Combined multi-modal assessment of glaucomatous damage with electroretinography and optical coherence tomography/angiography

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 tomog-raphy (OCT), OCT-angiography (OCT-A), and electrophysiological measures (multifocal photopic negative response ratio [mfPhNR] and steady-state pattern electroretinogra-phy [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 interre-lation 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 [rs ]: 0.58, P = 0.001; mfPhNR-pRNFL rs: 0.66, P < 0.001) was stronger than the functional-vascular correlation (mfPhNR-pfVD rs: 0.29, P = 0.13; mfPhNR-pVD rs: 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

Foveal pRF properties in the visual cortex depend on the extent of stimulated visual field

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 T 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 and are likely associated with methodological biases and/or physiological mechanisms. 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

CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism

Purpose: Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose.Methods: We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net.Results: The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts.Conclusions: CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics

Tracking the visual system—from the optic chiasm to primary visual cortex

&lt;jats:title&gt;Abstract&lt;/jats:title&gt;&lt;jats:p&gt;Epilepsy surgery is a well-established method of treatment for pharmacoresistant focal epilepsies, but it carries an inherent risk of damaging eloquent brain structures. This holds true in particular for visual system pathways, where the damage to, for example, the optic radiation may result in postoperative visual field defects. Such risk can be minimized by the identification and localization of visual pathways using diffusion magnetic resonance imaging (dMRI). The aim of this article is to provide an overview of the step-by-step process of reconstructing the visual pathways applying dMRI analysis. This includes data acquisition, preprocessing, identification of key structures of the visual system necessary for reconstruction, as well as diffusion modeling and the ultimate reconstruction of neural pathways. As a result, the reader will become familiar both with the ideas and challenges of imaging the visual system using dMRI and their relevance for planning the intervention. &lt;/jats:p&gt

CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism

Purpose: Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose. Methods: We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net. Results: The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts. Conclusions: CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics.</p