INTEL Semi-finalist Emily Prentiss, 18-year-old senior at Ossining HS

Tuesday, January 17, 2012
by Christine Yeres

Editor’s Note: After publishing Two New Castle high school seniors are Intel semi-finalists last Friday, NCNOW made a big discovery: Among the Ossining High School INTEL semi-finalists, there was a third New Castle resident, 18-year-old senior Emily Prentiss, pictured above (second from the right) with classmates Amelia Clements, Evan Olin (also of New Castle) and Frances Russell.

Each of the 300 semi-finalists receives a $1,000 award, each school $1,000 for each of its semi-finalist students.

Next steps from the Intel Newsroom

“On Jan. 25, 40 of the 300 semifinalists will be named as finalists. They will receive an all-expenses-paid trip to Washington, D.C. from March 8-13. There, they will compete for more than $630,000 in awards provided by the Intel Foundation. Winners will be selected based on rigorous judging sessions and announced at a black-tie gala award ceremony at the National Building Museum on March 13. The top award is $100,000; the remaining top 10 will receive awards totaling $305,000.”

NCNOW interviewed Ms. Prentiss over the weekend about her research project.

Will you describe your research project for us?

It’s really about the way the brain responds to stimuli we’re not really paying attention to.  For example, if you’re hearing a regular “beep,” “beep,” “beep,” going on and then instead a “boop” occurs, your brain notices the change.  Or you notice the sound of a siren, because it’s unexpected and it occurs in the ambient.  Or you’re in a room and someone touches you from behind.  You notice it.  There’s a distinct way the brain reacts to those changes and we can measure it.

How did you measure these changes?

I used an electroencephalogram (EEG) to monitor the brainwaves of seven normal adult subjects as they watched a movie.  Each of the the subjects was holding a mechanism in each hand.  We sent vibrations mainly to the right hand, then occasionally to the left instead, then analyzed the data. We found that the brain responds in 172 milliseconds (one-fifth of a second) after the stimulus is presented.

What can be done with the knowledge that these changes are measurable?

The paradigm I invented would be ideal for monitoring the recovery process of a stroke patient, for example.  The task could be administered on a weekly basis.  It would be inexpensive and non-invasive.

The paradigm also gives engineers and people designing crash warning systems a useful tool—for example, in fancy cars where they have a crash warning system to tell you you’ve departed from your lane or when you’re too close to another car.  These systems usually use an auditory or visual alert.  But when you’re driving, you’re already using your ears and eyes and such a warning system might cause you to look to the right or left when you should be looking at the road.  So implementing a tactile alert, such as vibrations in the steering wheel, might be more effective.

Emily Prentiss
Research Project: The Right Hand Knows What the Left Hand Does: High-Density Electrical Recording of Human Somatosensory Change Detection
Science research teachers: Angelo Piccirillo and Valerie Holmes
Mentor: Dr. John Butler from the Cognitive Neurophysiology Lab at Albert Einstein College of Medicine

Abstract

Automatic change detection is a fundamental function of the human brain. It is the process by which the brain detects minute changes in ambient stimuli, regardless of where attention is directed. While it is widely accepted that this function exists, very few studies have ever quantified this function using electrophysiology or mathematical models in the somatosensory domain. Thus, this study focused on quantifying this function using electroencephalography (EEG) and an advanced mathematical analysis method, as well as developing a paradigm that would enable us to measure the function exclusively and effectively. This study used Mismatch Negativity (MMN) as a change detection measure in conjunction with stimuli delivered through an oddball paradigm, in which a stream of standard frequent stimuli is interrupted with a deviant infrequent stimulus. Seven healthy adult subjects completed the experiment. Somatosensory stimulation was delivered to the left and the right hands. Event-related potentials (ERP - automatic, event-driven brain responses) were recorded and measured using high- density EEG covering the entire scalp. Upon analysis, we epoched the standard and deviant data (took small sections from continuous data), leaving 100ms of pre-stimulus recording, and 500ms of post-stimulus recording. On average, change detection occurred 172ms after stimulus presentation within the post-central gyrus across all subjects. The MMN is rapidly becoming an important tool for the diagnosis and monitoring of certain disorders. Thus, the results of this study represent an important addition to literature, as they provide both an easily repeated paradigm as well as a definite metric with which to compare healthy automatic change detection with the abnormal processing habits of clinical populations.

The 14 Westchester County Intel Semi-finalists

Byram Hills High School: Sammi Cannold, Jesse Aaron Honig and Aurora Xu.

Eastchester High School: Brian Christopher McGovern

Edgemont High School: Vikrampal Alexandar Bhandari-Young and Apoorva Ganjam Talanki

Horace Greeley High School: Davis Wong Wertheimer

Ossining High School: Amelia Lyn Clements, Evan Daniel Olin, Emily Katherine Prentiss and Frances Corene Russell.

Pelham Memorial High School: Katharine Winifred Costantini

Scarsdale High School: Gibran Mian

White Plains High School: Benjamin Mark Van Doren