Subcortical connectivity correlates selectively with attention's effects on spatial choice bias

Neural mechanisms of attention are extensively studied in the neocortex; comparatively little is known about how subcortical regions contribute to attention. The superior colliculus (SC) is an evolutionarily conserved, subcortical (midbrain) structure that has been implicated in controlling visuospatial attention. Yet how the SC contributes mechanistically to attention remains unknown. We investigated the role of the SC in attention, combining model-based psychophysics, diffusion imaging, and tractography in human participants. Specifically, we asked whether the SC contributes to enhancing sensitivity (d') to attended information, or whether it contributes to biasing choices (criteria) in favor of attended information. We tested human participants on a multialternative change detection task, with endogenous spatial cueing, and quantified sensitivity and bias with a recently developed multidimensional signal detection model (m-ADC model). At baseline, sensitivity and bias exhibited complementary patterns of asymmetries across the visual hemifields: While sensitivity was consistently higher for detecting changes in the left hemifield, bias was higher for reporting changes in the right hemifield. Remarkably, white matter connectivity of the SC with the neocortex mirrored this pattern of asymmetries. Specifically, the asymmetry in SC-cortex connectivity correlated with the asymmetry in choice bias, but not in sensitivity. In addition, SC-cortex connectivity strength could predict cueing-induced modulation of bias, but not of sensitivity, across individuals. In summary, the SC may be a key node in an evolutionarily conserved network for controlling choice bias during visuospatial attention

ReAl-LiFE: Accelerating the Discovery of Individualized Brain Connectomes on GPUs

Diffusion imaging and tractography enable mapping structural connections in the human brain, in-vivo. Linear Fascicle Evaluation (LiFE) is a state-of-the-art approach for pruning spurious connections in the estimated structural connectome, by optimizing its fit to the measured diffusion data. Yet, LiFE imposes heavy demands on computing time, precluding its use in analyses of large connectome databases. Here, we introduce a GPU-based implementation of LiFE that achieves 50-100x speedups over conventional CPU-based implementations for connectome sizes of up to several million fibers. Briefly, the algorithm accelerates generalized matrix multiplications on a compressed tensor through efficient GPU kernels, while ensuring favorable memory access patterns. Leveraging these speedups, we advance LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights during connectome pruning. Our regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal. We demonstrate the utility of our approach by classifying pathological signatures of structural connectivity in patients with Alzheimer’s Disease (AD). We estimated million fiber whole-brain connectomes, followed by pruning with ReAl-LiFE, for 90 individuals (45 AD patients and 45 healthy controls). Linear classifiers, based on support vector machines, achieved over 80% accuracy in classifying AD patients from healthy controls based on their ReAl-LiFE pruned structural connectomes alone. Moreover, classification based on the ReAl-LiFE pruned connectome outperformed both the unpruned connectome, as well as the LiFE pruned connectome, in terms of accuracy. We propose our GPU-accelerated approach as a widely relevant tool for non-negative least squares optimization, across many domains

Congenital rubella syndrome surveillance in India, 2016–21: Analysis of five years surveillance data

Background: In India, facility-based surveillance for congenital rubella syndrome (CRS) was initiated in 2016 to estimate the burden and monitor the progress made in rubella control. We analyzed the surveillance data for 2016–2021 from 14 sentinel sites to describe the epidemiology of CRS. Method: We analyzed the surveillance data to describe the distribution of suspected and laboratory confirmed CRS patients by time, place and person characteristics. We compared clinical signs of laboratory confirmed CRS and discarded case-patients to find independent predictors of CRS using logistic regression analysis and developed a risk prediction model. Results: During 2016–21, surveillance sites enrolled 3940 suspected CRS case-patients (Age 3.5 months, SD: 3.5). About one-fifth (n = 813, 20.6%) were enrolled during newborn examination. Of the suspected CRS patients, 493 (12.5%) had laboratory evidence of rubella infection. The proportion of laboratory confirmed CRS cases declined from 26% in 2017 to 8.7% in 2021. Laboratory confirmed patients had higher odds of having hearing impairment (Odds ratio [OR] = 9.5, 95% confidence interval [CI]: 5.6–16.2), cataract (OR = 7.8, 95% CI: 5.4–11.2), pigmentary retinopathy (OR = 6.7, 95 CI: 3.3–13.6), structural heart defect with hearing impairment (OR = 3.8, 95% CI: 1.2–12.2) and glaucoma (OR = 3.1, 95% CI: 1.2–8.1). Nomogram, along with a web version, was developed. Conclusions: Rubella continues to be a significant public health issue in India. The declining trend of test positivity among suspected CRS case-patients needs to be monitored through continued surveillance in these sentinel sites