Neural Signal Processing Algorithms

Recent technological and experimental advances in the capabilities to record signals from neural systems have led to an unprecedented increase in the types and volume of data collected in neuroscience experiments and hence, in the need for appropriate techniques to analyze them. Therefore, using combinations of likelihood, Bayesian, state-space, time-series and point process approaches, a primary focus of the research in my laboratory is the development of statistical methods and signal-processing algorithms for neuroscience data analysis.

We have used our methods to:

• characterize how hippocampal neurons represent spatial information in their ensemble firing patterns.
• analyze formation of spatial receptive fields in the hippocampus during learning of novel environments.
• relate changes in hippocampal neural activity to changes in performance during procedural learning.
• improve signal extraction from fMR imaging time-series.
• characterize the spiking properties of neurons in primary motor cortex.
• localize dynamically sources of neural activity in the brain from EEG and MEG recordings made during cognitive, motor and somatosensory tasks.
• measure the period of the circadian pacemaker (human biological clock) and its sensitivity to light.
• characterize the dynamics of human heart beats in physiological and pathological states.

Understanding General Anesthesia

General anesthesia is a neurophysiological state in which a patient is rendered unconscious, insensitive to pain, amnestic, and immobile, while being maintained physiologically stable. General anesthesia has been administered in the U.S. for nearly 160 years and currently, more than 50,000 people receive anesthesia daily in this country for surgery alone. Still, the mechanism by which an anesthetic drug induces general anesthesia remains a medical mystery. A new research direction in my laboratory is to use a systems neuroscience approach to study how the state of general anesthesia is induced and maintained. To do so, we are using fMRI, EEG, neurophysiological recordings, microdialysis methods and mathematical modeling in interdisciplinary collaborations with investigators in BCS, the MIT/Harvard Division of Health Science and Technology, Massachusetts General Hospital and Boston University. The long-term goal of this research is to establish a neurophysiological definition of anesthesia, safer, site-specific anesthetic drugs and to develop better neurophysiologically-based methods for measuring depth of anesthesia.

2014

Halassa M, Chen Z, Wimmer RD, Brunetti PM, Zhao S, Zikopoulos B, Wang F, Brown EN, Wilson MA. State-dependent architecture of thalamic reticular sub-networks. Cell. In Press. 2014.

Akeju O, Westover MB, Pavone KJ, Sampson A, Hartnack K, Brown EN, Purdon PL. Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence. Anesthesiology. In Press. 2014.

Wu W, Chen Z, Gao X, Li Y, Brown EN, Gao S. Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. vol.PP, no.99, pp.1,1. doi: 10.1109/TPAMI.2014.2330598.

Mukamel E, Pirondini E, Babadi B,  Wong KF, Pierce E, Harrell PG, Walsh J, Salazar-Gomez A, Cash S, Eskandar E, Weiner V, Brown EN, Purdon PL. A transition in brain state during propofol induced unconsciousness. Journal of Neuroscience. 2014 Jan 15;34(3):839-45. doi: 10.1523/JNEUROSCI.5813-12.2014. PMID: 24431442.

Faghih R, Klerman EB, Adler GK, Dahleh MA, Brown EN. Deconvolution of serum cortisol levels by using compressed sensing. PLoS One. 2014 Jan 28;9(1):e85204. doi: 10.1371/journal.pone.0085204. eCollection 2014. PMID: 24489656. PMCID: PMC3904842.

Babadi B, Ba D, Purdon PL, and Brown EN. Convergence and stability of a class of iteratively re-weighted least squares algorithms for sparse signal recovery in the presence of noise. IEEE Transactions on Signal Processing. vol.62, no.1, pp.183,195, Jan.1, 2014. doi: 10.1109/TSP.2013.2287685.

Solt K, Van Dort CJ, Chemali JJ, Taylor N, Brown EN. Electrical stimulation of the ventral tegmental area induces reanimation from general anesthesia. Anesthesiology. 2014 Jan 6. [Epub ahead of print]. PMID: 24398816.

Liu M, Lewis L, Shi R, Brown EN, Xu W. Differential requirement for NMDAR activity in SAP97β-mediated regulation of the number and strength of glutamatergic AMPAR-containing synapses. Journal of Neurophysiology. 2014 Feb;111(3):648-58. doi: 10.1152/jn.00262.2013. Epub 2013 Nov 13. PMID: 24225540. PMCID: PMC3921414.

Ba D, Temereanca S, Brown EN. Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models. Frontiers in Computational Neuroscience. 2014 Feb 10;8:6. doi: 10.3389/fncom.2014.00006. eCollection 2014. PMID: 24575001. PMCID: PMC3918645.

Babadi B, Obregon-Henao G, Lamus C, Hämäläinen, MS, Brown EN, Purdon PL. A Subspace pursuit-based iterative greedy hierarchical solution to the neuromagnetic inverse problem. NeuroImage. 2014 Feb 15;87:427-43. doi: 10.1016/j.neuroimage.2013.09.008. Epub 2013 Sep 18. PMID: 24055554. PMCID: PMC3946905.

2013

Citi L, Ba D, Brown EN, Barbieri B. Likelihood methods for point processes with refractoriness. Neural Computation. 2014 Feb;26(2):237-63. doi: 10.1162/NECO_a_00548. Epub 2013 Nov 8. PMID: 24206384.

Shanechi MM, Chemali JJ, Liberman M, Solt K, Brown EN (2013) A brain-machine interface for control of medically-induced coma. PLoS Comput Biol. 9(10): 2013 Oct;9(10):e1003284. doi: 10.1371/journal.pcbi.1003284. Epub 2013 Oct 31. PMID: 24204231.

Chemali J, Solt K, Purdon PL, Ching S, Brown EN. Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression. Journal of Neural Engineering. 2013, Oct;10(5):056017. doi: 10.1088/1741-2560/10/5/056017. Epub 2013 Sep 10. PMID: 24018288.