MVPA_research_diary

MVPA

Tasks and Ideas:

  1. Use shared-component GMM to model the location of the brain signal sources. This can be used in feature selection too.
  2. Try to use first k-component SVD to decompose the observation matrix. We don't need to represent the data in the beta space.
  3. Hierarchical Bayesian GMM
  4. Apply ICA to the individual beta map?
  5. Derive Gaussian EM for grid data based on Tori's paper
  6. Find the brain network in the task-oriented experiment, and compare the network with the activation areas obtained from the beta map!
  7. I'm running dimensionality reduction test on the the fMRI data..so far, MDS and LLE..still need to work on PCA, tSNE, SNE
  8. I will have to run some classifier on the data...SVM, LR, Decision Tree, Random Forest, AdaBoost
  9. Feature selection using Rand index, MI, whatever...
  10. Think about how to decompose the brain into components...GMM?

On-going work:

  1. Make similarity matrix for each pair of voxels using MI, then use spectral clustering to cluster them. We expect to see some cluster on the x y z coordinates
  2. In fact, we can use any kind of clustering, for instance GMM. It's a good idea to incorporate the x y z position too.
  3. Represent brain using r and theta coordinate?
  4. MI just talks about separability in all classes...what if we do separability for each class, say [c1 vs the rest], [c2 vs the rest], etc. This way we will know what voxel is the best to separate c1 from others, and we say that the voxel is corresponding for class c1. ---> we can plot the MI on the beta map

Readings:

Representational space

Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex [url]

Classifier ensembles for fMRI data analysis: an experiment [url]

Beyond mind-reading: multi-voxel pattern analysis of fMRI data [url]

Predicting Human Brain Activity Associated with the Meanings of Nouns [url]

Circular analysis in systems neuroscience: the dangers of double dipping [url]

A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex [url]

Decoding the Representation of Multiple Simultaneous Objects in Human Occipitotemporal Cortex [url]

Aligning Brains and Minds [url]

A topographic latent source model for fMRI data [url] (NeuroImg 2011) <--**

A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data [url] (IEEE Trans Med Img 2010) <--- *****

Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model [url] (Biometric 2009) <-- ***

The representation of biological classes in the human brain [url]

Searchlight approach

A comparison of volume-based and surface-based multi-voxel pattern analysis [url]

Slides or videos:

Overview of SPM: the SPM course video webpage from UCL

General Linear Model (GLM) applied to fMRI data analysis [pdf]

toolboxes:

Princeton's MVPA page

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Regularizations for logistic regression — Feb 4, 2013 1:39:01 AM

Voxel selection using submodular function optimization — Feb 4, 2013 1:24:00 AM

Next steps for distinguishability measure — Oct 7, 2012 10:14:57 PM

Collaborative filtering on brain — Aug 24, 2012 4:34:24 PM

Conference/Journal list — Aug 21, 2012 6:09:25 PM

Voxel selection using l1-norm — Aug 15, 2012 7:49:13 PM

Justify the model and the data using earning curve analysis — Aug 15, 2012 12:56:56 AM

A story to tell — Jul 30, 2012 6:34:12 PM

I like Gael's website — Jul 23, 2012 8:38:13 PM

Surface-based searchlight approach — Jul 23, 2012 8:14:41 PM

Haxby et al.'s several-subject fMRI is available — Jun 15, 2012 2:52:42 AM

Using independent voxel classification accuracy to determine feature order — May 25, 2012 6:50:38 AM

Using supervoxels in fMRI classification — May 19, 2012 1:16:42 AM

GMM on the class specific response map — May 19, 2012 1:14:18 AM