Name: identification of disease-related spatial covariance patterns using neuroimaging data
Uploaded: 2013-06-26T13:00:00
Description: Watch this Scientific Journal Video about Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data at JoVE.com

This work was supported by Grant No. P50NS071675 (Morris K. Udall Center of Excellence in Parkinson's Disease Research at The Feinstein Institute for Medical Research) to D.E. from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. The sponsor did not play a role in study design, collection, analysis and interpretation of data, writing of the report or in the decision to submit the paper for publication.

1. Data Collection and Preprocessing
<ol>
 <li>The SSM/PCA method can be applied to single volume images obtained from various sources and modalities. Specifically, for on-site PET imaging of metabolism, prepare a suitable radionuclide tracer such as [18F]-fluorodeoxyglucose (FDG) and administer to each patient. Patients are usually scanned at rest with eyes open, following a fast of at least 12 hr, off medications.</li>
 <li>Scan each subject for individual or group assessment. For pattern derivation, scan ...

The SSM/PCA model originally presented by Moeller et al.4 has evolved1-3 into a straightforward and robust technique for the analysis of neuroimaging data. However, there have been ambiguities in the application of this methodology that we have attempted to clarify here and in previous publications5-7,10. Some of these issues have been addressed in the text but are reemphasized here because of their importance. As detailed in the Introduction, SSM/PCA is primarily effective in re...

Neurodegenerative disorders have been extensively studied using techniques that localize and quantify abnormalities of brain metabolism as well as non-inferential methods that study regional interactions17. Data-driven multivariate analytical strategies such as principal component analysis (PCA)1,2,4,18 and independent component analysis (ICA)19,20, as well as supervised techniques such as partial least squares (PLS)21 and ordinal trends canonical variates analysis (OrT/CVA)22 can reveal characteristic patterns or "networks" of interrelated activity. The basics of multivariate procedures, particularly the scaled subprofile model (SSM)1,2,4-6,18 have been previously described in JoVE3. This PCA-based approach was originally developed to examine abnormal functional covariance relationships between brain regions in steady-state single volume images of cerebral blood flow and metabolism acquired in the resting state of modalities such as PET and SPECT that exhibit high signal-to-noise characteristics. Disease-specific SSM patterns are imaging biomarkers that reflect overall differences in the regional topography in patients compared to normal subjects7,16 and may reflect a single network process or the assimilation of several complex abnormal functions23. Metabolic covariance pattern brain networks are associated with expression values (subject scores) that can distinguish between normal and disease groups and provide network-based measures that correlate with clinical ratings of disease severity. Typically, subject scores for such patterns increase with disease progression and may even be expressed before symptom onset14,24. Indeed, disease-related network biomarkers have been characterized for neurodegenerative disorders such as Parkinson's disease10 (PD), Huntington's disease25(HD), and Alzheimer's disease8 (AD). Importantly, disease-related metabolic topographies have also been identified for atypical parkinsonian movement disorders such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). These patterns have been used in concert for the differential diagnosis of individuals with clinically similar "look-alike" syndromes12,13,26.
In contrast, typical fMRI voxel-based univariate methods assess the significance of differences between patients and controls in isolated brain clusters. More recently, methods have been developed to measure functional connectivity between variously defined brain regions27-29. This definition of functional connectivity is restricted to subject and region specific interactions and deviates from the original SSM/PCA concept that refers to the cross-sectional interconnectivity of intrinsic spatially distributed brain network regions1,2,23,30. To their advantage, MRI platforms are easily installed, widely available, non-invasive and typically require shorter scanning time than traditional radiotracer imaging modalities such as PET or SPECT resulting in an upsurge of potential methodologies described in recent literature. However, the resulting time-dependent fMRI signals provide indirect measures of local neural activity31,32. The generally complex analytical algorithms employed have been limited by the large size of datasets, the physiological noise inherent in fMRI signals, as well as the high variability in brain activity that exists between subjects and regions19,23. Although interesting information regarding brain organization can be inferred from the properties of fMRI "networks", they have not been sufficiently stable to be used as reliable disease biomarkers. Furthermore, the resulting network topographies are not necessarily equivalent to those identified using established functional imaging methodologies such as SSM/PCA. For the most part, rigorous cross-validation of the resulting fMRI topographies has been lacking with few examples of successful forward application of derived patterns in prospective scan data from single cases.
An advantage of PCA covariance analysis lies in its capacity to identify the most significant sources of data variation in the first few principal components but it is ineffective if the prominent eigenvectors represent random noise factors rather than actual intrinsic network response. By selecting only the first few eigenvectors and limiting to those that show significant differences in patient versus normal control scores, we greatly reduce the influence of noise elements. However, for the basic approach described here, these measures may not be adequate to generate robust estimators in a typical fMRI dataset with the exception of the modalities described below.
Thus, because of the stable direct relationship of regional glucose metabolism and synaptic activity33, this methodology has been applied primarily to the analysis of rest state FDG PET data. However, given that cerebral blood flow (CBF) is closely coupled to metabolic activity in the resting state10,11,34, SPECT35,36 and more recently arterial spin labeling (ASL) MRI perfusion imaging methods37,38, have been used to assess abnormal metabolic activity in individual cases. That said, the derivation of reliable spatial covariance patterns with resting state fMRI (rsfMRI) is as previously noted not straightforward 31,32. Even so, preliminary SSM/PCA analysis of rsfMRI data from PD patients and control subjects has revealed some topographical homologies between disease-related patterns identified using the two modalities, PET and amplitude of low-frequency fluctuations (ALFF) of BOLD fMRI39,40. Lastly, we also note that this approach has been applied successfully in voxel based morphometry (VBM) structural MRI data41,42, revealing distinctive spatial covariance patterns associated with age-related volume loss and in further comparisons of VBM and ASL patterns in the same subjects43. The relationship between SSM/PCA spatial covariance topographies and analogous brain networks identified using different analytical approaches and imaging platforms is a topic of ongoing investigation.

