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project1.html
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---
layout: default
title: Shailaja Akella
---
<div class = "blurb">
<h2 style = "text-align: center"> <strong> CURRENT RESEARCH </strong> </h2>
<div class="blurb">
<img src="Picture1.jpg" alt="neuromodulations">
<img src="Picture2.jpg" alt="Model">
<p style = "text-align:justify; text-justify: inter-word; font-size: 15px"> Field potentials recorded from the brain (invasive or non - invasively) are non - stationary signals that represent the dynamic interactions between local neural populations.
Synchronized interactions between populations then manifest as oscillatory bursts in the field potentials. These bursts are called neuromodulations and have been recognized
by clinicians as bursts of rhythmic activity that wax and wane when eyeballing the signal. Further, they are temporally sparse and transient. On the other hand,
unsynchronized interactions contribute to background activity that is featureless and unstructured. </p>
<p style = "text-align:justify; text-justify: inter-word; font-size: 15px"> Neuromodulatory patterns command much interest because of their role as potential biological markers in the early detection of neurological diseases and as pneural
patterns carrying cognitive content. While classical signal processing techniques attempt to identify these oscillatory bursts using spectral representations of the signal, they are limited by the inherent time - frequency trade-off.
Moreover, such analyses demand more specialized methods that incorporate the neurophysiology of signal generation and high-time resolution. In this project, we aim to adress these issues to classify and characterize neuromodulations
from field potentials. We do so in an unsupervised manner using information theoretic measures, sparse-coding techniques and point process modeling. </p>
<p style = "text-align: center"><a style = "text-align: center; color: #737373; font-size:18px; border:4px; border-style:solid; border-color:#4d94ff;
padding: 0.5em" href="https://pubmed.ncbi.nlm.nih.gov/33348332/"> <strong> FULL PAPER </strong> </a></p>
<p> <a style = "text-align: center; color:#737373; font-size: 15px" href = "projects.html"> <u> Close tab </u> </a> </p>
</div><!-- /.blurb -->
<hr>
<div class="row">
<div class = "column">
<img src="Picture6.jpg" alt="Model" style="width:500px;height:250px">
</div>
<div class = "column">
<h4 style = "text-align: center; color:#737373"> <strong> NEUROMODULATIONS IN WORKING MEMORY </strong> </h4>
<p style = "font-size:15px"> Working memory (WM) is a cognitive process by which salient sensory information is held temporarily during task execution.
We were insterested in studying the role of neuromodulations as encoders of cognitive content. We present our results on field potentials recorded from the prelimbic and secondary
motor cortices of 3 rats while performing a WM task.
<p style = "font-size:15px"><u> <a style = "color:#737373" href = "/project2.html"> Read more.. </a> </u></p>
</div>
</div>
<hr>
<div class="row">
<div class="column">
<img src="Exemplars (1).jpg" alt="Model">
</div>
<div class="column">
<h4 style = "text-align: center; color:#737373"><strong> AUTOMATIC SLEEP SPINDLE DETECTION </strong> </h4>
<p style = "font-size:15px"> Stage II of Non - Rapid Eye Movement (NREM) sleep is marked by stereotypical high frequency spike clusters known as sleep spindles, observable in the cortical electroencephalograms.
The identification of sleep spindles is usually carried out by visual inspection of signals obtained from polysomnography studies which both tedious and prone to errors.
In this study, we demonstrate the applicability of our model as a generalized sleep spindle detector. </p>
<p style = "font-size:15px"><u> <a style = "color:#737373" href = "/project3.html"> Read more.. </a> </u></p>
</div>
</div>
<hr>
<div class="blurb">
<h4 style = "text-align: center; color:#737373" > <a style = "text-align: center; color:#737373" href = "/project4.html"> <u> <strong> FUNCTIONAL CONNECTIVITY ANALYSIS</strong> </a> </u> </h4>
</div><!-- /.blurb -->
</div><!-- /.blurb -->