Biology subjects

Chakravarthy, S.

Publications and source records attributed to Chakravarthy, S..

18 recordsLinked to original sources

bioRxiv2025

A Mechanistic Whole Brain Model to Capture Simultaneous EEG-fMRI Data

This study introduces a novel oscillatory network model to simulate simultaneous EEG-fMRI data, addressing the reconstruction challenge that arises due to their contrasting spatiotemporal scales. Here, each brain region is modelled by two oscillator clusters - a cluster of low-frequency (LFO) and high-frequency Hopf oscillators (HFO) coupled with an innovative power-coupling rule, facilitating cross-frequency interactions. The model is trained in two stages: learning oscillators frequencies and phase relations using a biologically plausible complex-Hebbian rule in the first stage and, followed by a modified complex backpropagation for amplitude approximation, overcoming limitations of poor accuracy and computational complexity in existing models. This framework outperforms current methods in replicating empirical Functional Connectivity (FC), Functional Connectivity Dynamics (FCD), and modularity over disparate spatio-temporal scales. The correlation between the FC of fMRI and the FCs of various EEG frequency bands is reflected in the strengths of the LFO-HFO coupling. Furthermore, in-silico structural perturbation studies quantified the effect of pruning of the anatomical connectivity on spatiotemporal dynamics in terms of FC, FCD, modularity, and integration level (ISOR). The models ability to reconstruct simultaneous EEG-fMRI data showcases significant advancement in understanding the resting-state brains functionality from multimodal settings and deciphering neurological disorders in diverse spatiotemporal scales.

neuroscience↗

bioRxiv2025

Classifier-guided Deep Oscillatory Neural Networks (cDONN) for capturing both Neural Dynamics and Behavior simultaneously

Generating EEG signals alongside behavioural actions introduces substantial biological complexity, akin to an abstract model that mimics rich oscillatory brain dynamics by simultaneously producing dynamic neural activity and corresponding behavioural responses. In this work, we propose a novel class of bio-inspired classifier guided Deep Oscillatory Neural Network (cDONN), designed to simultaneously model and capture both neural signal dynamics and behavioural patterns. The cDONN integrates a feedforward neural network with Hopf oscillator neurons, specialized in handling sequential data generation tasks. The model features a Y-shaped structure with two output branches: one for classifying the input image that signifies behaviour, and the other for generating corresponding EEG signals that represent neural dynamics. We evaluate the performance of the behaviour classification branch using the standard ramp classification accuracy used for the cDONN network. For the EEG signal generation branch, we benchmark the generated signals using classification accuracy via a pre-trained DONN signal classifier, alongside comparison with real EEG signals using metrics such as Inception Score, Frechet Inception Distance (FID), power spectrum analysis and topoplot analysis. Our analysis reveals a strong correspondence between the generated and real EEG data, particularly in the midline parietal region within the alpha frequency band, and in the left hemisphere within the delta band, indicating high fidelity and neurophysiological realism in the synthesized signals. Our approach offers a novel perspective on modelling the simultaneous relationship between neural activity and behaviour, pushing the boundaries of EEG signal generation and behavioural analysis.

bioengineering↗

bioRxiv2024

SAXS/MC studies of the mixed-folded protein Cdt1 reveal monomeric, folded over conformations

Cdt1 is a protein critical for DNA replication licensing and is well-established to be a binding partner of the minichromosome maintenance (MCM) complex. Cdt1 has also been demonstrated to have an emerging, "moonlighting" role at the kinetochore via direct binding to microtubules and to the Ndc80 complex. However, it is not known how the structure and conformations of Cdt1 could allow for these multiple, completely unique sets of protein complexes. And while there exist multiple robust methods to study entirely folded or entirely unfolded proteins, structure-function studies of combined, mixed folded/disordered proteins remain challenging. It this work, we employ multiple orthogonal biophysical and computational techniques to provide a detailed structural characterization of human Cdt1 92-546. DSF and DSCD show both folded winged helix (WH) domains of Cdt1 are relatively unstable. CD and NMR show the N-terminal and the linker regions are intrinsically disordered. Using DLS and SEC-MALS, we show that Cdt1 is polydisperse, monomeric at high concentrations, and without any apparent inter-molecular self-association. SEC-SAXS of the monomer in solution enabled computational modeling of the protein in silico. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble. Using experimental SAXS data, we filtered for conformations which did and did not fit our data. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with our SAXS data. The best fit models have the N-terminal and linker regions extended into solution and the two folded domains close to each other in apparent "folded over" conformations. The best fit Cdt1 conformations are consistent with a function as a scaffold protein which may be sterically blocked without the presence of binding partners. Our studies also provide a template for combining experimental and computational biophysical techniques to study mixed-folded proteins.

