offers an amalgamation of programming and life science. I had been constantly on the hunt for an opportunity to work in this area and I hope I will be able to deliver the best from my side. Moreover it would be a great learning experience for me and open up opportunities in this domain for further exploration.Its a win-win situation for me.I hope that my interest in this domain will help in realising an intuitive dimension to this project. 

GeNN is a GPU-enhanced Neuronal Network simulation package in C++, that combines the ease of code generation, primarily for the purpose of setting up the parameters of the simulation through a model definition, with the flexibility of user-defined code, to actually run the simulation and record results. While there are several SNN libraries available, by combining GeNN and standard machine learning packages, it is possible to simulate SNNs and ANNs on the same hardware, thereby providing well-founded comparisons of model performance. This project will primarily use Python (PyGeNN, TensorFlow, Jupyter for tutorials), along with C++ (GeNN).

Time series analysis methods are regularly being developed and we don’t have any resources at present that compare the newly developed method with the methods that already exist to help the user determine the similarity between new and pre-existing methods. In this project, we are going to develop a web-based system that takes an analysis method as python code from a user, computes it with a diverse time-series dataset, and analyzes the relation of the newly developed method with the pre-existing one.

Psychiatric disorders are diagnosed based on symptom scores from clinical interviews, there are no existing gold standards that can be used for definitive validation. Brain functional neuroimaging techniques including functional magnetic resonance imaging (fMRI), Positron Emission Tomography (PET), and Electroencephalography (EEG) have become important tools in investigating brain disease. Thus, the analysis of functional neuroimaging data can be used to characterize brain function abnormality.

Recently the researchers have formulated different ways to analyze time-series data. Some sophisticated and some simple. Although the simple methods work quite well, there is a need to apply the complex methodologies for analysis. In the paper published by Fulcher et. al, 2017 ( hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction ), showed that numerous features (~7700) can be extracted from a time-series using the proposed hctsa tool. But using the hctsa features for analysis can be computationally expensive and requires closed Matlab licenses to run, limiting widespread adoption for medical and research applications. These features can be reduced to a lesser number of non-redundant features to represent the time-series, using the catch22 feature reduction approach ( catch22: CAnonical Time-series CHaracteristics ). The reduction is performed with minimal loss of the classification accuracy. In this method, the performance of each feature was evaluated on a set of datasets that are very different to NeuroImaging data. Thus, the current work will validate the proposed approach with NeuroImaging data.

This project is particularly important for measurements of brain dynamics of a patient to their disease diagnosis. It further reduces complexities by distilling a large literature on time series analysis into a small subset. It selects only the significant features to represent the time series for analysis, with minimal loss in classification accuracy. The optimized and efficiently coded features will be made into a package for the community to use it. This open source code will let other researchers find the underlying hidden features of NeuroImaging time series and utilize it in their work. The availability and ease of computing the features opens up the application to several areas.

The goal of this Working Group is to develop a set of specifications and tools that would allow standardization of a directory structure for experimental data, recorded with animal models in neuroscience. It will capitalize on the success of BIDS for human neuroimaging data, while retaining the specificities of data sets obtained in animal models. This standardized data structure will facilitate reproducible research and data sharing following the FAIR principles.

The Working Group is composed of electrophysiology representatives from the INCF network, Human Brain Project (HBP), Neurodata Without Borders (NWB) Core Development Team, and Stimulating Peripheral Activity to Relieve Conditions (SPARC), sharing use cases from the respective projects.The initial use cases are from intracellular electrophysiology, with good coverage over most typical ICEPhys protocols. This schema is also useful for other 1D signals over time that are used as stimuli. In addition, the ontology defines a specification for parameterizing the stimulus templates. This is a strategy for stimulus description that should eventually be easily extendable to 2D (i.e. visual) stimuli, which is another large class of stimuli that is common in NWB.