Biology subjects

Gupta, U.

Publications and source records attributed to Gupta, U..

4 recordsLinked to original sources

bioRxiv2024

A disinhibitory basal forebrain to cortex projection supports sustained attention

Sustained attention, as an essential cognitive faculty governing selective sensory processing, exhibits remarkable temporal fluctuations. However, the underlying neural circuits and computational mechanisms driving moment-to-moment attention fluctuations remain elusive. Here we demonstrate that cortex-projecting basal forebrain parvalbumin-expressing inhibitory neurons (BF-PV) mediate sustained attention in mice performing an attention task. BF-PV activity predicts the fluctuations of attentional performance metrics [-] reaction time and accuracy [-] trial-by-trial, and optogenetic activation of these neurons enhances performance. BF-PV neurons also respond to motivationally salient events, such as predictive cues, rewards, punishments, and surprises, which a computational model explains as representing motivational salience for allocating attention over time. Furthermore, we found that BF-PV neurons produce cortical disinhibition by inhibiting cortical PV+ inhibitory neurons, potentially underpinning the observed attentional gain modulation in the cortex. These findings reveal a disinhibitory BF-to-cortex projection that regulates cortical gain based on motivational salience, thereby promoting sustained attention. HIGHLIGHTSO_LIBF-PV activity predicts attentional performance metrics: reaction time and accuracy C_LIO_LIBF-PV responses reflect the computation of motivational salience-guided attention allocation C_LIO_LIOptogenetic activation of BF-PV neurons improves attentional performance C_LIO_LIBF-PV neurons produce cortical disinhibition through topographic projections and mediate gain modulation C_LI

neuroscience↗

bioRxiv2024

Endomucin knockout leads to delayed retinal vascular development and reduced ocular pathological neovascularization

Endomucin (EMCN), an endothelial-specific glycocalyx component highly expressed in capillary and venous endothelium, plays a critical role in regulating VEGF receptor 2 (VEGFR2) endocytosis and downstream VEGF signaling. Using the first global EMCN knockout mouse model, we investigated the effects of EMCN deficiency on retinal vascularization during development and pathological angiogenesis. We found relatively high expression of EMCN in choroidal capillaries and retinal vasculature. Emcn-/- mice exhibited delayed retinal vascularization at postnatal day 5, with fewer tip cells and reduced vessel density. Ultrastructural examination revealed disrupted and reduced fenestrations in choroidal capillary endothelium. In an oxygen-induced retinopathy model, while Emcn-/- mice showed no significant difference in avascular area compared to Emcn+/+ mice at postnatal day 12, there was a significant reduction in neovascular tufts in Emcn-/- mice at postnatal day 17. Similarly, in a laser-induced choroidal neovascularization model, Emcn-/- mice showed a significant reduction in vascular leakage and lesion size. These findings suggest that EMCN plays a critical role in both vascular development and pathological neovascularization, highlighting its potential as a target for anti-angiogenic therapies.

pathology↗

bioRxiv2022

UNSUPERVISED HARMONIZATION OF BRAIN MRI USING 3D CYCLE GANS AND ITS EFFECT ON BRAIN AGE PREDICTION

Deep learning methods trained on brain MRI data from one scanner or imaging protocol can fail catastrophically when tested on data from other sites or protocols - a problem known as domain shift. To address this, here we propose a domain adaptation method that trains a 3D CycleGAN (cycle-consistent generative adversarial network) to harmonize brain MRI data from 5 diverse sources (ADNI, WHIMS, OASIS, AIBL, and UK Biobank; total N=4,941 MRIs, age range: 46-96 years). The approach uses 2 generators and 2 discriminators to generate an image harmonized to a specific target dataset given an image from the source domain distribution and vice versa. We train the CycleGAN to jointly optimize an adversarial loss and cyclic consistency. We use a patch-based discriminator and impose identity loss to further regularize model training. To test the benefit of the harmonization, we show that brain age estimation - a common benchmarking task - is more accurate in GAN-harmonized versus raw data. t-SNE maps show the improved distributional overlap of the harmonized data in the latent space.

neuroscience↗

bioRxiv2022

Predicting Dementia Severity by Merging Anatomical and Diffusion MRI with Deep 3D Convolutional Neural Networks

Machine learning methods have been used for over a decade for staging and subtyping a variety of brain diseases, offering fast and objective methods to classify neurodegenerative diseases such as Alzheimers disease (AD). Deep learning models based on convolutional neural networks (CNNs) have also been used to infer dementia severity and predict future clinical decline. Most CNN-based deep learning models use T1-weighted brain MRI scans to identify predictive features for these tasks. In contrast, we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models of dementia severity. dMRI is sensitive to microstructural brain abnormalities not evident on standard anatomical MRI. By training CNNs on combined anatomical and diffusion MRI, we hypothesize that we could boost performance when predicting widely-used clinical assessments of dementia severity, such as individuals scores on the ADAS11, ADAS13, and MMSE (mini-mental state exam) clinical scales. For benchmarking, we evaluate CNNs that use T1-weighted MRI and dMRI to estimate "brain age" - the task of predicting a persons chronological age from their neuroimaging data. To assess which dMRI-derived maps were most beneficial, we computed DWI-derived diffusion tensor imaging (DTI) maps of mean and radial diffusivity (MD/RD), axial diffusivity (AD) and fractional anisotropy (FA) for 1198 elderly subjects (age: 74.35 +/- 7.74 yrs.; 600 F/598 M, with a distribution of 636 CN/421 MCI/141 AD) from the Alzheimers Disease Neuroimaging Initiative (ADNI). We tested both 2D Slice CNN and 3D CNN neural network models for the above predictive tasks. Our results suggest that for at least some deep learning architectures, diffusion-weighted MRI may enhance performance for several AD-relevant deep learning tasks relative to using T1-weighted images alone.

neuroscience↗