biophysics↗

bioRxiv2023

Modeling of whole brain Electroencephalogram (EEG) in a spatially organized oscillatory network

In this study, we model high-dimensional Electroencephalogram signals in sleep stages, using a general trainable network of Hopf oscillators. The proposed architecture has two components: a layer of oscillators with lateral connections, and a complex valued feedforward network with and without a hidden layer. The output of the Hopf oscillators, whose dynamics is described in the complex domain, is fed as input to the feedforward network and the output predicts the EEG signals. The proposed network is trained in two stages: in the 1st stage, intrinsic frequencies of the oscillators and the lateral connections are trained whereas, in the 2nd stage, the complex-valued feed-forward network is trained. Reconstruction error obtained when there is a hidden layer in the feedforward network is an order of magnitude smaller than that obtained without a hidden layer. Also, it has been shown that during testing our model is able to generate EEG signals, whose spectral properties accurately describe the corresponding target signals. In the first, the oscillators do not have any spatial localization, whereas in the second the oscillators are spatially distributed in a spherical shell geometry. The model described can be interpreted as a stepping stone towards a large-scale model of brain dynamics.

neuroscience↗

bioRxiv2023

A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson's Conditions

Working memory is considered as the scratchpad to write, read, and process information to perform cognitive tasks. Basal Ganglia (BG) and Prefrontal Cortex are two important parts of the brain that are involved in working memory functions and both the structures receive projections from dopaminergic nuclei. In this modelling study, we specifically focus on modelling the working memory functions of the BG, the working memory deficits in Parkinsons disease conditions, and the impact of dopamine deficiency on different kinds of working memory functions. Though there are many experimental and modelling studies of working memory properties, there is a paucity of models of the BG that provide insights into the contributions of the BG in working memory functions. The proposed model of the BG is a unified model that can explain the working memory functions of the BG over a wide variety of tasks in normal and Parkinsons disease conditions.

neuroscience↗

bioRxiv2022

A biologically plausible dynamic deep network for recognizing structure from motion and biological motion

A breakthrough in the understanding of dynamic 3D shape recognition was the discovery that our visual system can extract 3D shape from inputs having only sparse motion cues such as (i) point light displays and (ii) random dot displays representing rotating 3D shapes - phenomena named as biological motion (BM) processing and structure from motion (SFM) respectively. Previous psychological and computational modeling studies viewed these two as separate phenomena and could not fully identify the shared visual processing mechanisms underlying the two phenomena. Using a series of simulation studies, we describe the operations of a dynamic deep network model to explain the mechanisms underlying both SFM and BM processing. In simulation-1, the proposed Structure from Motion Network (SFMNW) is trained using displays of 5 rotating surfaces (cylinder, cone, ellipsoid, sphere and helix) and tested on its shape recognition performance under a variety of conditions: (i) varying dot density, (ii) eliminating local feature stability by introducing a finite dot lifetime, (iii) orienting shapes, (iv) occluding boundaries and intrinsic surfaces (v) embedding shape in static and dynamic noise backgrounds. Our results indicate that smaller dot density of rotating shape, oriented shapes, occluding boundaries, and dynamic noise backgrounds reduced the models performance whereas eliminating local feature stability, occluding intrinsic boundaries, and static noise backgrounds had little effect on shape recognition, suggesting that the motion of high curvature regions like shape boundaries provide strong cues in shape recognition. In simulation-2, the proposed Biological Motion Network (BMNW) is trained using 6 point-light actions (crawl, cycle, walk, jump, wave, and salute) and tested its action recognition performance on various conditions: (i) inverted (ii) scrambled (iii) tilted (iv) masked (v) actions, embedded in static and dynamic noise backgrounds. Model performance dropped significantly for the presentation of inverted and tilted actions. On the other hand, better accuracy was attained in distinguishing scrambled, masked actions, performed under static and dynamic noise backgrounds, suggesting that critical joint movements and their movement pattern generated in the course of action (actor configuration) play a key role in action recognition performance. We also presented the above two models with mixed stimuli (a point light actions embedded in rotating shapes), and achieved significantly high accuracies. Based on the above results we hypothesize that visual motion circuitry supporting robust SFM processing is also involved in the BM processing. The proposed models provide new insights into the relationships between the two visual motion phenomena viz., SFM and BM processing.

neuroscience↗

bioRxiv2022

Modeling the development of cortical responses in primate dorsal (where) pathway to optic flow using hierarchical neural field models

Although there is a plethora of modelling literature dedicated to the object recognition processes of the ventral ("what") pathway of primate visual systems, modelling studies on the motion sensitive regions like the Medial superior temporal area (MST) of the dorsal ("where") pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons in primates. The first two models are each composed of 3 stages: the first stage comprises the Direction Selective Mosaic Network (DSMN), the second stage comprises the Cell Plane Network (CPNW) or the Hebbian Network (HBNW) and the third stage comprises the optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas respectively in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian learning. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained using supervised backpropagation algorithm. We created various dot configurations that can move in translational, radial, and rotational trajectories to make training and test set. The simulation results show that, while neurons in model-1 and model-2 could account for MSTd cell properties found neurobiologically, model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway.

neuroscience↗

bioRxiv2022

Structure and mechanism of LARGE1 matriglycan polymerase

Matriglycan is a linear polysaccharide of alternating xylose and glucuronate that binds extracellular matrix proteins and acts as a receptor for Lassa fever virus. LARGE1 synthesizes matriglycan on dystroglycan and mutations in LARGE1 cause muscular dystrophy with abnormal brain development. However, the mechanism of matriglycan polymerization by LARGE1 is unknown. Here, we report the cryo-EM structure of LARGE1. We show that LARGE1 functions as a dimer to polymerize matriglycan by alternating activities between the xylose transferase domain on one protomer and the glucuronate transferase domain on the other protomer. Biochemical analyses using a recombinant Golgi form of dystroglycan reveal that LARGE1 polymerizes matriglycan processively. Our results provide mechanistic insights into LARGE1 function and may facilitate novel therapeutic strategies for treating neuromuscular disorders or arenaviral infections. One-Sentence SummaryDimeric LARGE1 processively polymerizes matriglycan on dystroglycan using orthogonal active sites on alternate protomers.

biochemistry↗

bioRxiv2022

Memory Consolidation with Orthogonal Gradients for avoiding Catastrophic Forgetting

The memory consolidation process enables the accumulation of recent and remote memories in the long-term memory store. In general, the deep network models of memory suffer from forgetting old information while learning new information, called catastrophic forgetting/interference. The human brain overcomes this problem quite effectively, a problem that continues to challenge current deep neural network models. We propose a regularization-based model to solve the problem of catastrophic forgetting. According to the proposed training mechanism, the network parameters are constrained to vary in a direction orthogonal to the average of the error gradients corresponding to the previous tasks. We also ensure that the constraint used in parameter updating satisfies the locality principle. The proposed models performance is compared with Elastic Weight Consolidation on standard datasets such as permuted MNIST and split MNIST on classification tasks using fully connected networks, and Convolution-based networks. The model performance is also compared to an autoencoder on split MNIST dataset, and to complex core50 dataset on two types of classification tasks with EWC. The proposed model gives a new view on plasticity at the neuronal level. In the proposed model, the parameter updating is controlled by the neuronal level plasticity rather than synapse level plasticity as in other standard models. The biological plausibility of the proposed model is discussed by linking the extra parameters to synaptic tagging, which represents the state of the synapse involved in Long Term Potentiation (LTP).

neuroscience↗

bioRxiv2021

Structural insight into the dual function of LbpB in mediating Neisserial pathogenesis

