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Chandra Sekaran, N. V.

Publications and source records attributed to Chandra Sekaran, N. V..

2 recordsLinked to original sources

Highly branched and complementary distributions oflayer 5 and layer 6 auditory corticofugal axons in mouse.

The auditory cortex (AC) exerts a powerful, yet heterogeneous, effect on its subcortical targets. Auditory corticofugal projections emanate from distinct bands in layers 5 (L5) and 6 (L6), which have complementary anatomical and physiological properties. While several studies have suggested that corticofugal projections from L5 branch widely, others have suggested that there are multiple, mostly independent sets of L5 corticofugal projections. Even less is known about L6; no studies have examined whether the various L6 corticofugal projections are independent. Therefore, we examined branching patterns of L5 and L6 auditory corticofugal neurons, using the corticocollicular system as an index projection, using both traditional and novel approaches. We first confirmed that dual retrograde injections into the mouse inferior colliculus and auditory thalamus co-labeled subpopulations of L5 and L6 AC neurons. We then used an intersectional approach to selectively re-label L5 or L6 corticocollicular somata and found that both layers sent extensive branches to striatum, amygdala, superior colliculus, thalamus and nuclei of the lateral lemniscus. L5 corticocollicular axons also sent an unpaired projection to the superior olivary complex. Using a novel approach to separately label L5 and L6 axons in the same mouse, we found that L5/6 terminal distributions partially spatially overlapped and that a subset of giant terminals was only found in L5-derived axons. Overall, the high degree of branching and complementarity in the distributions of L5 vs. L6 axons suggest that corticofugal projections should be considered as two widespread systems of projections, rather than a collection of individual projections.

neuroscience↗

Contrast-free Super-resolution Doppler (CS Doppler) based on Deep Generative Neural Networks

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

bioengineering↗