BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260524T134612Z UID:BFA24EC6-DC6F-4578-8F05-36CC2F4E6894 DTSTART;TZID=America/New_York:20260815T143000 DTEND;TZID=America/New_York:20260815T163000 DESCRIPTION:In the world of functional Neuroimaging\, the focus has histori cally been on "Gray Matter" (the brain's processors)\, often ignoring the "White Matter" (the communication cables). This talk introduces WhiFuN\, a pipeline designed to extract meaningful signal from these under-explored regions using unsupervised machine learning. We will start by framing Rest ing-State fMRI as a massive time-series dataset\, where each 3D pixel (vox el) in the brain contains a temporal signal of brain activity (movie of th e 3D Brain). By calculating Functional Connectivity\, essentially the Pear son correlation between these signals\, we can treat the brain as a comple x graph. The core of the talk will detail how we use K-means clustering on voxel-wise connectivity matrices to identify latent brain networks. We wi ll dive into the engineering challenges of this approach\, specifically ho w to decide the value of K. Finally\, we will look at the results: how the se data-driven networks allow us to identify group differences and behavio ral associations\, effectively turning raw\, noisy 4D imagery into a struc tured feature set for clinical analysis.\n\nSpeaker(s): Pratik Jain\, \n\n Agenda: \nHybrid event\, in-person or online:\n2:30 Introduction\nTechnica l Talks\nQuestion and Answer\nNetworking\n4:30 Conclusion\n\nRoom: Meeting Rooms 2\,3\, 2 Civic Center Drive\, East Brunswick\, New Jersey\, United States\, 08816\, Virtual: https://events.vtools.ieee.org/m/560974 LOCATION:Room: Meeting Rooms 2\,3\, 2 Civic Center Drive\, East Brunswick\, New Jersey\, United States\, 08816\, Virtual: https://events.vtools.ieee. org/m/560974 ORGANIZER:a.j.patel@ieee.org SEQUENCE:11 SUMMARY:Mapping the Brain's "Wiring": Unsupervised Learning on High-Dimensi onal fMRI Data to get the Brain networks URL;VALUE=URI:https://events.vtools.ieee.org/m/560974 X-ALT-DESC:Description: <br /><p>In the world of functional Neuroimaging\, the focus has historically been on "Gray Matter" (the brain's processors)\ , often ignoring the "White Matter" (the communication cables). This talk introduces WhiFuN\, a pipeline designed to extract meaningful signal from these under-explored regions using unsupervised machine learning. We will start by framing Resting-State fMRI as a massive time-series dataset\, whe re each 3D pixel (voxel) in the brain contains a temporal signal of brain activity (movie of the 3D Brain). By calculating Functional Connectivity\, essentially the Pearson correlation between these signals\, we can treat the brain as a complex graph. The core of the talk will detail how we use K-means clustering on voxel-wise connectivity matrices to identify latent brain networks. We will dive into the engineering challenges of this appro ach\, specifically how to decide the value of K. Finally\, we will look at the results: how these data-driven networks allow us to identify group di fferences and behavioral associations\, effectively turning raw\, noisy 4D imagery into a structured feature set for clinical analysis.</p><br /><br />Agenda: <br /><p>Hybrid event\, in-person or online:<br>2:30 Introducti on<br>Technical Talks<br>Question and Answer<br>Networking<br>4:30 Conclus ion</p> END:VEVENT END:VCALENDAR