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Nagarajan, S.

Publications and source records attributed to Nagarajan, S..

At least 19 recordsLinked to original sources

Modeling framework disentangles cerebellar mechanisms in speech feedback control, revealing trade-offs reshaped by degeneration

A central challenge in systems neuroscience is understanding how computational mechanisms--including those implemented within a single brain region--interact to produce behavior. For example, prior work in the literature attributes many computational functions to the cerebellum, but these functions have been tested in isolation and it remains unclear how they jointly contribute to motor control. Here, we test several established hypotheses of cerebellar function: internal modeling, timing of movement dynamics, sensory-error processing, delay processing, and multimodal integration. We first formalize these functions as mechanistic parameters within a computational model of speech motor control. We then use this formalism to investigate the relative contribution of each function to the abnormal speech corrective response seen in adults with cerebellar degeneration during perturbed auditory feedback. We find the following functions explain most of the behavioral differences: internal modeling, timing of movement dynamics, and multimodal integration. We also show that the key mechanisms have a trade-off relationship, and that cerebellar degeneration modulates those trade-off strengths and boundaries. These results both elaborate the mechanistic function of the cerebellum in speech feedback control and, more broadly, demonstrate the promise of using this paradigm to simultaneously test competing theories of neural function underlying behavior.

neuroscience↗

ARID1A orchestrates the activity of FOXA1 and AP-2 transcription factors in lobular breast cancer cells

Mutations in components of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin-remodelling complex are among the most common genetic alterations in human cancers, yet their functional consequences are difficult to predict as they are highly context-dependent. ARID1A, a core subunit of the canonical BAF complex, is the most frequently mutated SWI/SNF gene and is recurrently altered in breast cancer, with a notable enrichment in the lobular carcinoma subtype. While previous studies in ductal breast cancer have linked ARID1A loss to deregulated oestrogen receptor (ER) signalling and endocrine resistance through its association with the pioneer factor FOXA1, the role of ARID1A in lobular cancer remains poorly understood. Here, we define the genomic, transcriptomic, and chromatin accessibility landscapes governed by ARID1A in lobular breast cancer cells. We show that ARID1A and its catalytic partner SMARCA4 co-occupy distal regulatory regions enriched for forkhead and AP-2 transcription factor motifs. ARID1A depletion leads predominantly to loss of chromatin accessibility at these putative enhancer regions and downregulation of associated genes, indicating a primary role in maintaining a permissive regulatory landscape. Extensive co-binding and reciprocal dependencies between ARID1A, FOXA1, and AP-2 transcription factors reveal a coordinated regulatory network distinct from that observed in ductal breast cancers. ARID1A loss does not impair ER-mediated transcriptional responses in the lobular subtype but instead alters basal expression of a subset of oestrogen-responsive genes. Importantly, ARID1A, FOXA1, and AP-2 jointly regulate genes implicated in skeletal system development and bone metastasis, mirroring mutational patterns observed in lobular breast cancer patient datasets. These findings highlight a unique ARID1A-centred transcriptional programme in lobular breast cancer with potential implications for metastatic behaviour and therapeutic vulnerability.

Cancer Biology↗

Gene Gradients Reveal Directed Structural Connectivity Across Species

Studying the brain's inter-regional structural connectivity (SC) has transformed neuroscience, yet noninvasive neuroimaging remains blind to the directionality of neural wiring. Structural directionality must influence observed brain dynamics, but estimating edge-level directionality from brain function is ill-posed and underdetermined. Here, we show that gene co-expression gradients define a low-dimensional biological manifold that locally biases brain regions to act as network sources or sinks. Embedding gene gradients into a linear, higher-order network diffusion model defines a gene-structure-function relationship that recovers ground-truth directionality in C. elegans (r=0.70), mouse (r=0.57), and macaque (r=0.46). Applying this framework to 770 healthy young adults from the Human Connectome Project yields subject-specific directed connectomes that reproduce established corticothalamic asymmetries and reveal distinct gene ontologies associated with network sources and sinks. Furthermore, integrating directed structure with observed functional covariance enables us to derive a principled measure of directed functional connectivity, termed "angular flow" (AF), related to probability angular momentum in non-equilibrium statistical mechanics. Regional AF recapitulates the principal unimodal--multimodal gradient of functional connectivity, suggesting that this macroscale functional hierarchy emerges from a source--sink organization of signal flow through the brain. This framework resolves a long-standing limitation in connectomics and opens new opportunities to study structural directionality and non-equilibrium brain dynamics in health and disease.

