218 research outputs found
Cortical representations of a class of subjective contours
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1996.Includes bibliographical references.by Bhavin R. Sheth.Ph.D
Temporal Modulation of Spatial Borders in Rat Barrel Cortex
No full text Sheth, Bhavin R., Christopher I. Moore, and Mriganka Sur. Temporal modulation of spatial borders in rat barrel cortex. J. Neurophysiol. 79: 464–470, 1998. We examined the effects of varying vibrissa stimulation frequency on intrinsic signal and neuronal responses in rat barrel cortex. Optical imaging of intrinsic signals demonstrated that the region of cortex activated by deflection of a single vibrissa at 1 Hz is more diffuse and more widespread than the territory activated at 5 or 10 Hz. With the use of two different paradigms, constant time of stimulation and constant number of vibrissa deflections, we showed that the optically imaged spread of activity is more discrete at higher stimulation frequencies. We combined optical imaging with multiple electrode recording and confirmed that the neuronal response to individual vibrissa stimulation at the optically imaged center of activity is greater than the response away from the imaged center. Consistent with the imaging data, these recordings also showed no response to a second vibrissa deflection at 5 Hz at a peripheral recording site, though there was a significant response to a second vibrissa deflection at 1 Hz at the same peripheral site. These findings demonstrate that vibrissa stimulation at higher frequencies leads to more focused physiological responses in cortex. Thus the spread of activation in rat barrel cortex is modulated in a dynamic fashion by the frequency of vibrissa stimulation. </jats:p
sj-docx-1-jet-10.1177_15266028221134887 – Supplemental material for Impact of Chronic Kidney Disease on In-Hospital Outcomes of Hospitalizations With Acute Limb Ischemia Undergoing Endovascular Therapy
No full textSupplemental material, sj-docx-1-jet-10.1177_15266028221134887 for Impact of Chronic Kidney Disease on In-Hospital Outcomes of Hospitalizations With Acute Limb Ischemia Undergoing Endovascular Therapy by Harsh P. Patel, Dean Decter, Samarthkumar Thakkar, Mahesh Anantha-Narayanan, Ashish Kumar, Aakash R Sheth, Salman Zahid, Bhavin A. Patel, Toralben Patel, Hiteshkumar Devani, Vrushali Shah, Preet Mayank Doshi, Smit Patel, Mariam Shariff, Devina Adalja, Saraschandra Vallabhajosyula and Rajkumar Doshi in Journal of Endovascular Therapy</p
Identifying Neural Signatures of Satisfaction of Sleep Need
No full textSleep consists of multiple stages: S1, S2, slow-wave-sleep (SWS), and rapid eye movement (REM). Do these stages serve complementary, different functions, or the same function but with different magnitudes? During non-REM (S1àS2àSWS), the brain signal progressively slows and becomes larger: Slow-wave oscillation (SWO: 0-1Hz) and signal in delta band (1-4Hz) in non-REM are the most unlike wake and are more prominent following sleep deprivation and during SWS [1,2]. Low-frequency power is thought to be an indicator of sleep need being met. Our studies, based on overnight, scored sleep data and brain signal from 5000+ subjects (8000+ nights) [3] suggest low-frequency power in S2 and in SWS and SWS duration, but not S2 duration, indicate sleep need being met: increased delta energy in S2 means less SWS duration (rs=-0.9145, p<<.00001), but not less S2 duration, i.e. more efficient sleepers can fulfill sleep need with increased delta power without getting to SWS. In fact, increased S2 delta power means shorter S2 durations, i.e. with greater sleep need, an individual will transition from S2 into SWS quicker. Increased delta power in SWS means shorter S1,2 durations suggesting that sleep need is not met by increasing S1,2 durations. Within-subject analyses for subjects for whom a second night of data was recorded corroborate all our results, affirming our conclusion that delta power across non-REM sleep and SWS duration are indicators of sleep need. Thus, S1,2 appear to be transitional stages for satisfying sleep need and may not have a distinct function from SWS after all.Honors CollegeElectrical and Computer Engineering, Department o
How to Increase Confidence Without Improving Performance
No full textSo far, we have determined and adjusted experiment design and written the code for the experiment. We are currently collecting data from test subjects and working on creating related experiments for the future. The experiment code was written in MATLAB, using PsychToolbox 3 and a toolbox from CARLsim 3.0 that we modified. We are already planning similar experiments for the project, including testing with a modified form of volatility, a sleep study, and directional bias. This research should allow us to better understand the perceptual decision-making process in humans.Electrical and Computer Engineering, Department ofHonors Colleg
Reading Your Mind Through Your Eyes: Using Eye Scan Patterns and Machine Learning to Predict Number Choice
