Publications

PURPOSE: To evaluate discrete wavelet transform (DWT) features as quantitative biomarkers of macular cone function from pattern electroretinography (PERG) in macular-predominant inherited retinal diseases (mpIRDs).

METHODS: In total, 486 PERG recordings from 123 participants were obtained from the PERG-Institute of Applied Ophthalmobiology open-access data set and analyzed. Twenty mother wavelets were screened with an energy-to-entropy ratio criterion; six (haar, sym2, sym4, db4, coif1, fk4) were retained for feature generation. After feature cleaning and correlation pruning, a final set of 141 features was obtained and averaged per participant to avoid visit bias. Group separation was assessed with nonparametric statistics. Inverse-DWT signal reconstruction was performed with the sym2 wavelet to algorithmically determine time-frequency indices needed to preserve N35, P50, and N95 peaks. The smallest set of indices that achieved this was retained.

RESULTS: Sym2-D6-2 (38-75 ms, 13-27 Hz) emerged as the top discriminative feature (res = 0.644, common-language effect size = 0.875) and correlated strongly with the clinical macular cone marker |P50-N35| (rcorr = 0.95) across 67 normal participants (262 recordings). Compared with |P50-N35|, the same index showed tighter, nonoverlapping group distributions, a higher diagnostic area under the curve (0.875 vs. 0.835), and a larger effect size (res = 0.644 vs. 0.576).

CONCLUSIONS: DWT-derived time-frequency features, particularly sym2-D6-2, provide robust, multidimensional biomarkers of macular cone function. These quantitative endpoints hold promise for monitoring disease progression and evaluating therapeutics in mpIRDs.

TRANSLATIONAL RELEVANCE: Sym2-D6-2 provides an objective metric of macular cone function that could serve as a quantitative endpoint in mpIRD trials.

Studies of visual face processing often use flat images as proxies for real faces due to their ease of manipulation and experimental control. Although flat images capture many features of a face, they lack the rich three-dimensional (3D) structural information available when binocularly viewing real faces (e.g., binocular cues to a long nose). We used functional magnetic resonance imaging to investigate the contribution of naturalistic binocular depth information to univariate activation levels and multivariate activation patterns in depth- and face-selective human brain regions. We used two cameras to capture images of real people from the viewpoints of the two eyes. These images were presented with natural viewing geometry (such that the size, distance, and binocular disparities were comparable to a real face at a typical viewing distance). Participants viewed stereopairs under four conditions: accurate binocular disparity (3D), zero binocular disparity (two-dimensional [2D]), reversed binocular disparity (pseudoscopic 3D), and no binocular disparity (monocular 2D). Although 3D faces (both 3D and pseudoscopic 3D) elicited higher activation levels than 2D faces, as well as distinct activation patterns, in depth-selective occipitoparietal regions (V3A, V3B, IPS0, IPS1, hMT+), face-selective occipitotemporal regions (OFA, FFA, pSTS) showed limited sensitivity to internal facial disparities. These results suggest that 2D images are a reasonable proxy for studying the neural basis of face recognition in face-selective regions, although contributions from 3D structural processing within the dorsal visual stream warrant further consideration.