Endophthalmitis Rate in Immediately Sequential versus Delayed Sequential Bilateral Cataract Surgery within the Intelligent Research in Sight (IRIS®) Registry Data.

PURPOSE: To compare the rate of postoperative endophthalmitis after immediately sequential bilateral cataract surgery (ISBCS) versus delayed sequential bilateral cataract surgery (DSBCS) using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry database. DESIGN: Retrospective cohort study. PARTICIPANTS: Patients in the IRIS Registry who underwent cataract surgery from 2013 through 2018. METHODS: Patients who underwent cataract surgery were divided into 2 groups: (1) ISBCS and (2) DSBCS (second-eye surgery ≥1 day after the first-eye surgery) or unilateral surgery. Postoperative endophthalmitis was defined as endophthalmitis occurring within 4 weeks of surgery by International Classification of Diseases (ICD) code and ICD code with additional clinical criteria. MAIN OUTCOME MEASURES: Rate of postoperative endophthalmitis. RESULTS: Of 5 573 639 IRIS Registry patients who underwent cataract extraction, 165 609 underwent ISBCS, and 5 408 030 underwent DSBCS or unilateral surgery (3 695 440 DSBCS, 1 712 590 unilateral surgery only). A total of 3102 participants (0.056%) met study criteria of postoperative endophthalmitis with supporting clinical findings. The rates of endophthalmitis in either surgery eye between the 2 surgery groups were similar (0.059% in the ISBCS group vs. 0.056% in the DSBCS or unilateral group; P = 0.53). Although the incidence of endophthalmitis was slightly higher in the ISBCS group compared with the DSBCS or unilateral group, the odds ratio did not reach statistical significance (1.08; 95% confidence interval, 0.87-1.31; P = 0.47) after adjusting for age, sex, race, insurance status, and comorbid eye disease. Seven cases of bilateral endophthalmitis with supporting clinical data in the DSBCS group and no cases in the ISBCS group were identified. CONCLUSIONS: Risk of postoperative endophthalmitis was not statistically significantly different between patients who underwent ISBCS and DSBCS or unilateral cataract surgery.

Myelin development in cerebral gray and white matter during adolescence and late childhood.

Myelin development during adolescence is becoming an area of growing interest in view of its potential relationship to cognition, behavior, and learning. While recent investigations suggest that both white matter (WM) and gray matter (GM) undergo protracted myelination during adolescence, quantitative relations between myelin development in WM and GM have not been previously studied. We quantitatively characterized the dependence of cortical GM, WM, and subcortical myelin density across the brain on age, gender, and puberty status during adolescence with the use of a novel macromolecular proton fraction (MPF) mapping method. Whole-brain MPF maps from a cross-sectional sample of 146 adolescents (age range 9-17 years) were collected. Myelin density was calculated from MPF values in GM and WM of all brain lobes, as well as in subcortical structures. In general, myelination of cortical GM was widespread and more significantly correlated with age than that of WM. Myelination of GM in the parietal lobe was found to have a significantly stronger age dependence than that of GM in the frontal, occipital, temporal and insular lobes. Myelination of WM in the temporal lobe had the strongest association with age as compared to WM in other lobes. Myelin density was found to be higher in males as compared to females when averaged across all cortical lobes, as well as in a bilateral subcortical region. Puberty stage was significantly correlated with myelin density in several cortical areas and in the subcortical GM. These findings point to significant differences in the trajectories of myelination of GM and WM across brain regions and suggest that cortical GM myelination plays a dominant role during adolescent development.

Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.

BACKGROUND: Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. OBJECTIVE: To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND METHODS: Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. RESULTS: Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. CONCLUSION: In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI.

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25 = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.

Brain MR Imaging Findings and Associated Outcomes in Carriers of the Reciprocal Copy Number Variation at 16p11.2.

