Biology subjects

Chattopadhyay, T.

Publications and source records attributed to Chattopadhyay, T..

30 records · Page 2Linked to original sources

bioRxiv2024

Consumption of human-relevant levels of sucrose-water rewires macronutrient uptake and utilization mechanisms in a tissue specific manner

Consumption of sugar-sweetened beverages (SSBs) have been linked to metabolic dysfunction, obesity, diabetes and enhanced risk of cardiovascular diseases across all age-groups globally. Decades of work that have provided insights into pathophysiological manifestations of sucrose overfeeding have employed paradigms that rarely mimic human consumption of SSBs. Thus, our understanding of multi-organ cross-talk and molecular and/or cellular mechanisms, which operate across scales and drive physiological derangement is still poor. By employing a paradigm of sucrose water feeding in mice that closely resembles chronic SSB consumption in humans (10% sucrose in water), we have unraveled hitherto unknown tissue-specific mechanistic underpinnings, which contribute towards perturbed physiology. Our findings illustrate that systemic impaired glucose homeostasis, mediated by hepatic gluconeogenesis and insulin resistance, does not involve altered gene expression programs in the liver. We have discovered the pivotal role of the small intestine, which in conjunction with liver and muscles, drives dyshomeostasis. Importantly, we have uncovered rewiring of molecular mechanisms in the proximal intestine that is either causal or consequential to systemic ill-effects of chronic sucrose water consumption including dysfunction of liver and muscle mitochondria. Tissue-specific molecular signatures, which we have unveiled, clearly indicate that inefficient utilization of glucose is exacerbated by enhanced uptake by the gut. Besides providing systems-wide mechanistic insights, we propose that consumption of SSBs causes intestinal molecular addiction for deregulated absorption of hexose-sugars, and drives diseases such as diabetes and obesity.

physiology↗

bioRxiv2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

IntroductionDiffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. MethodsHere, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. ResultsIn two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics sensitivity to dementia. DiscussionWe show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brains neural pathways.

neuroscience↗

bioRxiv2024

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimers disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimers Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting "brain age" - the task of predicting a persons chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

neuroscience↗

bioRxiv2024

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNTs generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNTs capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimers disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

neuroscience↗

bioRxiv2023

Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network

Parkinsons disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimers disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here 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 for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Clinical RelevanceThis study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinsons disease.

neuroscience↗

bioRxiv2023

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

In brain imaging research, it is becoming standard practice to remove the face from the individuals 3D structural MRI scan to ensure data privacy standards are met. Face removal - or defacing - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on brain age prediction - a common benchmarking task of predicting a subjects chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

bioinformatics↗

bioRxiv2023

Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs

Abnormal {beta}-amyloid (A{beta}) accumulation in the brain is an early indicator of Alzheimers disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, A{beta} positivity (A{beta}+) is assessed using PET or CSF assays, both highly invasive procedures. Here, we investigate how well A{beta}+ can be predicted from T1 weighted brain MRI and gray matter, white matter and cerebrospinal fluid segmentations from T1-weighted brain MRI (T1w), a less invasive alternative. We used 3D convolutional neural networks to predict A{beta}+ based on 3D brain MRI data, from 762 elderly subjects (mean age: 75.1 yrs. {+/-}7.6SD; 394F/368M; 459 healthy controls, 67 with MCI and 236 with dementia) scanned as part of the Alzheimers Disease Neuroimaging Initiative. We also tested whether the accuracy increases when using transfer learning from the larger UK Biobank dataset. Overall, the 3D CNN predicted A{beta}+ with 76% balanced accuracy from T1w scans. The closest performance to this was using white matter maps alone when the model was pre-trained on an age prediction in the UK Biobank. The performance of individual tissue maps was less than the T1w, but transfer learning helped increase the accuracy. Although tests on more diverse data are warranted, deep learned models from standard MRI show initial promise for A{beta}+ estimation, before considering more invasive procedures. Clinical RelevanceEarly detection of A{beta} positivity from less invasive MRI images, could offer a screening test prior to more invasive testing procedures.

neuroscience↗

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↗

bioRxiv2022

Learning Optimal White Matter Tract Representations from Tractography using a Deep Generative Model for Population Analyses

