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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20240310T030000 RDATE:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20250309T030000 RDATE:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20260308T030000 RDATE:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:e27ecf97c2905f1e25730cfe50f58761 CATEGORIES:Computer Science Department Colloquium CREATED:20190823T084029 SUMMARY:Challenges in Big Data Analysis in Neuroimaging LOCATION:CoRE A 301 DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Modern neuroimaging is now a “big data” science. The rapid developments in in-vivo neuroimaging have led to large quantities of digital information about the human brain. Modern neurotechnologies produce massive, complex i maging data from multiple modalities that reflect brain structure and funct ion. Parsing, analyzing and interpreting this information is challenging du e to the high dimensionality in the data. Another challenge is the inherent population heterogeneity. In this talk, I will discuss advances in multiva riate pattern analysis techniques for neuroimaging. I will present two rece nt machine learning methods using supervised and unsupervised approaches, w hich were proposed to address the aforementioned challenges. In the first p art of the talk, I will describe a non-negative matrix factorization method to summarize high-dimensional neuroimaging data with a set of highly inter pretable brain networks. I will present results using structural neuroimagi ng data from a study of human brain aging and compare the proposed framewor k to commonly used matrix factorization techniques, such as PCA and ICA. In the second part of the talk, I will present a method that aims to reveal i nherent heterogeneity in the patient group by jointly performing disease cl assification and clustering of disease sub-groups. The method, termed HYDRA , extends the SVM framework by introducing multiple linear hyperplanes that form a convex polytope which separates the two groups, while each face of the polytope effectively defines a disease subtype. Results using data from a study of Alzheimer’s disease will be presented, showing disease sub-grou ps revealed by our method.
DTSTAMP:20240328T212907Z DTSTART;TZID=America/New_York:20161213T110000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR