CNI Video
CNI Inaugural Event
March 22-23, 2012
To view the videos from the CNI Inagural Event please visit the CNI Inaugural Event page.
CNI Neuroimaging Seminars
Imaging Genetics: Biostatistical Challenges
JB Poline - UC Berkeley, Neurospin, Gif sur Yvette, France -- 4/27/2012
- In this talk, I will describe some bioinformatics and statistical challenges faced by new neuroimaging genetics studies that search through a very high number of associations between brain regions and genetic variations. I will review some current solutions proposed in the literature and show how multivariate techniques associated with variable selection can yield significant results when massive univariate analyses do not.
fMRI Analysis Tutorial
Bob Dougherty - Director of Research, CNI, Stanford University -- 3/30/2012
- A tutorial on the basics of analyzing functional MR data, including preprocessing steps, GLM model specification, analysis, and visualization. I'll demonstrate a basic analysis using FSL and visualization with FreeSurfer. You can also get the slides from this tutorial.
Dynamics of Human Functional Brain Development In Vivo In Utero
Moriah Thomason - Assistant Professor, Wayne State University -- 3/15/2012
- There is growing evidence that many neuropsychiatric, neurodevelopmental, and behavioral disorders result from alterations of neural connectivity that have very early developmental origins. If disordered neural system development begins even before birth then abnormal functional connectivity (FC) patterns should be observable in the human fetal period. The fetal period is characterized by critical periods of cellular proliferation, differentiation and maturational steps that dictate structural and functional changes in cells, tissues and organ systems. The goal of this proposed research begins with the proposition that to understand maladaptive patterns of fetal functional neural connectivity, we must first characterize FC in representative pregnancies. The present study specifically sought to test for the presence of the default mode network (DMN) in the human fetal brain. Given that this brain network is one of the most widely studied, and credited as essential for functions such as, reflective thought, concepts of self, and memory formation, this endeavor appealed as far reaching, challenging, and potentially impactful for changing our fundamental concepts surrounding capabilities of the human fetal brain.
High Angular Resolution Diffusion Imaging (HARDI) Tutorial
Bob Dougherty - Director of Research, CNI, Stanford University -- 2/2/2012
- A tutorial on analyzing high-angular resolution diffusion imaging (HARDI) data. I'll mainly focus on the practical aspects of data analysis, including preprocessing steps, model fitting, and analysis. I'll also go over some of the analysis tools that I use, such as MRtrix, mrDiffusion and dipy, and give practical examples from each of them.
MRI Safety
Video Tutorials
MR Methods
Image Systems Engineering
Spectral Radiance Units - An introduction to spectral radiance units. - Brian Wandell
Measuring radiance - A description of the methods for measuring radiance. - Brian Wandell
Spectral Irradiance - An introduciton to spectral irradiance - Brian Wandell
Models/Linear Models.html Linear Models - An introduction to linear models - Brian Wandell
ISET
Matlab Startup - Starting up matlab for ISET - Brian Wandel
Energy and Quanta in calculations - Discussion of energy and quanta in ISET calculations - Brian Wandell
Course Lecture Videos
Statistics and data analysis in MATLAB: PSYCH216A
Kendrick Kay - Postdoctoral Fellow, Stanford University -- (Spring 2012)
The goal of this course is to (1) identify and explain basic statistical principles that are widely applicable to the analysis of neuroscience and behavioral data and (2) show how these principles can be translated into practice. We will use MATLAB as the programming environment, emphasizing good coding practices (code generality, code documentation, code efficiency). Topics will include probability distributions, error bars and confidence intervals, statistical significance, regression, classification, correlation, linear and nonlinear models, cross-validation, bootstrapping, model selection, and randomization methods, and may also include regularization methods (ridge regression, lasso) and unsupervised learning (PCA, ICA, k-means). We will focus on nonparametric and computational approaches to statistical problems, as opposed to classical statistical approaches involving parametric assumptions and analytic solutions. In each class we will cover the theory behind a particular statistical principle and learn how to implement the principle efficiently and effectively in MATLAB.
Lecture 1 - Probability distributions and error bars
Lecture 2 - Hypothesis testing and correlation
Lecture 3 - Model specification
Lecture 4 - Model fitting
Lecture 5 - Model accuracy
Lecture 6 - Model reliability
Lecture 7 - Discussion questions (no video)
Lecture 8 - Classification
Lecture 9 - Analysis Examples
Applied Vision and Image Systems: PSYCH 221
Brian Wandell - Professor, Stanford University -- Winter 2012
An introduction to vision science and its applications to imaging technologies. The course is designed specifically for students interested in various aspects of imaging technologies. The course consists of a series of lectures, seminar discussions, and laboratory tutorials. Lectures given by Professor Wandell will cover the basic tools used in digital imaging and image quality measurement; software simulations of digital imaging will be a significant component of the course; guests from industry will provide special seminars and give their perspectives on industry directions.
Lecture 1 - Introduction, course mechanics.
Lecture 2 - Introduction to optics: pinhole, diffraction, Airy disc, Snell's law, Lensmaker's equation
Lecture 3 - Depth of field, linear systems and optics (linespread, pointspread, modulation transfer function), human image formation
Lecture 4 - Linear systems and optics review, images and Fourier series, human image formation, chromatic aberration
Lecture 5 - Human chromatic aberration, adaptive optics, laser eye surgery
Lecture 6 - Adaptive optics, laser eye surgery, sensors architecture, sensor equations
Lecture 7 - Sensor equations, pixel designs, noise sources, imager components
Lecture 8 - Color architectures, high dynamic range photography, CCD
Lecture 9 - ISET overview, class projects presentation
Lecture 10 - Measuring image quality
Lecture 11 - Photoreceptor mosaic, light and superposition
Lecture 12 - Light and superposition, rod color matching, cone color matching, physiology and color matching
Lecture 13 - Physiology and color matching, color appearance, photometry, color matching functions
Lecture 14 - Illuminance, luminance, CIE XYZ and xy
Lecture 15 - Color metrics, opponent colors
Lecture 16 - Munsell color appearance, color and pattern, color matching with displays
Lecture 17 - Spatial pattern vision, retina and the brain, visual masking, luminance peripheral field, alignment acuity
Lecture 18 - Image processing pipeline
Lecture 19 - Spatial vision demonstrations, image compression, JPEG
Lecture 20 - Multiscale representations, display technologies: LCD, plasma, DLP, OLED