Purpose To identify developmental neuroradiologic findings in a large cohort of carriers who have deletion and duplication at 16p11.2 (one of the most common genetic causes of autism spectrum disorder [ASD]) and assess how these features are associated with behavioral and cognitive outcomes. Materials and Methods Seventy-nine carriers of a deletion at 16p11.2 (referred to as deletion carriers; age range, 1-48 years; mean age, 12.3 years; 42 male patients), 79 carriers of a duplication at 16p11.2 (referred to as duplication carriers; age range, 1-63 years; mean age, 24.8 years; 43 male patients), 64 unaffected family members (referred to as familial noncarriers; age range, 1-46 years; mean age, 11.7 years; 31 male participants), and 109 population control participants (age range, 6-64 years; mean age, 25.5 years; 64 male participants) were enrolled in this cross-sectional study. Participants underwent structural magnetic resonance (MR) imaging and completed cognitive and behavioral tests. MR images were reviewed for development-related abnormalities by neuroradiologists. Differences in frequency were assessed with a Fisher exact test corrected for multiple comparisons. Unsupervised machine learning was used to cluster radiologic features and an association between clusters and cognitive and behavioral scores from IQ testing, and parental measures of development were tested by using analysis of covariance. Volumetric analysis with automated segmentation was used to confirm radiologic interpretation. Results For deletion carriers, the most prominent features were dysmorphic and thicker corpora callosa compared with familial noncarriers and population control participants (16%; P < .001 and P < .001, respectively) and a greater likelihood of cerebellar tonsillar ectopia (30.7%; P < .002 and P < .001, respectively) and Chiari I malformations (9.3%; P < .299 and P < .002, respectively). For duplication carriers, the most salient findings compared with familial noncarriers and population control participants were reciprocally thinner corpora callosa (18.6%; P < .003 and P < .001, respectively), decreased white matter volume (22.9%; P < .001, and P < .001, respectively), and increased ventricular volume (24.3%; P < .001 and P < .001, respectively). By comparing cognitive assessments to imaging findings, the presence of any imaging feature associated with deletion carriers indicated worse daily living, communication, and social skills compared with deletion carriers without any radiologic abnormalities (P < .005, P < .002, and P < .004, respectively). For the duplication carriers, presence of decreased white matter, callosal volume, and/or increased ventricle size was associated with decreased full-scale and verbal IQ scores compared with duplication carriers without these findings (P < .007 and P < .004, respectively). Conclusion In two genetically related cohorts at high risk for ASD, reciprocal neuroanatomic abnormalities were found and determined to be associated with cognitive and behavioral impairments. © RSNA, 2017 Online supplemental material is available for this article.

Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease.

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

Reciprocal white matter alterations due to 16p11.2 chromosomal deletions versus duplications.

Copy number variants at the 16p11.2 chromosomal locus are associated with several neuropsychiatric disorders, including autism, schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and speech and language disorders. A gene dosage dependence has been suggested, with 16p11.2 deletion carriers demonstrating higher body mass index and head circumference, and 16p11.2 duplication carriers demonstrating lower body mass index and head circumference. Here, we use diffusion tensor imaging to elucidate this reciprocal relationship in white matter organization, showing widespread increases of fractional anisotropy throughout the supratentorial white matter in pediatric deletion carriers and, in contrast, extensive decreases of white matter fractional anisotropy in pediatric and adult duplication carriers. We find associations of these white matter alterations with cognitive and behavioral impairments. We further demonstrate the value of imaging metrics for characterizing the copy number variant phenotype by employing linear discriminant analysis to predict the gene dosage status of the study subjects. These results show an effect of 16p11.2 gene dosage on white matter microstructure, and further suggest that opposite changes in diffusion tensor imaging metrics can lead to similar cognitive and behavioral deficits. Given the large effect sizes found in this study, our results support the view that specific genetic variations are more strongly associated with specific brain alterations than are shared neuropsychiatric diagnoses. Hum Brain Mapp 37:2833-2848, 2016. © 2016 Wiley Periodicals, Inc.

Aberrant white matter microstructure in children with 16p11.2 deletions.

Copy number variants (CNVs) of the chromosomal locus 16p11.2, consisting of either deletions or duplications, have been implicated in autism, schizophrenia, epilepsy, and other neuropsychiatric disorders. Since abnormal white matter microstructure can be seen in these more broadly defined clinical disorders, we used diffusion magnetic resonance imaging and tract-based spatial statistics to investigate white matter microstructural integrity in human children with 16p11.2 deletions. We show that deletion carriers, compared with typically developing matched controls, have increased axial diffusivity (AD) in many major central white matter tracts, including the anterior corpus callosum as well as bilateral internal and external capsules. Higher AD correlated with lower nonverbal IQ in the deletion carriers, but not controls. Increases in fractional anisotropy and mean diffusivity were also found in some of the same tracts with elevated AD. Closer examination with neurite orientation dispersion and density imaging revealed that fiber orientation dispersion was decreased in some central white matter tracts. Notably, these alterations of white matter are unlike microstructural differences reported for any other neurodevelopmental disorders, including autism spectrum disorders that have phenotypic overlap with the deletion carriers. These findings suggest that deletion of the 16p11.2 locus is associated with a unique widespread pattern of aberrant white matter microstructure that may underlie the impaired cognition characteristic of this CNV.

Edge density imaging: mapping the anatomic embedding of the structural connectome within the white matter of the human brain.

The structural connectome has emerged as a powerful tool to characterize the network architecture of the human brain and shows great potential for generating important new biomarkers for neurologic and psychiatric disorders. The edges of the cerebral graph traverse white matter to interconnect cortical and subcortical nodes, although the anatomic embedding of these edges is generally overlooked in the literature. Mapping the paths of the connectome edges could elucidate the relative importance of individual white matter tracts to the overall network topology of the brain and also lead to a better understanding of the effect of regionally-specific white matter pathology on cognition and behavior. In this work, we introduce edge density imaging (EDI), which maps the number of network edges that pass through every white matter voxel. Test-retest analysis shows good to excellent reliability for edge density (ED) measurements, with consistent results using different cortical and subcortical parcellation schemes and different diffusion MR imaging acquisition parameters. We also demonstrate that ED yields complementary information to both traditional and emerging voxel-wise metrics of white matter microstructure and connectivity, including fractional anisotropy, track density, fiber orientation dispersion and neurite density. Our results demonstrate spatially ordered variations of ED throughout the white matter, notably including greater ED in posterior than anterior cerebral white matter. The EDI framework is employed to map the white matter regions that are enriched with pathways connecting rich club nodes and also those with high densities of intra-modular and inter-modular edges. We show that periventricular white matter has particularly high ED and high densities of rich club edges, which is significant for diseases in which these areas are selectively affected, ranging from white matter injury of prematurity in infants to leukoaraiosis in the elderly. Using edge betweenness centrality, we identify specific white matter regions involved in a large number of shortest paths, some containing highly connected rich club edges while others are relatively isolated within individual modules. Overall, these findings reveal an intricate relationship between white matter anatomy and the structural connectome, motivating further exploration of EDI for biomarkers of cognition and behavior.

Stochastic geometric network models for groups of functional and structural connectomes.

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. The current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of network density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high "smallworldness" beyond that arising from geometric and degree considerations alone.

Outer Retinal Thickness Is Associated With Cognitive Function in Normal Aging to Intermediate Age-Related Macular Degeneration.

