School, Program, and Course Information

  • Spring Course on Brain Analysis

    I will be offering PSYC5171-001 Special Topics in Cognitive Science: Neuroimaging Methods this Spring, meeting Friday 2-5pm in Arjona 340. As in past years, the course will cover the practical issue of maps: the process of constructing maps of brain activity implicated in cognitive states from functional magnetic resonance imaging data (fMRI). New this year, the majority of the course will explore the mapping problem: how can we use noninvasive macroscale methods such as fMRI to map between the multiple scales of computations posited by theories of cognition and neuronal function? To help answer this question, we will focus discussion on the actual or potential application of analytic concepts and techniques from engineering, mathematics, physics, and other sciences to fMRI data. Graduate and advanced undergraduate students from these fields are welcome to enroll.

    Please contact Roeland.Hancock@uconn.edu for a permission number. A tentative list of topics is below.

     

    Roeland Hancock, PhD
    Associate Director,  Brain Imaging Research Center
    Assistant Research Professor, Psychological Sciences
    Affiliate, CT Institute for Brain and Cognitive Sciences | Mathematics

     

    Unit 1: Origins and analysis of event-related functional magnetic resonance imaging data

    • Origins and filter characteristics of the BOLD signal
    • Sources of non-neural noise
    • Quality assessment and preprocessing of fMRI using containers
    • General linear models for fMRI
    • Group-level statistical inference

     

    Unit 2: Models of the brain

    • The challenges of linking fMRI activity to neural scales and computational cognitive neuroscience
    • Cognitive models
    • Deep learning/neural networks
    • Generative models (neural field and neural mass models)

     

    Unit 3: The high dimensional brain

    • Multivariate classification
    • Representational similarity analysis
    • Spatial entropy

     

    Unit 4: The low dimensional brain and dynamics

    • Dimensionality reduction methods
    • Applications of graph theory
    • Manifold descriptions
    • Complexity and self-organized criticality

     

    For more information, contact: Roeland Hancock at roeland.hancock@Uconn.edu