The assessment of neurological systems with functional imaging

Abstract

In recent years a number of multivariate approaches have been introduced to map neural systems in health and disease. In this review, we focus on spatial covariance methods applied to functional imaging data to identify patterns of regional activity associated with behavior. In the rest state, this form of network analysis can be used to detect abnormal topographies reflecting regional changes in distributed neural systems. In activation experiments, this approach can be used to quantify network-performance relationships in normal and disease cohorts. Functional changes in the relevant neural systems are likely to underlie the behavioral abnormalities observed in a variety of disease and treatment conditions.

Fig. 1

Identified Parkinson’s disease spatial covariance patterns. (A) Parkinson’s disease-related metabolic covariance pattern (PDRP). This PD-related pattern (PDRP) was identified by network analysis of [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography scans from 20 PD patients and 20 age-matched healthy volunteers (Carbon & Eidelberg, 2002). This pattern (representing the Wrst principal component, which accounted for 20.7% of the subject × voxel variation) was characterized by relative pallidothalamic (left), pontine, and cerebellar hypermetabolism (right), associated with metabolic decrements in the lateral premotor and posterior parietal areas (bottom). PDRP expression was significantly increased in the PD cohort (p < .00001) compared to controls. In a prospective individual case analysis, we computed the expression of the PDRP in 14 subsequent PD patients and 14 control subjects (Asanuma et al., 2005). As in the original analysis, prospectively computed PDRP scores were significantly elevated in the disease group (p < .00001). [The display represents voxels associated with a significant correlation (p < .001) between local metabolic rate and pattern expression. These were also demonstrated to be reliably different from zero by bootstrap estimation procedures (p < .001). Voxels with positive region weights (metabolic increases) are color coded from red to yellow; those with negative region weights (metabolic decreases) are color coded from blue to purple]. (B) Parkinson’s disease-related metabolic pattern associated with cognitive performance (PDCP). This cognition-related spatial covariance pattern was identified in the network analysis of FDG PET scans from 15 non-demented PD patients with mild-moderate motor symptoms (Mentis et al., 2002). This metabolic network was independent (orthogonal) to the PDRP and accounted for 17.6% of the subject × voxel variation. This pattern was characterized by relative hypometabolism of dorsolateral prefrontal cortex (DLPFC), preSMA, and superior parietal regions (right), associated with cerebellar/dentate nucleus (DN) metabolic increases (left). Subject scores for this pattern correlated significantly (p < .01) with psychometric indices of executive functioning and verbal learning (California Verbal Learning Test, CVLT). The activity of this network was highest and cortical metabolism lowest in subjects with impaired verbal learning and executive functioning. In a prospective analysis, we quantiWed PDCP expression in the FDG PET scans of 32 subsequent PD patients on an individual case basis. Those computed scores accurately predicted verbal learning performance in these subjects (p < .005). [The display represents voxels that contribute significantly to the network at p < .001 and that are reliably different from zero by bootstrap estimation procedures (p < .05). Voxels with positive region weights (metabolic increases) are color coded from red to yellow; those with negative region weights (metabolic decreases) are color coded from blue to purple].

Fig. 2

Changes in network expression with disease progression. Mean expression of the PD-related metabolic covariance patterns (Fig. 1) computed in 15 early PD patients scanned longitudinally with FDG PET (see text). The data indicate that the activity of both metabolic topographies progress over time (p < .005). However, the rate of change in network expression is significantly slower for the cognition-related PDCP pattern (p < .02).

Fig. 3

Network analysis of activation data from healthy volunteers scanned during motor sequence learning. (A) Target acquisition: principal components analysis (PCA) of image subtraction data from 18 healthy volunteers (Carbon et al., 2003) disclosed a significant spatial covariance pattern associated with target acquisition. This network topography (accounting for 9.0% of the subject × voxel variation) was characterized by learning-related activations (SEQ > CCW, see text) in the caudate and ventral prefrontal cortex, as well as in the superior temporal gyrus and posterior parietal regions. Subject scores for this pattern correlated with concurrent behavioral measures of target acquisition (R² =.31, p < .01). Reprinted with permission of Wiley-Liss, Inc. a subsidiary of John Wiley & Sons, Inc. (B) Target retrieval: in the same analysis, an independent spatial covariance pattern was associated with target retrieval. This network topography (accounting for 9.2% of the subject × voxel variation) was characterized by bilateral learning-related activations in the dorsolateral prefrontal cortex (DLPFC) and premotor cortex (PMC), and in the left anterior cingulate area and the right inferior parietal cortex. Subject scores for this pattern correlated with concurrently measured retrieval indices (R² =.60, p < .001). [Positive region weights (red–yellow) were thresholded at Z =2 to display clusters contributing significantly (p < .01) to the network].

Fig. 4

Changes in learning performance and network activity during acute antiparkinsonian intervention with deep brain stimulation (DBS) (squares) or levodopa infusion (triangles). Treatment-mediated changes in the subject scores for the normal retrieval network (Fig. 3B) correlated significantly with concurrently measured changes in the global retrieval index (R² =.40, p < .01). Learning performance and network activity tended to increase during DBS and to decline with levodopa infusion (see Carbon et al., 2003). Reprinted with permission of Wiley-Liss, Inc. a subsidiary of John Wiley & Sons, Inc.