Recent advances in modeling oxygen supply to cortical brain tissue have begun to elucidate the functional mechanisms of neurovascular coupling. While the principal mechanisms of blood flow regulation after neuronal firing are generally known, mechanistic hemodynamic simulations cannot yet pinpoint the exact spatial and temporal coordination between the network of arteries, arterioles, capillaries and veins for the entire brain. Because of the potential significance of blood flow and oxygen supply simulations for illuminating spatiotemporal regulation inside the cortical microanatomy, there is a need to create mathematical models of the entire cerebral circulation with realistic anatomical detail. Our hypothesis is that an anatomically accurate reconstruction of the cerebrocirculatory architecture will inform about possible regulatory mechanisms of the neurovascular interface. In this article, we introduce large-scale networks of the murine cerebral circulation spanning the Circle of Willis, main cerebral arteries connected to the pial network down to the microcirculation in the capillary bed. Several multiscale models were generated from state-of-the-art neuroimaging data. Using a vascular network construction algorithm, the entire circulation of the middle cerebral artery was synthesized. Blood flow simulations indicate a consistent trend of higher hematocrit in deeper cortical layers, while surface layers with shorter vascular path lengths seem to carry comparatively lower red blood cell (RBC) concentrations. Moreover, the variability of RBC flux decreases with cortical depth. These results support the notion that plasma skimming serves a self-regulating function for maintaining uniform oxygen perfusion to neurons irrespective of their location in the blood supply hierarchy. Our computations also demonstrate the practicality of simulating blood flow for large portions of the mouse brain with existing computer resources. The efficient simulation of blood flow throughout the entire middle cerebral artery (MCA) territory is a promising milestone towards the final aim of predicting blood flow patterns for the entire brain.

Objective Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. Materials and Methods BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. Results We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Conclusions Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.

Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.

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Background Thyroid eye disease (TED) can result in eye-bulging (proptosis) and double-vision (diplopia) and inflammation, which frequently impacts quality of life (QoL). Teprotumumab, an insulin like growth factor-1 receptor inhibitory antibody, improves TED outcomes and QoL1 as measured by the Graves Ophthalmopathy-Quality of Life (GO-QoL) questionnaire and its appearance (AP) and visual function (VF) subscales. The primary factors driving QoL improvement in TED are unknown; therefore, we examined outcomes associated with improvement as observed in 2 placebo-controlled trials. Methods Data from Phase 2/3 placebo-controlled trials of teprotumumab were examined with a mixed-effect model with change in overall GO-QoL, AP, and VF scores as dependent variables to explain within-patient variability. Independent variables included demographics, visits, treatment, symptoms (Gorman diplopia scores [0-3], proptosis change (mm), spontaneous orbital pain, gaze-evoked orbital pain). Variability between subjects was tested over the 24-week study. Results Teprotumumab treatment significantly correlated with improved overall GO-QoL, VF and AP scores. Improvements in diplopia, proptosis, gaze-evoked and spontaneous orbital pain were associated with those in overall GO-QoL score (coefficient -4.01, -1.00, -31.21 and -4.37, respectively, all p<0.001). Improvements in diplopia scores and spontaneous orbital pain were significantly correlated with higher VF scores (coefficients -5.51 and -6.66, respectively, both p<0.001). Improvements in diplopia and proptosis correlated significantly with higher AP scores (coefficients -2.98, -1.62, both p<0.001). Patients with pain had lower AP scores (coefficient -38.21, p<0.001). Increasing age was positively correlated with higher GO-QoL AP scores (coefficient 0.41, p<0.001), but negatively correlated with GO-QoL VF scores (coefficient -0.29, p<0.001). Variability between subjects was considerable, accounting for >60% of random variance. Conclusions Improvements in diplopia, proptosis, and pain drove improvements in QoL. In older patients, changes in AP impacted QoL to a lesser degree, while reduced VF had a greater negative impact on QoL. References Kahaly et al, Lancet Diabetes and Endocrinol 2021; 9(6): 360-372 Presentation: Sunday, June 12, 2022 11:45 a.m. - 12:00 p.m.

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