<table border="1">
 <tbody>
 <tr>
 <td>Name of Equipment</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Image Acquisition </td>
 </tr>
 <tr>
 <td>PET Scanner</td>
 <td>GE Medical Systems</td>
 <td>GE Advance</td>
 <td>Any PET, PET/CT and PET/MRI Scanners from GE, Siemens and Philips </td>
 </tr>
 <tr>
 <td>PC Workstations</td>
 <td>Lenovo</td>
 <td>Any</td>
 <td><a href="http://www.lenovo.com/us/en/" target="_blank">http://www.lenovo.com/us/en/</a></td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Radiopharmaceuticals </td>
 </tr>
 <tr>
 <td>[18F]-fluorodeoxyglucose</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Routine Production</td>
 <td>Also distributed by Cardinal Health <a href="http://www.cardinal.com/" target="_blank">http://www.cardinal.com/</a> </td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Software </td>
 </tr>
 <tr>
 <td>ScanVP</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Version 5.9.1, Version 6.2, To be released</td>
 <td><a href="http://www.feinsteinneuroscience.org/" target="_blank">www.feinsteinneuroscience.org</a></td>
 </tr>
 <tr>
 <td>SPM</td>
 <td>The UCL Institute of Neurology</td>
 <td>spm99-spm8</td>
 <td><a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://www.fil.ion.ucl.ac.uk/spm</a></td>
 </tr>
 <tr>
 <td>Windows</td>
 <td>Microsoft</td>
 <td>Any</td>
 <td></td>
 </tr>
 <tr>
 <td>Matlab</td>
 <td>Mathworks</td>
 <td>Matlab Version 7.0, 7.3</td>
 <td><a href="http://www.mathworks.com/" target="_blank">http://www.mathworks.com/</a></td>
 </tr>
 <tr>
 <td>JMP</td>
 <td>SAS</td>
 <td>Version 5</td>
 <td><a href="http://www.jmp.com/" target="_blank">http://www.jmp.com/</a></td>
 </tr>
 </tbody>
</table>

identification of disease-related spatial covariance patterns using neuroimaging data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

A simple application of multivariate SSM/PCA analysis to derive a neuroimaging biomarker pattern for PD is illustrated below. PET FDG images of ten clinically diagnosed PD patients (6M/4F, 59y &plusmn; 7y sd) of variable diseased duration (9y &plusmn; 5y sd) and ten age and gender matched normal controls (6M/4F, 58y &plusmn;7y sd) were analyzed using our ssmpca routine. The twenty corresponding spatially pre-normalized images were initially selected under the categories disease subjects or controls along with th...

Watch this Scientific Journal Video about Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data at JoVE.com

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

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