Purpose: Research on Alzheimer's disease (AD) and precursor states demonstrates a thinner retinal nerve fiber layer (NFL) compared to age-similar controls. Because AD and age-related macular degeneration (AMD) both impact older adults and share risk factors, we asked if retinal layer thicknesses, including NFL, are associated with cognition in AMD. Methods: Adults ≥ 70 years with normal retinal aging, early AMD, or intermediate AMD per Age-Related Eye Disease Study (AREDS) nine-step grading of color fundus photography were enrolled in a cross-sectional study. Optical coherence tomography (OCT) volumes underwent 11-line segmentation and adjustments by a trained operator. Evaluated thicknesses reflect the vertical organization of retinal neurons and two vascular watersheds: NFL, ganglion cell layer-inner plexiform layer complex (GCL-IPL), inner retina, outer retina (including retinal pigment epithelium-Bruch's membrane), and total retina. Thicknesses were area weighted to achieve mean thickness across the 6-mm-diameter Early Treatment of Diabetic Retinopathy Study (ETDRS) grid. Cognitive status was assessed by the National Institutes of Health Toolbox cognitive battery for fluid and crystallized cognition. Correlations estimated associations between cognition and thicknesses, adjusting for age. Results: Based on 63 subjects (21 per group), thinning of the outer retina was significantly correlated with lower cognition scores (P < 0.05). No other retinal thickness variables were associated with cognition. Conclusions: Only the outer retina (photoreceptors, supporting glia, retinal pigment epithelium, Bruch's membrane) is associated with cognition in aging to intermediate AMD; NFL was not associated with cognition, contrary to AD-associated condition reports. Early and intermediate AMD constitute a retinal disease whose earliest, primary impact is in the outer retina. Our findings hint at a unique impact on the brain from the outer retina in persons with AMD.

The structural connectome of the human brain in agenesis of the corpus callosum.

Adopting a network perspective, the structural connectome reveals the large-scale white matter connectivity of the human brain, yielding insights into cerebral organization otherwise inaccessible to researchers and clinicians. Connectomics has great potential for elucidating abnormal connectivity in congenital brain malformations, especially axonal pathfinding disorders. Agenesis of the corpus callosum (AgCC) is one of the most common brain malformations and can also be considered a prototypical genetic disorder of axonal guidance in humans. In this exploratory study, the structural connectome of AgCC is mapped and compared to that of the normal human brain. Multiple levels of granularity of the AgCC connectome are investigated, including summary network metrics, modularity analysis, and network consistency measures, with comparison to the normal structural connectome after simulated removal of all callosal connections ("virtual callostomy"). These investigations reveal four major findings. First, global connectivity is abnormally reduced in AgCC, but local connectivity is increased. Second, the network topology of AgCC is more variable than that of the normal human connectome, contradicting the predictions of the virtual callostomy model. Third, modularity analysis reveals that many of the tracts that comprise the structural core of the cerebral cortex have relatively weak connectivity in AgCC, especially the cingulate bundles bilaterally. Finally, virtual lesions of the Probst bundles in the AgCC connectome demonstrate that there is consistency across subjects in many of the connections generated by these ectopic white matter tracts, and that they are a mixture of cortical and subcortical fibers. These results go beyond prior diffusion tractography studies to provide a systems-level perspective on anomalous connectivity in AgCC. Furthermore, this work offers a proof of principle for the utility of the connectome framework in neurodevelopmental disorders.

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data.

Resting functional connectivity in patients with brain tumors in eloquent areas.

OBJECTIVE: Resection of brain tumors adjacent to eloquent areas represents a challenge in neurosurgery. If maximal resection is desired without inducing postoperative neurological deficits, a detailed knowledge of the functional topography in and around the tumor is crucial. The aim of the present work is to evaluate the value of preoperative magnetoencephalography (MEG) imaging of functional connectivity to predict the results of intraoperative electrical stimulation (IES) mapping, the clinical gold standard for neurosurgical localization of functional areas. METHODS: Resting-state whole-cortex MEG recordings were obtained from 57 consecutive subjects with focal brain tumors near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering algorithms, and the mean imaginary coherence between the rest of the brain and voxels in and around brain tumors were compared to the mean imaginary coherence between the rest of the brain and contralesional voxels as an index of functional connectivity. IES mapping was performed in all subjects. The cortical connectivity pattern near the tumor was compared to the IES results. RESULTS: Maps with decreased resting-state functional connectivity in the entire tumor area had a negative predictive value of 100% for absence of eloquent cortex during IES. Maps showing increased resting-state functional connectivity within the tumor area had a positive predictive value of 64% for finding language, motor, or sensory cortical sites during IES mapping. INTERPRETATION: Preoperative resting state MEG connectivity analysis is a useful noninvasive tool to evaluate the functionality of the tissue surrounding tumors within eloquent areas, and could potentially contribute to surgical planning and patient counseling.