Lactoferrin binding protein B (LbpB) is a lipoprotein present on the surface of Neisseria that has been postulated to serve dual functions during pathogenesis in both iron acquisition from lactoferrin, and in providing protection against the cationic antimicrobial peptide lactoferricin. Here, we present the structures of LbpB from N. meningitidis and N. gonorrhoeae in complex with human holo-lactoferrin, forming a 1:1 complex and confirmed by SEC-SAXS. LbpB consists of N- and C-lobes with the N-lobe interacting extensively with the C-lobe of lactoferrin. Our structures provides insight into LbpBs preference towards holo-lactoferrin, and our mutagenesis and binding studies show that lactoferrin and lactoferricin bind independently. Our studies provide the molecular details for how LbpB serves to capture and preserve lactoferrin in an iron-bound state for delivery to the membrane transporter LbpA for iron piracy, and as an antimicrobial peptide sink to evade host immune defenses.

biophysics↗

bioRxiv2020

TWO CLASSES OF MYOSIN INHIBITORS, BLEBBISTATIN AND MAVACAMTEN, STABILIZE β-CARDIAC MYOSIN IN DIFFERENT STRUCTURAL AND FUNCTIONAL STATES

In addition to a conventional relaxed state, a fraction of myosins in the cardiac muscle exists in a newly-discovered low-energy consuming super-relaxed (SRX) state, which is kept as a reserve pool that may be engaged under sustained increased cardiac demand. The conventional and the super-relaxed states are widely assumed to correspond respectively to a structure where myosin heads are in an open configuration, free to interact with actin, and a closed configuration, inhibiting binding to actin. Disruption of the SRX population in different heart diseases, such as hypertrophic cardiomyopathy, results in unwarranted muscle contraction, and stabilizing them using myosin inhibitors is budding as an attractive therapeutic strategy. Here we examine the structure-function relationships of two myosin ATPase inhibitors, mavacamten, and blebbistatin, and found that binding of mavacamten to myosin at a site different than blebbistatin populates myosin into the SRX state. Blebbistatin, and para-nitroblebbistatin, binding to a distal pocket to the myosin lever arm near the nucleotide-binding site, does not affect the usual myosin SRX state but instead appears to render myosin into a new, perhaps non-functional, ultra-relaxed state. X-ray scattering-based rigid body modeling shows that both mavacamten and para-nitroblebbistatin induce novel conformations that diverge significantly from the hypothetical open and closed states and furthermore, mavacamten treatment causes a greater compaction than para-nitroblebbistatin. Taken together, we conclude that mavacamten and blebbistatin stabilize myosin in different structural states, and such states may give rise to different functional energy-sparing SRX states.

biochemistry↗

bioRxiv2020

The solution structures of higher-order human telomere G-quadruplex multimers

Human telomeres contain the repeat DNA sequence 5(TTAGGG), with duplex regions that are several kilobases long terminating in a 3 single-stranded overhang. The structure of the single-stranded overhang is not known with certainty, with disparate modes proposed in the literature. We report here the results of an integrated structural biology approach that combines small-angle X-ray scattering, circular dichroism (CD), analytical ultracentrifugation, size-exclusion column chromatography and molecular dynamics simulations that provide the most detailed characterization to date of the structure of the telomeric overhang. We find that the single-stranded sequences 5(TTAGGG)n, with n=8, 12, and 16, fold into multimeric structures containing the maximal number (2, 3, and 4, respectively) of contiguous G4 units with no long gaps between units. The G4 units are a mixture of hybrid-1 and hybrid-2 conformers. In the multimeric structures, G4 units interact, at least transiently, at the interfaces between units to produce distinctive CD signatures. Global fitting of our hydrodynamic and scattering data to a worm-like chain (WLC) model indicates that these multimeric G4 structures are semi-flexible, with a persistence length of about 34 [A]. Investigations of its flexibility using MD simulations reveal stacking, unstacking, and coiling movements, which yield unique sites for drug targeting.

biophysics↗

bioRxiv2020

Competitive exclusion of uropathogenic E. coli biofilm by Salmonella through matrix inhibition

