Biology subjects

Roy, D.

Publications and source records attributed to Roy, D..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

MEG Oscillatory and Aperiodic neural dynamics contribute to different cognitive aspects of short-term memory decline through the lifespan

Neural communication or signal transmission in the brain propagates via distinct oscillatory frequency bands. With aging, the communication mediated by these frequency bands is hindered by noise, which arises from the increased stochastic variability in the baseline neural spiking. This increase in noise measured as 1/f power-law scaling reflects the global background noise and is often linked to impaired cognition in different tasks. In this study, we quantified the 1/f slope and intercept of MEG brain signal as a putative marker of neural noise and examined its effect on cognitive and metacognitive measures. We hypothesize that as neural communication becomes noisier with age, it impacts global information processing, whereas specific periodic features mediate local aspects of cognition. Using recently proposed parametric Fooof model, we first characterised the normative pattern of periodic and aperiodic features (temporal dynamics) across the lifespan, modelled via spectral peaks (Central frequency, power, bandwidth) and 1/f noise activity (slope and intercept) respectively. Secondly, how this Resting-State (RS) baseline shift in temporal dynamics of the signal is associated with various aspects of visual short-term memory (VSTM). Our results suggest that age-associated global change in noisy baseline affects global information processing and crucially impacts the oscillatory features, which relates to more local processing and selective behavioural measures in the VSTM task. Moreover, we suggest that the task-related differences observed across age groups are due to the baseline shift of periodic and aperiodic features. Significant statementAging is accompanied by the decline in cognitive functions and age itself is a major risk factor for Alzheimers Disease and other neurological conditions. Our study provides MEG 1/f aperiodic and periodic markers across the healthy adult lifespan and shows that different frequency bands and their spectral features mediate age-related changes across different brain regions, in multiple cognitive and metacognitive domains, which not only provides us with a better understanding of the aging process but would also help in better prevention of cognitive impairments. A clear characterization of the association between baseline MEG temporal dynamics, healthy aging and cognition, is established in this study.

neuroscience↗

bioRxiv2020

Hemispheric asymmetry of entrained auditory steady state responses arise from the structural connectome

Structure-function relationships are fundamental to studies of neural systems, yet the mechanistic underpinnings of how structural networks shape hemispheric lateralization remain elusive. For instance, the asymmetric neuroanatomic embedding of primary auditory cortices was shown when connectivity with all other brain areas were considered. Concomitantly, functional lateralization e.g., left hemispheric dominance of speech processing and right hemispheric dominance of music perception, is widely acknowledged. The present article provides a parsimonious mechanistic explanation based on computational modelling and empirical recordings to demonstrate emergence of hemispheric lateralization of brain function. For validation of the model, empirical EEG recordings of auditory steady state responses (ASSR) were undertaken, and empirical findings suggest right hemispheric dominance at the level of cortical sources in binaural and monaural hearing conditions. Subsequently, we demonstrate the entrainment and phase of oscillations in connected brain regions based on a neurodynamic model constrained by empirically derived structural connectivity matrix from diffusion data. For relevance, we have taken into consideration time-delays in neural communication stemming from fibre distances and neuronal coupling arising from fibre densities. Based on relevant network parameters, we could demonstrate the emergence of right hemispheric dominance of ASSR in binaural and monaural hearing conditions when auditory cortical areas were chosen as triggers of entrained phase oscillators. Furthermore, we discovered emergence of left-lateralized functional response when bilateral Brocas area replaced auditory areas as triggers. Hence, a single unified mechanism based on entrainment of phase oscillators in a large-scale brain network could explain both emergence of right and left hemispheric laterality. Significance statementThe origin of hemispheric specialization of sensory processing is a fundamental question in neuroscience. For instance, speech and language are predominantly processed in the left hemispheric regions, while the right hemisphere is specialized for processing rhythmic, tonal, and melodic stimuli. Identification of the network mechanisms that give rise to such functional lateralization from structural constraints remains elusive. In the present study, we simulate neural activity observed during human EEG recordings of auditory steady-state responses from a biophysically realistic large-scale model constrained by underlying structural connectivity. Subsequently, we demonstrate how hemispheric lateralization of brain responses to sensory stimuli emerge from the time-delayed interactions among whole-brain neuronal ensembles.

neuroscience↗

bioRxiv2020

Contextual Prediction Errors Reorganize Episodic Memories in Time

Episodic memories are contextual experiences ordered in time. This is underpinned by associative binding between events within the same contexts. The role of prediction errors in strengthening declarative memory is well established but has not been investigated in the time dimension of complex episodic memories. Here we used 3-day movie viewing paradigm to test the hypothesis that contextual prediction errors leads to temporal organization of sequential memory processing. Our main findings uncover that prediction errors lead to changes in temporal organization of events, secondly, new unexpected sequences show as high accuracy as control sequences viewed repeatedly, and these effects are specifically due to prediction errors, and not novel associations. A drift-diffusion modelling further revealed a lower decision threshold for the newer, unexpected sequences compared to older sequences reflected by their faster recall leads to reorganization of episodes in time. Moreover, we found individual decision threshold could significantly predict their relative speed of sequence memory recall. Taking together our results suggest a temporally distinct role for prediction errors in ordering sequences of events in episodic memory.