No full textEye tracking technology measures eye movements in real time. Studies have shown that eye movement patterns can express spatial cognitive thoughts, confirming the existence of the eye-cognition link. The purpose of this experiment is to determine a correlation between spatial cognition and number processing. If a correlation exists, features suggesting this correlation will be identified to predict a subject’s response using machine learning. Fifty subjects were asked to look at a screen and respond to the following auditory prompt: “Think of a number x/y/z and say it out loud”. These intervals were named Number Line, Pre-prompt, Mid-prompt, and Post-prompt. A predicted model was created by training a Random Forest (RF) Algorithm. Results indicate a strong correlation between saccade vector and participants’ response, suggesting subjects made saccades toward the direction of response in mind. A strong correlation was also identified between Fixation X and participants’ response as these fixations were made in relative locations of the given numbers on a number line. Test accuracy of 90.5% for Trial 1 was obtained with 50 trees. All ten trials resulted in a mean accuracy of 89%. Our future plans involve determining the existence of a vertical component of spatial cognition.Honors CollegeElectrical and Computer Engineering, Department o
An Electrical Circuit Model of Circle of Willis to Predict Stroke
No full textIs it possible to predict which individuals are more prone to stroke years before they actually get one? An affirmative answer to this question has big implications for public health. The Circle of Willis (CoW), a ring of arteries that distributes blood flow to the two hemispheres of the brain, is only completely present in ~50% of all people. We hypothesized that the differences in the variations explain the differences in the relative probability of individuals suffering a blockage owing to a stroke. To address this, it is necessary to model these variations and to introduce blockages in these modeled variations and observe how it affects blood flow, temporarily or permanently. This research modeled the CoW using different parameters of the biological structure with electrical components. Biological data from empirical, clinical studies constrained our circuit model. High-Performance-Computing was utilized to match electrical current ratios to blood flow ratios obtained from empirical studies to compute biologically plausible circuit models where the values of the circuit components match the anatomical values. Approximately 215 million potential solution models were computed and compared with clinical data, of which 3,000+ models with flow rates matching clinical data for complete and two incomplete variations of the CoW were found. Then the circuit model was simplified and the changes in blood flow across variations were analyzed. We anticipate our solution models to be the starting point for in-depth analyses of the variations or blockages in the CoW for which there are no clinical data readily available.Electrical and Computer Engineering, Department ofHonors Colleg
Exploring the Role of Heritability in Disease
No full textThe heritability (h2) of a disease is the proportion of phenotypic variance that is due to genetics. The goal of my research is to determine if a trend exists between the heritability of a particular disease and its earliest age of onset. Common genetic wisdom suggests that diseases with greater heritability will have an earlier age of onset, but this has not been heavily tested yet. We collected heritability values for diseases based on ACE model estimates because this divides the total phenotypic variance into additive genetic effects, shared environmental effects, and unique environmental effects. We found values for 97 diseases and categorized them based on ICD-11 classifications before performing a Spearman correlation. Our results suggest that common genetic wisdom might be incorrect, but these results were inconclusive because we have only scratched the surface of possible diseases to test. We will add to our list of diseases before we make any definitive claims about disease onset and heritability. Gathering more data will also allow us to determine whether certain organ systems are genetically predisposed to disease. Inspired by heritability and the ACE model, we also plan to incorporate the environmental components and search for trends with age of onset, prevalence, incidence, and NIH funding allocated for a particular disease. Changes in genetic and environmental proportions and incidence and prevalence could determine the effectiveness of current public health interventions and research studies and potentially motivate reason to change the way we combat disease.Electrical and Computer Engineering, Department ofHonors Colleg
Circle of Willis: Accessing the Possibility of Ruptured Aneurysm
No full textElectrical and Computer Engineering, Department ofHonors Colleg
How is Rapid Eye Movement (REM) Sleep Related to Deep Slow-Wave Sleep (SWS) in Humans?
No full textSleep consists of two broad phases: rapid eye movement (REM), and non-REM sleep, and the deepest stage of non-REM sleep is slow-wave sleep (SWS). SWS and REM sleep are believed to be related, but exactly how is not known. As SWS has been studied and believed to be essential to brain restoration, REM sleep could hold an important function that needs to be explored. This research analyzed the electroencephalogram data of over 5,000 subjects to find the relationship between various parameters within REM sleep and other stages of sleep. The data was recorded by Sleep Heart Health Study and analyzed by MATLAB. The main parameters which were studied are power spectra, sleep time, percentage of total sleep time, REM density, and nasal air temperature. Using Borbely study as the reference, parameters were paired and taken Spearman correlation coefficients for all subjects on first visit, only healthy subjects on first visit, and all subjects that continued follow-up visit (1). Instead of getting a contrasting relationship between SWS and REM sleep as suggested in previous studies, the result shows a complementary relationship for SWS and REM sleep.Electrical and Computer Engineering, Department ofHonors Colleg
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