Biofilm is a predominant lifestyle of bacteria in host and non-host environments with cell collectives and extracellular matrix as the defining principles of biofilm. Several factors trigger biofilm formation including response to competition. Urinary tract infections (UTI) are highly prevalent worldwide and mainly caused by uropathogenic E. coli (UPEC), which progresses into chronic form due to the biofilm formation by the pathogen. In this study, we hypothesized that competition for territorial space could occur between species by intervening in the biofilm matrix production, particularly of UPEC, thereby reducing its colonizing ability. UPEC colony displays different morphology in congo red media based on matrix production, which we exploited for screening bacterial isolates capable of inhibiting the matrix. This was validated by using the cell-free supernatants of the isolates to impair UPEC biofilm. Isolates that inhibited matrix production belonged to species of Shigella, Escherichia, Enterobacter, and Salmonella from Enterobacteriaceae family. Competition experiments between the isolates and UPEC revealed spiteful interactions particularly during biofilm formation, indicating fierce competition for territorial space colonization. The isolate Salmonella enterica B1 could competitively exclude UPEC in the biofilm. Altogether, we show that interference competition by matrix inhibition occurs as a strategy by bacteria to colonize territorial space.

microbiology↗

bioRxiv2019

Structural assembly of the human Miro1/2 GTPases based on the crystal structure of the N-terminal GTPase domain

Dysfunction in mitochondrial dynamics is believed to contribute to a host of neurological disorders and has recently been implicated in cancer metastasis. The outer mitochondrial membrane adapter protein Miro functions in the regulation of mitochondrial mobility and degradation, however, the structural basis for its roles in mitochondrial regulation remain unknown. Here, we report a 1.7[A] crystal structure of N-terminal GTPase domain (nGTPase) of human Miro1 bound unexpectedly to GTP, thereby revealing a non-catalytic configuration of the putative GTPase active site. We identify two conserved surfaces of the nGTPase, the \"SELFYY\" and \"ITIP\" motifs, that are potentially positioned to mediate dimerization or interaction with binding partners. Additionally, we report small angle X-ray scattering (SAXS) data obtained from the intact soluble HsMiro1 and its paralog HsMiro2. Taken together, the data allow modeling of a crescent-shaped assembly of the full-length soluble domain of HsMiro1/2.\n\nPDB referenceCrystal structure of the human Miro1 N-terminal GTPase bound to GTP, 6D71

cell biology↗

bioRxiv2018

A Cortico- Basal Ganglia Model for choosing an optimal rehabilitation strategy in Hemiparetic Stroke

To facilitate the selection of an optimal therapy for a stroke patient with upper extremity hemiparesis, we propose a cortico-basal ganglia model capable of performing reaching tasks under normal and stroke conditions. The model contains two hemispherical systems, each organized into an outer sensory-motor cortical loop and an inner basal ganglia (BG) loop, controlling their respective hands. In addition to constraint induced movement therapy (CIMT), the model performs both unimanual and bimanual reaching tasks and the simulation results are in congruence with the experiment conducted by Rose et al (2004). Based on our study on the effect of lesion size on arm performance, we hypothesize that the effectiveness of a therapy could greatly depend on this factor. By virtue of the models ability to capture the experimental results effectively, we believe that it can serve as a benchmark for the development and testing of various rehabilitation strategies for stroke.

neuroscience↗

bioRxiv2018

A Hierarchical Anti-Hebbian Network Model for the Formation of Spatial Cells in Three-Dimensional Space

Three dimensional (3D) spatial cells in the mammalian hippocampalformationare believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here, we present a hierarchical network model for the formation of 3D spatial cells using anti-hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border and grid-cells as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher dimensional cognitive maps.

neuroscience↗

bioRxiv2017

Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Bipolar disorder is characterized by mood swings - oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patients mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms leads to a positive outcome, while the other leads to a negative outcome. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. The model is cast at three levels of abstraction: 1) a two-dimensional dynamical system model, 2) a phenomenological basal ganglia model, 3) a detailed network model of basal ganglia. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological bipolar-like oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The networks dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

neuroscience↗

bioRxiv2017

A computational model that explores the effect of environmental geometries on grid cell representations

Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern was originally considered to be invariant to the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al 2015) and in connected environments (Carpenter et al 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the grid cell periodicity from a computational point of view using a model that was successful in generating a wide range of spatial cells, including grid cells, place cells, head direction cells and border cells. We simulated the model in four types of environmental geometries such as: 1) connected environments, 2) convex shapes, 3) concave shapes and 4) regular polygons with varying number of sides. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model code not just for local position but also for more global information like the shape of the environment. The proposed model is interesting not only because it was able to capture the aforementioned experimental results but, more importantly, it was able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity.

neuroscience↗