neuroscience↗

Pathology of dose dependent inocula of H5N8 avian influenza viruses in experimentally infected chicken

In the present study, we assessed the pathogenicity of H5N8 avian influenza viruses belongs to the clade 2.3.4.4b in chicken. Birds of three different dose groups, 102, 104, and 106 EID50 were used in the study. No mortality was observed in 102 EID0 group. Percent cumulative mortality of 104 and 106 EID50 group was 66.67 and 100 %, respectively. Varying duration of MDT of 3.2 and 2 days was observed in 104 and 106 EID50 group, respectively. The CID50 of virus was found to be 104.5 EID50. High no. of viral RNA copies were found both in oropharyngeal and cloacal swabs and in various organs of birds infected in 104 and 106 EID50 group. Significant gross and histological changes and presence of viral antigen in various organs were observed in 104 and 106 EID50 group. So, the study concludes that Indian HPAI, H5N8 isolates are highly pathogenic in nature to chicken by affecting most organs systemically. CID50 of this H5N8 virus indicates poor adaption in chicken and it implies poor transmission possibility of this virus for host species in field condition. Though this virus is highly pathogenic in nature as that of HPAI, H5N1 viruses, absence of endothelial staining in most organ attributes variation in replication process and pathogenesis from HPAI, H5N1 viruses. Hence, further studies need to be done to elucidate the pathobiology of this virus in various bird species. HighlightsO_LIH5N8 virus belong to the clade 2.3.4.4b, Indian isolate is highly pathogenic in nature as that of HPAIV, H5N1. C_LIO_LIThe dose inocula, 102 EID50 is noninfectious to chicken. C_LIO_LIThe dose inocula, 104 and 106 EID50 had caused significant mortality in the inoculated chicken with MDT of 2 and 3.2 days, respectively. C_LIO_LIH5N8 virus was detected with high viral titres in clocal and oral shedding and in multiple organ with the dose inocula, 104 and 106 EID50. C_LIO_LI104 and 106 EID50 of H5N8 inocula virus caused significant gross and histological changes in multiple organs and viral antigens were detected in respective organs. C_LI

microbiology↗

Mechanistic insights into E. coli recovery from growth arrest

Bacteria survive hostile conditions in clinically relevant conditions by shutting down protein synthesis, but how they restart growth remains poorly understood. Here, we use an E. coli {Delta}rimM strain, which exhibits a prolonged growth arrest, as a model to investigate how bacteria recover from this arrested state and restore protein synthesis. RimM is a conserved ribosome maturation factor for the 3-major (head) domain of the 16S rRNA within the bacterial 30S subunit. The loss of RimM causes a significantly longer delay in recovery than other 30S maturation factors, including RbfA - the presumed primary factor in 30S maturation. Cryo-EM analysis of {Delta}rimM ribosomes revealed a delayed recruitment of ribosomal proteins to the 30S head domain and increased occupancy of the initiation factors IF1 and IF3, as well as recruitment of the silencing factor RsfS to the 50S subunit. These coordinated changes provide a safeguarding mechanism to block the assembly of premature 70S ribosomes. Notably, while the delayed 30S assembly in {Delta}rimM reduces the activity of global protein synthesis during the recovery phase, bacteria attempt to compensate for this deficiency by producing higher levels of the ribosomal machinery, indicating a programmatic change in energy allocation to generate the ribosome machinery. These findings highlight the importance of the RimM-assisted assembly of the ribosomal head domain for bacterial recovery from growth arrest.

molecular biology↗

HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals

Brain regions perform distinct computations, and their signals propagate through the whole-brain white matter network. Yet, mathematical models that describe this signal propagation via purely passive diffusion can predict a considerable amount of the observed functional connectivity between regions. This raises a critical question: if so much functional connectivity can be explained by a passive process, how can we isolate the active process? Here, we calculate in closed-form an estimate for such an active signal in functional MRI by spatially deconvolving the effect of passive signal spread over the brains structural connectivity using a higher-order network diffusion (HONeD) model. Across 770 Human Connectome Project subjects, we show that the resulting HONeD-innovation (HONeD-in) signal 1) sparsifies functional connectivity while retaining a well-connected network, 2) remodels resting-state networks (RSNs), 3) mixes the unimodal- multimodal hierarchical organization of RSNs into a circle with no clear hierarchy, and 4) deblurs task-activation maps. Together, our results highlight HONeD deconvolution as a generalizable new way to study resting-state and task fMRI brain signals.

neuroscience↗

Aperiodic neural dynamics define a novel signature of glioma-induced excitation-inhibition dysregulation

Diffuse gliomas remodel neuronal circuits with prognostic and therapeutic significance for patients. Electrophysiologic measures of cortical excitability hold promise for monitoring disease progression and evaluating therapeutic responses. The power law exponent (aperiodic slope) reflects the balance between excitatory and inhibitory activity within neuronal networks, a critical aspect of normal brain function often disrupted in neurological conditions. Despite its potential, the significance of the aperiodic slope in glioma-infiltrated tissue and its underlying cellular processes has not been fully investigated. Here, we integrate multi-modal electrophysiological analysis with transcriptomic profiling to analyze the aperiodic slope in both normal and glioma-infiltrated cortex. We determine that glioma infiltration induces a flattening of the aperiodic slope, indicating a shift toward excitation dominance that varies according to tumor subtype and correlates with impairments in semantic naming. Single-nucleus RNA sequencing revealed that cortical regions with flat aperiodic slope exhibit transcriptional programs enriched in glutamatergic signaling, membrane depolarization, and excitatory synaptic transmission. The aperiodic slope responds to pharmacologically induced changes in cortical inhibition during propofol administration, a GABAA agonist. Our results establish the aperiodic slope as a robust biomarker of glioma-associated excitation-inhibition imbalance, with potential applications in tumor classification and treatment monitoring.

cancer biology↗

Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers transcribe non-coding RNAs (called enhancer RNAs or eRNAs). eRNA expression is an early indicator of transcription factor activity and is associated with treatment response and survival in cancer patients. However, the attempts to identify prognostic eRNAs in breast cancers were inadequate, as these studies ignored the heterogenous nature of breast cancers with distinct molecular subtypes. By analysing [~]300,000 eRNA loci profiled using RNA-sequencing datasets from 1,095 breast cancer patients using machine learning approaches, we categorised eRNAs which are specific to breast cancer molecular subtypes and survival. The classified eRNAs were associated with gene pathways related to relevant subtypes. Interestingly, transcription factor analyses highlighted involvement of nuclear receptors other than the estrogen receptor with luminal-specific eRNAs. Basal eRNAs showed association with the transcriptional corepressor TRIM28 and androgen receptor. Luminal eRNAs were associated with better outcomes and Her2 eRNAs with worse outcome in patients. Overall, we demonstrate that machine learning approaches performed on RNA-seq datasets can classify subtype-specific and prognostic eRNAs which can be used to identify critical gene pathways and transcription factor networks in breast cancer.

cancer biology↗

Customized feminine hygiene wash containing postbiotics from Lactobacillus spp. to treat Urinary Tract Infections (UTI)

The rising prevalence of antimicrobial resistance has intensified the search for innovative therapeutic strategies, particularly in the prevention of urinary tract infections (UTIs). This study presents the development and evaluation of a novel postbiotics vaginal wash formulated to prevent UTIs by utilizing metabolites derived from indigenous vaginal Lactobacillus spp. The primary objective was to create a cost-effective, stable, and non-invasive solution targeting uropathogenic bacteria. Key metabolites, including tryptamine, (-)-terpinen-4-ol, and itaconic anhydride, were identified from cell free supernatant of vaginal Lactobacillus and incorporated into a poloxamer 407-based formulation. In vitro assays demonstrated significant bioactivity against uropathogenic bacteria, effectively inhibiting bacterial colonization and biofilm formation. Preclinical validation was conducted using BALB/c mice models to assess both the safety and efficacy of the vaginal wash. Results indicated a substantial reduction in infection rates among treated mice, with no observed adverse effects, confirming the formulations safety profile. In conclusion, this novel postbiotics vaginal wash represents a promising non-invasive therapeutic approach for UTI prevention. By harnessing bioactive metabolites from human vaginal Lactobacillus spp., the formulation offers a potential solution to combat antimicrobial resistance while improving womens health outcomes. Further clinical studies are warranted to validate these findings and explore broader applications in clinical practice, paving the way for new strategies in managing UTIs and enhancing overall female health.