Measuring functional connectivity using MEG: methodology and comparison with fcMRI.

Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the ß band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response.

Refractive Outcomes After Immediate Sequential vs Delayed Sequential Bilateral Cataract Surgery.

Importance: Approximately 2 million cataract operations are performed annually in the US, and patterns of cataract surgery delivery are changing to meet the increasing demand. Therefore, a comparative analysis of visual acuity outcomes after immediate sequential bilateral cataract surgery (ISBCS) vs delayed sequential bilateral cataract surgery (DSBCS) is important for informing future best practices. Objective: To compare refractive outcomes of patients who underwent ISBCS, short-interval (1-14 days between operations) DSBCS (DSBCS-14), and long-interval (15-90 days) DSBCS (DSBCS-90) procedures. Design, Setting, and Participants: This retrospective cohort study used population-based data from the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry. A total of 1 824 196 IRIS Registry participants with bilateral visual acuity measurements who underwent bilateral cataract surgery were assessed. Exposures: Participants were divided into 3 groups (DSBCS-90, DSBCS-14, and ISBCS groups) based on the timing of the second eye surgery. Univariable and multivariable linear regression models were used to analyze the refractive outcomes of the first and second surgery eye. Main Outcomes and Measures: Mean postoperative uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) after cataract surgery. Results: This study analyzed data from 1 824 196 patients undergoing bilateral cataract surgery (mean [SD] age for those <87 years, 70.03 [7.77]; 684 916 [37.5%] male). Compared with the DSBCS-90 group, after age, self-reported race, insurance status, history of age-related macular degeneration, diabetic retinopathy, and glaucoma were controlled for, the UCVA of the first surgical eye was higher by 0.41 (95% CI, 0.36-0.45; P < .001) letters, and the BCVA was higher by 0.89 (95% CI, 0.86-0.92; P < .001) letters in the DSBCS-14 group, whereas in the ISBCS group, the UCVA was lower by 2.79 (95% CI, -2.95 to -2.63; P < .001) letters and the BCVA by 1.64 (95% CI, -1.74 to -1.53; P < .001) letters. Similarly, compared with the DSBCS-90 group for the second eye, in the DSBCS-14 group, the UCVA was higher by 0.79 (95% CI, 0.74-0.83; P < .001) letters and the BCVA by 0.48 (95% CI, 0.45-0.51; P < .001) letters, whereas in the ISBCS group, the UCVA was lower by -1.67 (95% CI, -1.83 to -1.51; P < .001) letters and the BCVA by -1.88 (95% CI, -1.98 to -1.78; P < .001) letters. Conclusions and Relevance: The results of this cohort study of patients in the IRIS Registry suggest that compared with DSBCS-14 or DSBCS-90, ISBCS is associated with worse visual outcomes, which may or may not be clinically relevant, depending on patients' additional risk factors. Nonrandom surgery group assignment, confounding factors, and large sample size could account for the small but statistically significant differences noted. Further studies are warranted to determine whether these factors should be considered clinically relevant when counseling patients before cataract surgery.

PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements.

