Prof. Neelam Sinha

Associate Professor

Neelam Sinha’s interests lie in applying machine learning techniques for multi-modal neuroimaging.

She obtained PhD at IISc for her work on strategies for rapid MR imaging, in 2008.  She worked in the MR Imaging group at GE Healthcare for a year, and then joined IIIT-Bangalore. At IIIT-Bangalore, her research focus was on problems in healthcare, which included surgical video, fundal image and neuro-data analysis. She executed a DST-funded project on resting state fMRI for brain characterization. She has worked on problems such as age-estimation using Diffusion MR, analysis of atypical PD variants, chromosomal mutation detection in Low Grade Glioma using structural MR images, in collaboration with NIMHANS. As part of an industry-sponsored project, she has worked on visual functioning networks in the brain, utilizing fMRI time series. She was part of a state-funded centre (MINRO) with projects on EEG analysis for quantifying abstract notions, such as creativity. She joined CBR in July 2023.

Multi-modal Neuroimaging using Machine Learning

Neuroimaging has vastly advanced our understanding of the brain. However, one is so sure that there is so much more to be harnessed. Many more patterns to uncover among the structural MR images, cognitive gaps to be identified from functional MR volume time series. How best can one study these information-rich sources? Deep learning approaches have been hugely successful on natural images-sounds-text. However, unless one includes robustness, interpretability and risk evaluation, deep learning approaches on biomedical data might be of very limited use. In our exploration, we will be looking at these critical aspects. We will also try to answer how best can one put together findings from structural, functional, diffusion MR data to infer a holistic view. Machine learning approaches to exploit multi-modal Neuroimaging could take us a long way!

Selected Publications:

  1. “Analysis of Mild Cognitive Impairment utilizing covariance matrices of brain regions”, Ammu R and Neelam Sinha, accepted for publication in 33rd IEEE International workshop on Machine Learning for signal processing (MLSP 2023)
  2. “3D segmentation of unruptured intra-cranial aneurysms using task-specific transfer learning and pure convnets”, Snigdha Agarwal and Neelam Sinha accepted for publication in 33rd IEEE International workshop on Machine Learning for signal processing (MLSP 2023)
  3. “Measuring deviation from stochasticity in time series using autoencoder-based time-invariant representation: Application to black hole data”, Chaka SaiPradeep, Neelam Sinha and Banibrata Mukhopadhyay, in proceedings of 48th IEEE Conference on Acoustics, speech and signal processing (ICASSP 2023)
  4. Simultaneous segmentation of multiple structures in fundal images using multi-tasking deep neural networks”, Sunil Kumar Vengalil, Bharath K and Neelam Sinha, in Frontiers of Signal Processing 2:71, Dec 2022
  5. “An investigation of the multidimensional (1D vs 2D vs 3D) analyses of EEG signals using traditional methods and deep-learning based methods”, Shah D, Gopika K. G and Neelam Sinha, in Frontiers of Signal Processing 2:93760, July 2022
  6. “Analysis of single channel electroencephalographic signals for visual creativity: A pilot study”, by Gopika Gopan K, SVRA Reddy, Madhav Rao and Neelam Sinha in Biomedical Signal Processing and Control, Volume 75, May 2022, 103542.
  7. “Determining chromosomal arms 1p/19q co-deletion status in low graded glioma by cross correlation-periodogram pattern analysis”, by Debanjali Bhattacharya, Neelam Sinha & Jitender Saini, in Scientific Reports-Nature, Vol. 11(23866), 2021
  8. “EEG Analysis of Mathematical Cognitive Function and Startle Response using Single Channel Electrode”, by Gopika Gopan K, SVRA Reddy, Kumaresh Krishnan, Madhav Rao and Neelam Sinha in CSI Transactions on ICT 8 (4), 367-376, Dec 2020
  9. “A New Statistical Framework for Corpus Callosum Sub-region Characterization Based on LBP Texture in Parkinsonian Disorders”, by Debanjali Bhattacharya, Neelam Sinha, Jitender Saini, Shweta Prasad, Pramod Pal, and Sandhya M, Frontiers in Neuroscience, Vol.14, Article 477, pp:1-13, May 2020
  10. “Sleep EEG analysis utilizing inter-channel covariance matrices”, by Gopika Gopan K, S. S Prabhu and Neelam Sinha, in Biocybernetics and Biomedical Engineering 40 (1), 527-545, Jan 2020

For list of all publications: Google scholar

Centre for Brain Research
Indian Institute of Science Campus
CV Raman Avenue
Bangalore 560012. India.

Email: neelamsinha[at]
TelephoneOffice +91 80 2293 3588