microbiology↗

Integrative, Segregative, and Degenerate Harmonics of the Structural Connectome

Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the "binding problem." Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain-its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes-integrative, segregative, and degenerate- that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve "bottom-up" sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve "top-down" networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.

neuroscience↗

Global MEG Resting State Functional Connectivity in Children with Autism and Sensory Processing Dysfunction

Sensory processing dysfunction not only affects most individuals with autism spectrum disorder (ASD), but at least 5% of children without ASD also experience dysfunctional sensory processing. Our understanding of the relationship between sensory dysfunction and resting state brain activity is still emerging. This study compared long-range resting state functional connectivity of neural oscillatory behavior in children aged 8-12 years with autism spectrum disorder (ASD; N=18), those with sensory processing dysfunction (SPD; N=18) who do not meet ASD criteria, and typically developing control participants (TDC; N=24) using magnetoencephalography (MEG). Functional connectivity analyses were performed in the alpha and beta frequency bands, which are known to be implicated in sensory information processing. Group differences in functional connectivity and associations between sensory abilities and functional connectivity were examined. Distinct patterns of functional connectivity differences between ASD and SPD groups were found only in the beta band, but not in the alpha band. In both alpha and beta bands, ASD and SPD cohorts differed from the TDC cohort. Somatosensory cortical beta-band functional connectivity was associated with tactile processing abilities, while higher-order auditory cortical alpha-band functional connectivity was associated with auditory processing abilities. These findings demonstrate distinct long-range neural synchrony alterations in SPD and ASD that are associated with sensory processing abilities. Neural synchrony measures could serve as potential sensitive biomarkers for ASD and SPD.

neuroscience↗

Unraveling molecular basis for reduced neuraminidase inhibitors susceptibility in highly pathogenic avian influenza A (H5N1) viruses isolated from chickens in India

The increasing resistance cases in influenza viruses to different classes of antiviral drugs, necessitates the in-depth analysis of molecular interactions governing reduced susceptibility to these drugs. This study explores the molecular basis of neuraminidase inhibitors resistance in avian H5N1 influenza viruses identified in our previous research. Using comprehensive modeling and docking tools, we investigated two isolates--A/chicken/India/85459/2008 (N294S) and A/chicken/WestBengal/142121/2008 (E119A + I117V). The N294S mutation conferred oseltamivir resistance while retaining zanamivir susceptibility, whereas the E119A + I117V mutations led to zanamivir resistance while reducing oseltamivir susceptibility. Molecular interactions analysis unveiled varied fitness levels, hydrogen bonding, and affinity transitions associated with N294S and E119A + I117V mutations. This study provides crucial insights into molecular interactions responsible for reduced susceptibility to neuraminidase inhibitors, which is essential for optimizing antiviral strategies and pandemic preparedness.

microbiology↗

Neurophysiological trajectories in Alzheimer's disease progression

Alzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

neuroscience↗

Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise

The inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.

bioengineering↗

Bayesian Inference of a Spectral Graph Model for Brain Oscillations

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

neuroscience↗

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimers disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimers disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

neuroscience↗

Multi-tasking Deep Network for Tinnitus Classification and Severity Prediction from Multimodal Structural Images

Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical or functional morphological information in magnetic resonance images (MRI) of the brain, we examined data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction. We propose a deep multi-task multi-modal framework for joint functionalities using structural MRI (sMRI) data. To leverage cross-information multimodal neuroimaging data, we integrated two modalities of 3-dimensional sMRI - T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segmented both T1w and T2w images into three different components - cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM), and examined performance of each segmented image. Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction. Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.

neuroscience↗

Estimating Brain Functional Connectivity from Common Subspace Mapping Between Structural and Functional Connectomes

Understanding the connection between the brains structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subjects functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.

neuroscience↗