2014 Educational Course Teaching Materials

Please click on a course title below to download a slides for the educational course that you are attending. If you would like printouts of the slides to take notes during the course, you are responsible for printing your own slides. You will NOT receive printed handouts at the conference.

Full Day Courses:

• Anatomy and its impact on structural and functional imaging
  Surface anatomy of the brain and landmarks
  Development of the cerebral cortex
  High resolution imaging and anatomy
  Cytoarchitecture of the human cerebral cortex
  Receptorarchitecture and neural systems
  Anatomical conditions and MR-morphometry
  Anatomical background of dynamic causal modelling and connectivity
• Advanced fMRI Course
  Experimental design for fMRI
  Posterior Probability Mapping
• Pattern Recognition for NeuroImaging (or PR4NI)
  Pattern recognition in neuroimaging: principles & tools
  Validation & Inference
  Interpreting predictive models in terms of anatomically labelled regions
  Multivariate modesls of inter-subject anatomical variability    
  Spatial Regularization & sparsity for brain mapping
  Decoding and predicting intentions 
  Decoding of conceptual representations
  PET based classification for clinical diagnosis
  Network based predictive models
  M/EEG classification and brain computer interfacing
• Introduction to Imaging Genetics

Half Day Courses:

• Brain Stimulation: Past, Present and Future
  Overviews of basic technologies: tDCS / TES
  Modelling and imaging of the brain effects of stimulation / TMS / PET
  Modelling and imaging of the brain effects of stimulation / tDCS / fMRI
  Modelling and imaging of the brain effects of stimulation / tDCS / MRS/ DTI
  Examples of brain stimulation and imaging applied to controls and specific patient groups
• Tools to parcellate the brain and its relation to function
  PART I Parcellate the brain using anatomical features: Tractography based subdivision
  PART I Parcellate the brain using anatomical features: Myelin mapping in vivo
  PART II Parcellate the brain using functional features: Resting state functional connectivity subdivision
• Functional ASL: Perfusion based functional MRI using arterial spin labeling
  Modeling and Quantification    
• Applications of ASL in neuroscience             
• MR Diffusion Imaging: Getting Your Measures Right
  Diffusion MRI data acquisition
  Data Processing and Quality Control of DTI data
  Diffusion Imaging Models 1: from DTI to HARDI models
  Diffusion Imaging Models 2: from DTI to microstructure quantification
  Diffusion Tractography
• Neuroimaging Meta-Analysis
  ALE and BrainMap
  Inferring mental states from imaging data: OpenfMRI and the Cognitive Atlas
• A New Paradigm for Studying Drug Effects: Calibrated FMRI and Resting State Connectivity
  Pharmacologic FMRI and Methodological Concerns
  Overview of Calibrated FMRI
  Modeling and What We Have Learned About Calibrated FMRI From Studying Caffeine 
  How to Map Functional Connectivity Based on Synchronized CMRO2 Fluctuations During the Resting State
  Neurovascular Factors in Resting State FMRI
  Physiological Noise in Resting State Pharmacologic FMRI
  Pulling it all Together: Resting State Pharmacologic Calibrated FMRI Study of Alcohol and Nicotine
  Future Directions
• Electromagnetical Neuroimaging
  Basics of electromagnetic field mapping
  Understanding baselines
  A framework for collective multimodal reverse engineering the brain
  Scalp field dynamics in health and disease
  Source analysis and multimodal integration
  The physiological basis of multimodal integration in a clinical context
  Connectivity in source space
• The Art and Pitfalls of fMRI Preprocessing
  Introductory remarks: the problem of pre-processing pipeline dependence for task-based and resting-state fMRI
  Temporal Preprocessing (slice-timing, temporal filtering, spike removal)
  Spatial Preprocessing (Spatial Alignment, Normalization, and Smoothing)
  Interaction of Preprocessing Steps
  Artefact Removal (motion-related)
  Artefact Removal (Physiological)
  Data-driven Structured Noise Removal (FIX)