Vanessa

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Derivation of Functional Networks with Independent Component Analysis

A data driven approach to differentiate functional networks for disease diagnosis

Comparing functional brain networks between a healthy and a disease population would allow for the identification of biomarkers to classify the disease, and would further contribute to our understanding of normal and aberrant brain connectivity. I am interested in data driven approaches to study the brain, and so for this project, my first aim was to compare functional networks between a clinical and normal group by using independent component analysis. My second aim was to incorporate machine learning into this project. Put together, these aims encompass a primary goal of building a classifier that can take an ICA run as input, and output a diagnosis.

Application

Attention deficit hyperactivity disorder (ADHD) is a controversial developmental disorder characterized by hyperactivity, attention problems, and impulsivity that effects 3-5% of school aged children, posing a high societal cost ($36 to $52 billion dollars annually), and increasing in incidence (a 5.5% increase annually) (1)(2). Making progress in understanding ADHD through structural and functional imaging is challenged by the range in severity and type of symptoms, and inability to make connections between functional and structural findings on a large scale. Functional deficits in ADHD have been narrowed down to problems with sustained attention, cognitive-control and inhibition, with evidence of this symptomatology lasting into adulthood. Structural deficits in ADHD include both cortical thinning and reduced volume of fronto-striatal regions, cerebellum, basal ganglia, and parietotemporal regions. Given the heterogeneous nature of this disorder and lack of data-driven methods for diagnosis, it is a prime application for this project.

Question

Are there significant differences in the default mode network (DMN) between ADHD and control?

Data

Making datasets available in the public domain is growing in popularity, and so I wanted to use data of this type. The ADHD200 dataset consists of data for normal and ADHD (combined, inattentive, and hyperactive) across 8 different sites. I decided to use a subset of this larger data set, data from NYU, which is one of the sites. My goal would then be to derive and find differences in functional networks between ADHD and control for the NYU dataset. My subject population originally included 222 individuals including healthy controls (99), ADHD combined (77), ADHD inattentive (44), and ADHD-Hyperactive (2). Due to the small sample of the hyperactive subtype, these data were not included in analysis. After quality analysis and visual inspection of data, the final group included 162 individuals (69 TD Control, 62 ADHD combined, 31 ADHD inattentive) with mean ages 12.77, 10.75, and 12.41 for further analysis. The reason that I was interested in splitting the ADHD into sub-diagnoses was because not only do we have poor understanding about what differentiates healthy control from disease, but also what differentiates disease subtypes. I thought that I could look at both of these differences for my analysis. Functional resting BOLD and an anatomical T1 were both collected on a Siemens Magneton Allegra at the New York Child Study Center (scan parameters are detailed below, and full details can be found in the supplementary section).

Image Collection Parameters


METHODS

Preprocessing

I chose to use FSL for its MELODIC program (to perform ICA) and command line utility. I looked at the source code to break apart the pre-determined GUI program, and wrote my own set of python and bash scripts to accomplish similar functionality. Anatomical data was brain extracted with FSL's BET tool at a threshold of .225. Functional data was also brain extracted with a threshold of .3, motion corrected with FSL's MCFLIRT, and smoothed at a 6mm kernel (2 times the voxel size). I chose to smooth because I am interested in larger scale patterns of brain activation across a large group size as opposed to detailed activation patterns in a small number of individuals or voxels. Functional data was bandpass filtered at a threshold of .008 to .1, and noise correction was performed with FSL's SUSAN. A linear registration was performed with FSL's FLIRT tool by registering the functional data to the MNI 152 Standard Template with the subject's native anatomical as an intermediate.

Independent Component Analysis

I am interested in data-driven approaches, so I knew from the getgo that I wanted to do independent component analysis (ICA) to break a 4D dataset (xyz coordinates over time) into spatial and temporal components. The signal in fMRI we know is a combination of different sources of variability, including noise, machine artifact, physiological signal, motion, and then finally the BOLD signal. When we analyze this data we usually have a specific hypothesis about BOLD activity at each voxel, and we might do a regression or a GLM. However in the case of resting BOLD data where there is not a task of interest to model, we can utilize ICA.

ICA is usually thought of as a more "exploratory" data analysis technique to find independently distributed spatial patterns in the data. These independent signals are linear combinations of true signals. If we think of a dataset with n voxels at p timepoints:

p x n we will call this matrix X

and we want to decompose it so that:

X = A S

where S is optimized to contain statistically independent spatial maps in rows A is the square mixing matrix, in each columns is a time-courses for the spatial map in S.

To perform this on a single resting BOLD dataset, we can think of that dataset as a 2D matrix where time is on the x axis, and space is on the Y axis. Performing ICA on an individual dataset would result in components that are orthogonal to one another and independent, with the first component accounting for the highest percentage of variance in the data. This technique will be used to derive individual functional networks, and a similar technique called multisession temporal concatenation (stacking many datasets together before performing ICA) will be used to derive group networks.

