Publications by authors named "Akshay Arora"

5 Publications

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Stimulation of the Posterior Cingulate Cortex Impairs Episodic Memory Encoding.

J Neurosci 2019 09 29;39(36):7173-7182. Epub 2019 Jul 29.

Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas 75390, and.

Neuroimaging experiments implicate the posterior cingulate cortex (PCC) in episodic memory processing, making it a potential target for responsive neuromodulation strategies outside of the hippocampal network. However, causal evidence for the role that PCC plays in memory encoding is lacking. In human female and male participants ( = 17) undergoing seizure mapping, we investigated functional properties of the PCC using deep brain stimulation (DBS) and stereotactic electroencephalography. We used a verbal free recall paradigm in which the PCC was stimulated during presentation of half of the study lists, whereas no stimulation was applied during presentation of the remaining lists. We investigated whether stimulation affected memory and modulated hippocampal activity. Results revealed four main findings. First, stimulation during episodic memory encoding impaired subsequent free recall, predominantly for items presented early in the study lists. Second, PCC stimulation increased hippocampal gamma-band power. Third, stimulation-induced hippocampal gamma power predicted the magnitude of memory impairment. Fourth, functional connectivity between the hippocampus and PCC predicted the strength of the stimulation effect on memory. Our findings offer causal evidence implicating the PCC in episodic memory encoding. Importantly, the results indicate that stimulation targeted outside of the temporal lobe can modulate hippocampal activity and impact behavior. Furthermore, measures of connectivity between brain regions within a functional network can be informative in predicting behavioral effects of stimulation. Our findings have significant implications for developing therapies to treat memory disorders and cognitive impairment using DBS. Cognitive impairment and memory loss are critical public health challenges. Deep brain stimulation (DBS) is a promising tool for developing strategies to ameliorate memory disorders by targeting brain regions involved in mnemonic processing. Using DBS, our study sheds light on the lesser-known role of the posterior cingulate cortex (PCC) in memory encoding. Stimulating the PCC during encoding impairs subsequent recall memory. The degree of impairment is predicted by stimulation-induced hippocampal gamma oscillations and functional connectivity between PCC and hippocampus. Our findings provide the first causal evidence implicating PCC in memory encoding and highlight the PCC as a favorable target for neuromodulation strategies using connectivity measures to predict stimulation effects. This has significant implications for developing therapies for memory diseases.
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http://dx.doi.org/10.1523/JNEUROSCI.0698-19.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733540PMC
September 2019

Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.

J Neural Eng 2018 12 13;15(6):066028. Epub 2018 Sep 13.

Department of Neurological Surgery, University of Texas-Southwestern Medical Center, Dallas, TX 75390, United States of America.

Objective: We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep learning) for the problem of predicting successful episodic memory encoding using direct brain recordings obtained from human stereo EEG subjects. We also sought to test the impact of applying t-distributed stochastic neighbor embedding (tSNE) for unsupervised dimensionality reduction, as well as testing the effect of reducing input features to a core set of memory relevant brain areas. This work builds upon published efforts to develop a closed-loop stimulation device to improve memory performance.

Approach: We used a unique data set consisting of 30 stereo EEG patients with electrodes implanted into a core set of five common brain regions (along with other areas) who performed the free recall episodic memory task as brain activity was recorded. Using three different machine learning strategies, we trained classifiers to predict successful versus unsuccessful memory encoding and compared the difference in classifier performance (as measured by the AUC) at the subject level and in aggregate across modalities. We report the impact of feature reduction on the classifiers, including reducing the number of input brain regions, frequency bands, and the impact of tSNE.

Results: Deep learning classifiers outperformed both support vector machines (SVM) and logistic regression (LR). A priori selection of core brain regions also improved classifier performance for LR and SVM models, especially when combined with tSNE.

