Purpose To compare the diagnostic performance and to evaluate the interrelationship of electroretinographical and structural and vascular measures in glaucoma. Methods For 14 eyes of 14 healthy controls and 15 eyes of 12 patients with glaucoma ranging from preperimetric to advanced stages optical coherence tomography (OCT), OCT-angiography (OCT-A), and electrophysiological measures (multifocal photopic negative response ratio [mfPhNR] and steady-state pattern electroretinography [ssPERG]) were applied to assess changes in retinal structure, microvasculature, and function, respectively. The diagnostic performance was assessed via area-under-curve (AUC) measures obtained from receiver operating characteristics analyses. The interrelation of the different measures was assessed with correlation analyses. Results The mfPhNR, ssPERG amplitude, parafoveal (pfVD) and peripapillary vessel density (pVD), macular ganglion cell inner plexiform layer thickness (mGCIPL) and peripapillary retinal nerve fiber layer thickness (pRNFL) were significantly reduced in glaucoma. The AUC for mfPhNR was highest among diagnostic modalities (AUC: 0.88, 95% confidence interval: 0.75–1.0, P < 0.001), albeit not statistically different from that for macular (mGCIPL: 0.76, 0.58–0.94, P < 0.05; pfVD: 0.81, 0.65–0.97, P < 0.01) or peripapillary imaging (pRNFL: 0.85, 0.70–1.0, P < 0.01; pVD: 0.82, 0.68–0.97, P < 0.01). Combined functional/vascular measures yielded the highest AUC (mfPhNR-pfVD: 0.94, 0.85–1.0, P < 0.001). The functional/structural measure correlation (mfPhNR-mGCIPL correlation coefficient [r s ]: 0.58, P = 0.001; mfPhNR-pRNFL r s : 0.66, P < 0.001) was stronger than the functional-vascular correlation (mfPhNR-pfVD r s : 0.29, P = 0.13; mfPhNR-pVD r s : 0.54, P = 0.003). Conclusions The combination of ERG measures and OCT-A improved diagnostic performance and enhanced understanding of pathophysiology in glaucoma. Translational Relevance Multimodal assessment of glaucoma damage improves diagnostics and monitoring of disease progression.
Current initiatives to restore vision emphasize the need for objective assessments of visual field (VF) defects as pursued with functional magnetic resonance imaging (fMRI) approaches. Here, we compared population receptive field (pRF) mapping-based VF reconstructions to an fMRI method that uses more robust visual stimulation (on-off block design) in combination with individualized anatomy-driven retinotopic atlas-information (atlas-based VF). We investigated participants with sizable peripheral VF-deficits due to advanced glaucoma (n = 4) or retinitis pigmentosa (RP; n = 2) and controls (n = 6) with simulated scotoma. We obtained (1) standard automated perimetry (SAP) data as reference VFs and 3T fMRI data for (2) pRF-mapping [8-direction bar stimulus, fixation color change task] and (3) block-design full-field stimulation [8-direction drifting contrast patterns during (a) passive viewing (PV) and (b) one-back-task (OBT; reporting successions of identical motion directions) to probe the impact of previously reported task-related unspecific visual cortex activations]. Correspondence measures between the SAP and fMRI-based VFs were accuracy, assisted by sensitivity and specificity. We found an accuracy of pRF-based VF from V1 in patients [median: 0.62] that was similar to previous reports and increased by adding V2 and V3 to the analysis [0.74]. In comparison to the pRF-based VF, equivalent accuracies were obtained for the atlas-based VF for both PV [0.67] and, unexpectedly, the OBT [0.59], where, however, unspecific cortical activations were reflected by a reduction in sensitivity [0.71 (PV) and 0.35 (OBT)]. In conclusion, in patients with peripheral VF-defects, we demonstrate that previous fMRI procedures to obtain VF-estimates might be enhanced by: (1) pooling V1-V3 to enhance accuracy; (2) reporting sensitivity and specificity measures to increase transparency of the VF-reconstruction metric; (3) applying atlas-based procedures, if pRF-based VFs are not available or difficult to obtain; and (4) giving, counter-intuitively, preference to PV. These findings are expected to provide guidance to overcome current limitations of translating fMRI-based methods to a clinical work-up.
Previous studies demonstrated that alterations in functional MRI derived receptive field (pRF) properties in cortical projection zones of retinal lesions can erroneously be mistaken for cortical large-scale reorganization in response to visual system pathologies. We tested, whether such confounds are also evident in the normal cortical projection zone of the fovea for simulated peripheral visual field defects. We applied fMRI-based visual field mapping of the central visual field at 3 Tesla in eight controls to compare the pRF properties of the central visual field of a reference condition (stimulus radius: 14°) and two conditions with simulated peripheral visual field defect, i.e., with a peripheral gray mask, stimulating only the central 7° or 4° radius. We quantified, for the cortical representation of the actually stimulated visual field, the changes in the position and size of the pRFs associated with reduced peripheral stimulation using conventional and advanced pRF modeling. We found foveal pRF-positions (≤3°) to be significantly shifted towards the periphery (p<0.05, corrected). These pRF-shifts were largest for the 4° condition [visual area (mean eccentricity shift): V1 (0.9°), V2 (0.9°), V3 (1.0°)], but also evident for the 7° condition [V1 (0.5°), V2 (0.5°), V3 (0.9°)]. Further, an overall enlargement of pRF-sizes was observed. These findings indicate the dependence of foveal pRF parameters on the spatial extent of the stimulated visual field. Consequently, our results imply that, previously reported similar findings in patients with actual peripheral scotomas need to be interpreted with caution and indicate the need for adequate control conditions in investigations of visual cortex reorganization.
Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (n = 1049) from the Human Connectome Project (HCP) repository and automatically generated algorithm-based optic chiasm masks. The trained CNN was subsequently tested on data of persons with albinism (PWA; n = 9) and controls (n = 8) from the CHIASM repository. The quality of outcome segmentation was assessed via the comparison to manually defined optic chiasm masks using the Dice similarity coefficient (DSC). The results revealed contrasting quality of masks obtained for control (mean DSC ± SEM = 0.75 ± 0.03) and PWA data (0.43 ± 0.8, few-corrected p = 0.04). The fact that the CNN recognition of the optic chiasm fails for chiasm abnormalities in PWA underlines the fundamental differences in their spatial features. This finding provides proof of concept for a novel deep-learning-based diagnostics approach of chiasmal misrouting from T1w images, as well as further analyses on chiasmal misrouting and their impact on the structure and function of the visual system.
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