Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Summary

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.

Abstract

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 data^2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors^7,8. 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 subjects^5,6. 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 diagnosis^12,13 and to assess disease progression and treatment effects at the network level^7,14-16. 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.

Introduction

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 s....

Protocol

1. Data Collection and Preprocessing

1. 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.
2. Scan each subject for individual or group assessment. For pattern derivation, scan an equal number of gender- and age-matched patients and controls.
3. Transfer data to a workstation and convert to an appropriate format for an....

Results

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 ± 7y sd) of variable diseased duration (9y ± 5y sd) and ten age and gender matched normal controls (6M/4F, 58y ±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 the default mask (

Discussion

The SSM/PCA model originally presented by Moeller et al.⁴ has evolved^1-3 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 publications^5-7,10. 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.......

Materials

List of materials used in this article

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