Spatial and Temporal Characteristics of Visual Field Progression in Glaucoma Assessed by Parallel Factor Analysis

Abstract

Purpose: To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression through multi-way decomposition of data.

Methods: Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80 perimetric glaucoma eyes were arranged into a three-dimensional cube. The data were decomposed using parallel factor analysis.

Results: Three tri-linear components (i.e., spatial scores, temporal loadings, and subject-specific loadings) were identified. Component 1 clearly showed differences between superior and inferior hemispheres, linear trends over time, and wide variability in perimetric glaucoma. Findings were compatible with well-known characteristics of glaucomatous VF defects. Component 2 showed nasal and central areas in contrast with superior, inferior, and temporal peripheral locations, whereas component 3 showed a contrast between nasal and temporal hemispheres. Both components 2 and 3 failed to show clear temporal trends.

Conclusions: Identification of spatio-temporal patterns shows new possibilities for a multi-way decomposition methodology for earlier diagnosis and prediction of glaucomatous VF progression.

Keywords: Glaucoma; Multi-way decomposition; Parallel factor analysis; Visual fields.

Fig. 1. Diagram representing the parallel factor analysis model. VF = visual field.

Fig. 2. Goodness of fit (%) with varying number of components (the number under each point denotes the number of components included in the analysis).

Fig. 3. Spatial scores of three components. (A) Component 1, (B) component 2, and (C) component 3 are arranged to match visual field locations (both eyes presented as the right eye). (D) Reference map of visual field locations. SN = superonasal; ST = superotemporal; IN = inferonasal; IT = inferotemporal.

Fig. 4. Temporal loadings of three components. (A) Component 1 showed clear linear pattern. (B) Component 2 and (C) component 3 did not show the same trend (time 1 = initial follow-up, time 6 = last follow-up).

Fig. 5. Histogram of subject loadings by group. PG subjects had wider and subnormal distributions compared to PPG subjects in (A) component 1, (B) component 2, and (C) component 3. (A) Component 1 showed clear variability. PPG = pre-perimetric glaucoma; PG = perimetric glaucoma.