The discovery of Blood Oxygen Level Dependent (BOLD) response in 1990 has enabled the Neuroscientists to make rapid progress in understanding the human brain function. The functional MRI imaging modality, which is based on the BOLD response, provides a non-invasive method to (indirectly) measure the activity inside the brain. Using fMRI scientists can now begin to study the various sub-organs inside the human brain, their functions and interactions while engaging in a variety of different tasks. Most of the fMRI experiments are based on paradigm that is restricted by its data analysis technique called the General Linear Model. Under this paradigm, the subject inside an fMRI scanner instructed to perform different tasks under different conditions. The fMRI data hence collected is correlated with the experimental condition to find the regions of brain activated in response to different experimental and task conditions. 

Newer data analysis techniques have enabled more complex experimental paradigms using which human brain can be studied in a much broader range of experimental conditions. In this talk, I will review some of this work and describe how techniques in Machine Learning, Statistics and Computer Science can be used to advance our understanding of the human brain. I will briefly describe some of our work on how fMRI data can be used to decipher consistent patterns of "information flow".  I will conclude with few comments on how it is might be possible to integrate the modern science and technology with the ancient Indian wisdom to attempt to get the best of both the worlds.

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