Identification of Default Mode Network

The default mode network (DMN) was manually identified from the group networks (below, left) based on visual matching to verified ventral and dorsal DMN maps produced by the Grecius lab at Stanford (below, right). The network was also verified by a postdoc in the Stanford Cognitive and Systems Neuroscience Laboratory.

Group derived DMN component (left), as compared to templates from Grecius Lab (right)

This group network was used in a template matching procedure to identify the DMN for all individual subjects. The algorithm calculates the average activation per voxel shared between the contender image and template, and subtracts the average activation per voxel not shared, resulting in a “difference score” with higher scores indicating better matches. This algorithm was carried out in a custom python script that uses the nibabel module for reading nifti files. The top matches for each subject were manually viewed to select the correct component for the default mode network.

Statistical Analysis

1 sample T-Tests for each of the three groups (healthy control, ADHD inattentive and ADHD combined) were carried out to confirm the correct selection of the default mode network. We would expect to see significant overlap between subjects in areas that are part of the network, which would be apparent in the 1 sample T-test. Two sample T-tests were carried out to compare differences between default mode networks between groups.


RESULTS

Statistical Analysis

The 1 sample T-tests confirmed that the default mode network was correctly selected for the three groups: control, combined, and inattentive (below). It should be noted that the inattentive subtype did not have as robust a network, however going back to check the selected components did not reveal any incorrectly selected networks. It is likely that the ventral and dorsal DMN were separated into separate components, and this result should be considered a limitation of the analysis.

Control, Combined, and Inattentive 1 Sample T-tests verify correct identification of DMN

Two sample T-tests to assess differences between groups only revealed sub-significant findings (uncorrected, .001) for Inattentive > Control in right putamen, superior frontal gyrus, and middle temporal gyrus, Control > Combined in middle frontal gyrus and temporal pole, Combined > Control in precuneus, and Inattentive > Combined in middle temporal gyrus and OFC. The putamen is implicated to be involved with motor learning and control predominantly driven by the neurotransmitter dopamine. Given the attentional and cognitive deficits associated with ADHD, it makes sense that we might see differences between ADHD and control focused on this region. The precuneus is involved with visospatial processing, consciousness, introspection, and episodic memory, which arguably are other functions known to be aberrant with ADHD (3)(4).

Two sample T-tests for inattentive > control, control > combined, combined > control, and inattentive > combined

CLASSIFICATION

To perform classification of disease based on the default mode network, a 1 sample T-test was done for all participants, and the resulting image saved, masked to gray matter, and binarized to represent a DMN template for the entire group. This resulted in an n x p matrix, with n rows of subjects and p rows of voxels. A support vector machine was used with a linear kernel. **(More results to be posted, this part of analysis still in progress)

Discussion and Future Work

The DMN was originally selected because it has been shown to be aberrant across many disorders, and because it is a relatively easy network to identify out of the many components. It is salient, however, that this same analysis done with an attention network might be more relevant to ADHD. I did not have the time to perform all of the analysis over again with a different network, however I want to note that it is something that I am going to pursue.

My long term goals in this field pertain to classification of large, publicly available datasets. With the goal of identifying biomarkers of disease to aid with diagnosis, what would be useful is to develop pipelines that take raw resting BOLD data for different groups, filters and derives individual networks, and then matches them to templates, derives differences. A classifier might then be built to distinguish between normal and disease, and used to diagnose a novel dataset. While one lone classifier probably is not good enough to be used to classify this new ICA run, the technique of “boosting” could be used with multiple classifiers to come to an informed diagnosis. This technique can be thought of as taking a “majority vote.” Further extension of this method would be to extract relevant features from these maps to build a database with supporting infrastructure to query semantically accessible results, however this is more of a problem for bioinformatics than neuroscience.

[test]

Appendix

REFERENCES

 (1) Cherkasova, M. V., & Hechtman, L. (2009). Neuroimaging in attention-deficit hyperactivity disorder: beyond the frontostriatal circuitry. Canadian journal of psychiatry. Revue canadienne de psychiatrie, 54(10), 651-64.

(2) Cubillo, A., & Rubia, K. (2010). Structural and functional brain imaging in adult attention-deficit/hyperactivity disorder. Expert review of neurotherapeutics, 10(4), 603-20. doi:10.1586/ern.10.4

(3) Kelly, a M. C., Margulies, D. S., & Castellanos, F. X. (2007). Recent advances in structural and functional brain imaging studies of attention-deficit/hyperactivity disorder. Current psychiatry reports, 9(5), 401-7.

(4) Seidman, L. J., Valera, E. M., & Makris, N. (2005). Structural brain imaging of attention-deficit/hyperactivity disorder. Biological psychiatry, 57(11), 1263-72. doi:10.1016/j.biopsych.2004.11.019

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