Significance: We report for the first time a direct comparison among traditional and deep learning methods of binary classification to the problem of predicting successful memory encoding using human brain electrophysiological data. Our findings will inform the design of brain machine interface devices to affect memory processing.
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http://dx.doi.org/10.1088/1741-2552/aae131DOI Listing
December 2018

Off-flavor precursors in soy protein isolate and novel strategies for their removal.

Annu Rev Food Sci Technol 2013 3;4:327-46. Epub 2013 Jan 3.

Department of Food Science, University of Wisconsin, Madison, WI, USA.

Off-flavors remain a major hurdle in expanding the use of soy protein isolate (SPI) in mainstream food applications. The complexity in solving this problem arises from the presence of protein-bound precursors in SPI. Among the most predominant sources of off-flavors in SPI is the residual amount of phospholipids that contain polyunsaturated fatty acids (PUFAs). Autoxidation of PUFAs generates several classes of volatile compounds that contribute to the beany, grassy, or green odor of SPI. In addition, several polyphenolic compounds, such as isoflavones, saponins, phenolic acids, etc., impart bitter and astringent tastes to SPI. Traditional methods for removing protein-bound precursors from SPI and their limitations are reviewed. The most notable trade-off of conventional methods is the loss of protein functionality to some degree. Therefore, pursuit of gentler treatments to overcome SPI off-flavor has been the focus of industry and academia alike. Novel approaches that employ β-cyclodextrin to remove both SPI-bound precursors and volatile compounds are described.
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http://dx.doi.org/10.1146/annurev-food-030212-182650DOI Listing
July 2013

Removal of soy protein-bound phospholipids by a combination of sonication, β-cyclodextrin, and phospholipase A2 treatments.

Food Chem 2011 Aug 26;127(3):1007-13. Epub 2011 Jan 26.

Department of Food Science, University of Wisconsin-Madison, Madison, WI 53706, USA. Electronic address:

One of the contributing factors to generation of off-flavours in soy protein isolate (SPI) during storage is autoxidation of residual amounts of phospholipids present in SPI. Thus, removal of phospholipids from SPI is a likely first step to improve its flavour stability and enhanced utilisation of SPI in food products. We describe a β-cyclodextrin-based (βCD) process to remove protein-bound phospholipids and free fatty acids in SPI. Treating SPI solution (8%) with 10mM βCD alone at pH 8.0 decreased the phospholipid content of SPI by about 36%. A greater than 99% removal of phospholipids and free fatty acids was achieved by using a combination of treatments involving sonication of the SPI solution for 5min at 50°C followed by treatment with phospholipase A2 and βCD. SPI prepared by this method was white in colour. The results presented here offer a process for removing residual off-flavour causing phospholipids from soy protein.
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http://dx.doi.org/10.1016/j.foodchem.2011.01.073DOI Listing
August 2011

Rheology and stability of acidified food emulsions treated with high pressure.

J Agric Food Chem 2003 Apr;51(9):2591-6

Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus, OH 43210, USA.

The stability and rheology of acidified model oil-in-water emulsions (pH 3.6 +/- 0.1) were evaluated before and after high-pressure treatments. Varying concentrations of canola oil (0-50% w/w), whey protein isolate, polysorbate 60, soy lecithin (0.1-1.5% w/w each), and xanthan (0.0-0.2% w/w) were chosen. Exposure to high pressures (up to 800 MPa for 5 min at 30 degrees C) did not significantly affect the equivalent surface mean diameter D[3,2], flow behavior, and viscoelasticity of the whey protein isolate and polysorbate 60-stabilized emulsions. Pressure treatments had negligible effects on emulsion stability in these systems, except when xanthan (0.2% w/w) was present in which pressure improved the stability of polysorbate 60-stabilized emulsions. Soy lecithin-stabilized emulsions had larger mean particles sizes and lower emulsion volume indices than the others, indicating potential instability, and application of pressure further destabilized these emulsions.
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http://dx.doi.org/10.1021/jf0260141DOI Listing
April 2003