Whole brain tractography is commonly used to study the brains white matter fiber pathways, but the large number of streamlines generated - up to one million per brain - can be challenging for large-scale population studies. We propose a robust dimensionality reduction framework for tractography, using a Convolutional Variational Autoencoder (ConvVAE) to learn low-dimensional embeddings from white matter bundles. The resulting embeddings can be used to facilitate downstream tasks such as outlier and abnormality detection, and mapping of disease effects on white matter tracts in individuals or groups. We design experiments to evaluate how well embeddings of different dimensions preserve distances from the original high-dimensional dataset, using distance correlation methods. We find that streamline distances and inter-bundle distances are well preserved in the latent space, with a 6-dimensional optimal embedding space. The generative ConvVAE model allows fast inference on new data, and the smooth latent space enables meaningful decodings that can be used for downstream tasks. We demonstrate the use of a ConvVAE model trained on control subjects data to detect structural anomalies in white matter tracts in patients with Alzheimers disease (AD). Using ConvVAEs to facilitate population analyses, we identified 6 tracts with statistically significant differences between AD and controls after controlling for age and sex effect, visualizing specific locations along the tracts with high anomalies despite large inter-subject variations in fiber bundle geometry.

neuroscience↗

bioRxiv2019

Hyperinsulinemia promotes HMGB1 release leading to inflammation induced systemic insulin resistance: An interplay between pancreatic beta-cell and peripheral organs

Insulin resistance results from several pathophysiologic mechanisms, including chronic tissue inflammation and defective insulin signaling. Pancreatic {beta}-cells hypersecretion (hyperinsulinemia), is a central hallmark of peripheral insulin resistance. However, the underlying mechanism by which hyperinsulinemia perpetuates towards the development of insulin resistance remains unclear and is still a bigger therapeutic challenge. Here, we found hyperinsulinemia triggers inflammation and insulin resistance by stimulating TLR4-driven inflammatory cascades. We show that hyperinsulinemia activates the TLR4 signaling through HMGB1, an endogenous TLR4 ligand emanating from hyperinsulinemia exposed immune cells and peripheral organs like adipose tissue and liver. Further, our observation suggests hyperinsulinemia ensuring hyperacetylation, nuclear-to-cytoplasmic shuttling and release of HMGB1 into the extracellular space. HMGB1 was also found to be elevated in serum of T2DM patients. We found that extracellular HMGB1 plays a crucial role to promote proinflammatory responses and provokes systemic insulin resistance. Importantly, in-vitro and in-vivo treatment with naltrexone, a TLR4 antagonist led to an anti-inflammatory phenotype with protection from hyperinsulinemia mediated insulin resistance. In-vitro treatment with naltrexone directly enhanced SIRT1 activity, blocked the release of HMGB1 into extracellular milieu, suppressed release of proinflammatory cytokines and ultimately led to insulin-sensitizing effects. These observations elucidate a regulatory network between pancreatic {beta}-cells, macrophage and hepatocytes and assign an unexpected role of TLR4 - HMGB1 signaling axis in hyperinsulinemia mediated systemic insulin resistance.\n\nGraphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

physiology↗

bioRxiv2019

Temporal gating of SIRT1 functions by O-GlcNAcylation prevents hyperglycemia and enables physiological transitions in liver

Inefficient fasted-to-refed transitions are known to cause metabolic diseases. Thus, identifying mechanisms that may constitute molecular switches during such physiological transitions become crucial. Specifically, whether nutrients program a relay of interactions in master regulators, such as SIRT1, and affect their stability is underexplored. Here, we elucidate nutrient-dependent O-GlcNAcylation of SIRT1, within its N-terminal domain, as a key determinant of hepatic glucose- and fat-metabolism, and insulin signaling. SIRT1 glycosylation dictates interactions with PPAR/FOXO1/PGC1/SREBP1, to exert a temporal control over transcription of genes during fasted-to-refed transitions. Interestingly, glycosylation-dependent cytosolic export of SIRT1 promotes a transient interaction with AKT and subsequent proteasomal degradation. Loss of glycosylation discomposes these interactions and enhances stability of SIRT1 even upon refeeding, which causes insulin resistance, hyperglycemia and hepatic-inflammation. Aberrant glycosylation of SIRT1 is associated with aging and/or metabolic diseases. Thus, nutrient-dependent glycosylation constrains spatio-temporal dynamics of SIRT1 and gates its functions to maintain metabolic homeostasis.

physiology↗