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.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.

BACKGROUND: Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS: We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS: We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS: Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

In this study, we explored the relationship between glaucoma, the most common cause of blindness, and changes within the brain. We used data from diffusion MRI, a measurement method which assesses the properties of brain connections. We examined 905 individuals with glaucoma alongside 5292 healthy people. We refined the test cohort to be closely matched in age, sex, ethnicity, and socioeconomic backgrounds. The use of deep learning neural networks allowed accurate detection of glaucoma by focusing on the tissue properties of the optic radiations, a major brain pathway that transmits visual information, rather than other brain pathways used for comparison. Our work provides additional evidence that brain connections may age differently based on varying sensory inputs.

Optic radiations representing different eccentricities age differently.

The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.

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.

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.

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.

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.

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.

Exploring a Structural Basis for Delayed Rod-Mediated Dark Adaptation in Age-Related Macular Degeneration Via Deep Learning.

Purpose: Delayed rod-mediated dark adaptation (RMDA) is a functional biomarker for incipient age-related macular degeneration (AMD). We used anatomically restricted spectral domain optical coherence tomography (SD-OCT) imaging data to localize de novo imaging features associated with and to test hypotheses about delayed RMDA. Methods: Rod intercept time (RIT) was measured in participants with and without AMD at 5 degrees from the fovea, and macular SD-OCT images were obtained. A deep learning model was trained with anatomically restricted information using a single representative B-scan through the fovea of each eye. Mean-occlusion masking was utilized to isolate the relevant imaging features. Results: The model identified hyporeflective outer retinal bands on macular SD-OCT associated with delayed RMDA. The validation mean standard error (MSE) registered to the foveal B-scan localized the lowest error to 0.5 mm temporal to the fovea center, within an overall low-error region across the rod-free zone and adjoining parafovea. Mean absolute error (MAE) on the test set was 4.71 minutes (8.8% of the dynamic range). Conclusions: We report a novel framework for imaging biomarker discovery using deep learning and demonstrate its ability to identify and localize a previously undescribed biomarker in retinal imaging. The hyporeflective outer retinal bands in central macula on SD-OCT demonstrate a structural basis for dysfunctional rod vision that correlates to published histopathologic findings. Translational Relevance: This agnostic approach to anatomic biomarker discovery strengthens the rationale for RMDA as an outcome measure in early AMD clinical trials, and also expands the utility of deep learning beyond automated diagnosis to fundamental discovery.

Smoking Is Associated with Higher Intraocular Pressure Regardless of Glaucoma: A Retrospective Study of 12.5 Million Patients Using the Intelligent Research in Sight (IRIS®) Registry.

PURPOSE: To compare the average intraocular pressure (IOP) among smokers, past smokers, and never smokers using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry. DESIGN: Retrospective database study of the IRIS® Registry data. PARTICIPANTS: Intelligent Research in Sight Registry patients who were seen by an eye care provider during 2017. METHODS: Patients were divided into current smoker, past smoker, and never smoker categories. The IOP was based on an average measurement, and separate analyses were performed in patients with and without a glaucoma diagnosis based on International Classification of Diseases (Ninth Edition and Tenth Edition) codes. Stratified, descriptive statistics by glaucoma status were determined, and the relationship between smoking and IOP was assessed with a multivariate linear regression model. MAIN OUTCOME MEASURES: Mean IOP. RESULTS: A total of 12 535 013 patients were included. Compared with never smokers, current and past smokers showed a statistically significantly higher IOP by 0.92 mmHg (95% confidence interval [CI], 0.88-0.95 mmHg) and 0.77 mmHg (95% CI, 0.75-0.79 mmHg), respectively, after adjusting for age, gender, glaucoma, age-related macular degeneration, diabetic retinopathy, cataract, glaucoma surgery, cataract surgery, and first-order interactions. In addition, the difference in IOP between current and never smokers was the highest in the fourth decade, regardless of the glaucoma status (glaucoma group, 1.14 mmHg [95% CI, 1.00-1.29 mmHg]; without glaucoma group, 0.68 mmHg [95% CI, 0.65-0.71 mmHg]). CONCLUSIONS: Current smokers and past smokers have higher IOP than patients who never smoked. This difference is higher in patients with an underlying glaucoma diagnosis.

White Matter Lesions in Mild Cognitive Impairment and Idiopathic Parkinson's Disease: Multimodal Advanced MRI and Cognitive Associations.

Cerebrovascular disease is a common comorbidity in older adults, typically assessed in terms of white matter hyperintensities (WMHs) on MRI. While it is well known that WMHs exacerbate cognitive symptoms, the exact relation of WMHs with cognitive performance and other degenerative diseases is unknown. Furthermore, based on location, WMHs are often classified into periventricular and deep WMHs and are believed to have different pathological origins. Whether the two types of WMHs influence cognition differently is unclear. Using regression models, we assessed the independent association of these two types of WMHs with cognitive performance in two separate studies focused on distinct degenerative diseases, early Alzheimer's (mild cognitive impairment), and Parkinson's disease. We further tested if the two types of WMHs were differentially associated with reduced cortical cerebral blood flow (CBF) as measured by arterial spin labeling and increased mean diffusivity (MD, a marker of tissue injury) as measured by diffusion imaging. Our approach revealed that both deep and periventricular WMHs were associated with poor performance on tests of global cognition (Montreal cognitive Assessment, MoCA), task processing (Trail making test), and category fluency in the study of mild cognitive impairment. They were associated with poor performance in global cognition (MoCA) and category fluency in the Parkinson's disease study. Of note, more associations were detected between cognitive performance and deep WMHs than between cognitive performance and periventricular WMHs. Mechanistically, both deep and periventricular WMHs were associated with increased MD. Both deep and periventricular WMHs were also associated with reduced CBF in the gray matter.

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.

Spectral graph theory of brain oscillations.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .

An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography.

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.

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.

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers.

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps - evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.

Applying microstructural models to understand the role of white matter in cognitive development.

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development.

Sensory over-responsivity: parent report, direct assessment measures, and neural architecture.

Background: Sensory processing difficulties are common across neurodevelopmental disorders. Thus, reliable measures are needed to understand the biological underpinnings of these differences. This study aimed to define a scoring methodology specific to auditory (AOR) and tactile (TOR) over-responsivity. Second, in a pilot cohort using MRI Diffusion Tensor Imaging, we performed a proof of concept study of whether children with AOR showed measurable differences in their white matter integrity. Methods: This study included children with AOR and TOR from a mixed neurodevelopmental disorder cohort including autism and sensory processing dysfunction (n = 176) as well as neurotypical children (n = 128). We established cohorts based on sensory over-responsivity using parent report (Short Sensory Profile (SSP)) and direct assessment (Sensory Processing-Three Dimensions: Assessment (SP-3D:A)) measures. With a subset of the children (n = 39), group comparisons, based on AOR phenotype, were conducted comparing the white matter fractional anisotropy in 23 regions of interest. Results: Using direct assessment, 31% of the children with neurodevelopmental disorders had AOR and 27% had TOR. The inter-test agreement between SSP and SP-3D:A for AOR was 65% and TOR was 50%. Children with AOR had three white matter tracts showing decreased fractional anisotropy relative to children without AOR. Conclusions: This study identified cut-off scores for AOR and TOR using the SSP parent report and SP-3D:A observation. A combination of questionnaire and direct observation measures should be used in clinical and research settings. The SSP parent report and SP-3D:A direct observation ratings overlapped moderately for sensory related behaviors. Based on these preliminary structural neuroimaging results, we suggest a putative neural network may contribute to AOR.