PURPOSE: To develop a deep learning semantic segmentation network to automate the assessment of 8 periorbital measurements DESIGN: Development and validation of an artificial intelligence (AI) segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. These data were used to develop a post-processing algorithm that measured margin reflex distance (MRD) 1 and 2, medial canthal height (MCH), lateral canthal height (LCH), medial brow height (MBH), lateral brow height (LBH), medial intercanthal distance (MID), and lateral intercanthal distance (LID). The algorithm validity was evaluated on a prospective hold-out test set against 3 graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland-Altman analysis. A smartphone video was also segmented and evaluated as proof of concept. RESULTS: The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases, demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured. CONCLUSIONS: We present, to our knowledge, the first open-sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes.

Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data.

Purpose: To evaluate the performance of a federated learning framework for deep neural network-based retinal microvasculature segmentation and referable diabetic retinopathy (RDR) classification using OCT and OCT angiography (OCTA). Design: Retrospective analysis of clinical OCT and OCTA scans of control participants and patients with diabetes. Participants: The 153 OCTA en face images used for microvasculature segmentation were acquired from 4 OCT instruments with fields of view ranging from 2 × 2-mm to 6 × 6-mm. The 700 eyes used for RDR classification consisted of OCTA en face images and structural OCT projections acquired from 2 commercial OCT systems. Methods: OCT angiography images used for microvasculature segmentation were delineated manually and verified by retina experts. Diabetic retinopathy (DR) severity was evaluated by retinal specialists and was condensed into 2 classes: non-RDR and RDR. The federated learning configuration was demonstrated via simulation using 4 clients for microvasculature segmentation and was compared with other collaborative training methods. Subsequently, federated learning was applied over multiple institutions for RDR classification and was compared with models trained and tested on data from the same institution (internal models) and different institutions (external models). Main Outcome Measures: For microvasculature segmentation, we measured the accuracy and Dice similarity coefficient (DSC). For severity classification, we measured accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, balanced accuracy, F1 score, sensitivity, and specificity. Results: For both applications, federated learning achieved similar performance as internal models. Specifically, for microvasculature segmentation, the federated learning model achieved similar performance (mean DSC across all test sets, 0.793) as models trained on a fully centralized dataset (mean DSC, 0.807). For RDR classification, federated learning achieved a mean AUROC of 0.954 and 0.960; the internal models attained a mean AUROC of 0.956 and 0.973. Similar results are reflected in the other calculated evaluation metrics. Conclusions: Federated learning showed similar results to traditional deep learning in both applications of segmentation and classification, while maintaining data privacy. Evaluation metrics highlight the potential of collaborative learning for increasing domain diversity and the generalizability of models used for the classification of OCT data.

Student becomes teacher: training faster deep learning lightweight networks for automated identification of optical coherence tomography B-scans of interest using a student-teacher framework.

This work explores a student-teacher framework that leverages unlabeled images to train lightweight deep learning models with fewer parameters to perform fast automated detection of optical coherence tomography B-scans of interest. Twenty-seven lightweight models (LWMs) from four families of models were trained on expert-labeled B-scans (∼70 K) as either "abnormal" or "normal", which established a baseline performance for the models. Then the LWMs were trained from random initialization using a student-teacher framework to incorporate a large number of unlabeled B-scans (∼500 K). A pre-trained ResNet50 model served as the teacher network. The ResNet50 teacher model achieved 96.0% validation accuracy and the validation accuracy achieved by the LWMs ranged from 89.6% to 95.1%. The best performing LWMs were 2.53 to 4.13 times faster than ResNet50 (0.109s to 0.178s vs. 0.452s). All LWMs benefitted from increasing the training set by including unlabeled B-scans in the student-teacher framework, with several models achieving validation accuracy of 96.0% or higher. The three best-performing models achieved comparable sensitivity and specificity in two hold-out test sets to the teacher network. We demonstrated the effectiveness of a student-teacher framework for training fast LWMs for automated B-scan of interest detection leveraging unlabeled, routinely-available data.

Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG.

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.