Month: January 2019

Talk: Dr. James V. Haxby, Dartmouth College

James V. Haxby, PhD

Dartmouth College

Distinguished Speaker

Wednesday, February 20 2019 3:30-5:00PM Bousfield A106

Abstract: Multivariate pattern analysis (MVPA) has revealed that information is encoded in finegrained patterns of cortical activity that can be measured with fMRI. Study of cortical functional connectivity also has revealed fine-grained topographies in the connectome that are closely related to these patterns of activity. The surface structure of functional cortical topographies, however, allows considerable variability across brains for encoding the same information. We introduced a new conceptual framework with computational algorithms that make it possible to model the shared information that is encoded in fine-grained functional topographies that vary across brains. This framework, “hyperalignment”, models shared information as a high-dimensional information space, rather than attempting to model a shared or canonical topographic structure in the physical space of cortical anatomy. Hyperalignment is based on computational algorithms that discover this space and calculate transformations that project individually-variable patterns of neural activity and connectivity into the common model information space.

Research Focus: My current research focuses on the development of computational methods for building models of representational spaces. We assume that distributed population responses encode information. Within a cortical field, a broad range of stimuli or cognitive states can be represented as different patterns of response. We use fMRI to measure these patterns of response and multivariate pattern (MVP) analysis to decode their meaning. We are currently developing methods that make it possible to decode an individual’s brain data using MVP classifiers that are based on other subjects’ data. We use a complex, natural stimulus to sample a broad range of brain representational states as a basis for building high-dimensional models of representational spaces within cortical fields. These models are based on response tuning functions that are common across subjects. Initially, we demonstrated the validity of such a model in ventral temporal cortex. We are working on building similar models in other visual areas and in auditory areas. We also plan to investigate representation of social cognition using this same conceptual framework.

 

Visitors from UCHC are encouraged to use the UCHC-Storrs shuttle service. Talks can also be joined remotely. Please contact us if you are interested in meeting with the speaker.

 

Call for IBACS-BIRC Research Assistantships in Neuroimaging

The CT Institute for the Brain and Cognitive Sciences (IBACS) is offering graduate assistantships of 10 hours per week during the Fall (2019) and Spring (2020) semesters at the Brain Imaging Research Center (BIRC). During the first year, assistants will be trained in neuroimaging methods, data science, and reproducibility. Assistants will spend the remaining allocated hours at BIRC, supporting users of BIRC facilities. This could involve helping design and implement experimental procedures for fMRI, EEG, tDCS, TMS etc., recruitment and prepping of participants, data analysis, or overseeing use of equipment by others. Applicants will be expected to commit to the full duration of the assistantship (Fall & Spring). Funds may be available during Summer 2019 to enable IBRAiN students to pursue their own research at BIRC. IBRAiN students also receive an allocation of 20 hours of MRI time to be used at BIRC during the course of the fellowship.

We anticipate three 10-hour assistantships starting Fall 2019, joining the existing IBRAiN students who have already completed their first year at BIRC and are starting their second year on the program. Click here for more information and the application form.

The deadline for receipt of applications will be midnight on February 28, 2019.