neuroscience↗

bioRxiv2020

Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

Brain network modeling studies are often limited with respect to the number of data features fitted, although capturing multiple empirical features is important to validate the models overall biological plausibility. Here we construct personalized models from multimodal data of 50 healthy individuals (18-80 years) with The Virtual Brain and demonstrate that an individuals brain has its own converging optimal working point in the parameter space that predicts multiple empirical features in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We further show that bimodality in the alpha band power - as an explored novel feature - arises as a function of global coupling and exhibits inter-regional differences depending on the degree. Reliable inter-individual differences with respect to these optimal working points were found that seem to be driven by the individual structural rather than by the functional connectivity. Our results provide the groundwork for future multimodal brain modeling studies.

neuroscience↗

bioRxiv2020

PSP2, a gene encoding RGG-motif protein, is a novel suppressor of clathrin heavy chain deficiency

Clathrin, made up of the heavy- and light-chains, constitutes one of the most abundant protein in vesicles involved in intracellular protein trafficking and endocytosis. YPR129W, which encodes RGG-motif containing translation repressor was identified as a part of multi-gene construct (SCD6) that suppressed clathrin deficiency. However, the contribution of YPR129W alone in suppressing clathrin deficiency has not been documented. In this study we identify YPR129W as a necessary and sufficient gene in a multigene construct SCD6 that suppresses clathrin deficiency. Importantly, we identify cytoplasmic RGG-motif protein encoding gene PSP2 as a novel suppressor of clathrin deficiency. Three other RGG-motif protein encoding genes SBP1, DED1 and GBP2 do not suppress clathrin deficiency. DHH1, a DEAD-box RNA helicase with translation repression activity also fails to rescue clathrin deficiency. -factor secretion assay suggests that suppression of clathrin deficiency by SCD6 and PSP2 is not mediated by the rescue of the trans-Golgi network (TGN) protein sorting defect observed in the absence of CHC1. Detailed domain analysis of the two suppressors reveals that the RGG-motif of both Scd6 and Psp2 is important for suppressing clathrin deficiency. Additionally, the Lsm domain deletion as well as the arginine to alanine mutation in the arginine methylation defective (AMD) mutant render Scd6 defective in suppressing clathrin deficiency. Overall based on our results using SCD6 and PSP2 proteins, we identify a novel role of RGG-motif in suppressing clathrin deficiency. Since both the suppressors are RNA-binding granule-resident proteins, this study opens an exciting avenue for exploring the connection between clathrin function and cytoplasmic RNA metabolism.

cell biology↗

bioRxiv2020

Metabolism of long-chain fatty acids affects disulfide bond formation in Escherichia coli and activates envelope stress response pathways as a combat strategy

The envelope of gram-negative bacteria serves as the first line of defense against environmental insults. Therefore, its integrity is continuously monitored and maintained by several envelope stress response (ESR) systems. Due to its oxidizing environment, the envelope represents an important site for disulfide bond formation. In Escherichia coli, the periplasmic oxidoreductase, DsbA introduces disulfide bonds in substrate proteins and transfers electrons to the inner membrane oxidoreductase, DsbB. Under aerobic conditions, the reduced form of DsbB is re-oxidized by ubiquinone, an electron carrier in the electron transport chain (ETC). Given the critical role of ubiquinone in transferring electrons derived from the oxidation of reduced cofactors, we were intrigued whether metabolic conditions that generate a large number of reduced cofactors render ubiquinone unavailable for disulfide bond formation. To test this, here we investigated the influence of metabolism of long-chain fatty acid (LCFA), an energy-rich carbon source, on the redox state of the envelope. We show that LCFA degradation increases electron flow in the ETC. Further, we find that whereas cells metabolizing LCFAs exhibit several characteristics of insufficient disulfide bond formation, these hallmarks are averted in cells exogenously provided with ubiquinone. Importantly, the ESR pathways, Cpx and {sigma}E, are activated by envelope signals generated during LCFA metabolism, and these systems maintain proper disulfide bond formation. We find that {sigma}E downregulation hampers disulfide bond formation only in the absence of Cpx, and amongst the two ESR systems, only Cpx senses redox-dependent signal and is induced to a greater extent by LCFAs. Therefore, we argue that Cpx is the primary ESR that senses and maintains envelope redox homeostasis. Taken together, our results demonstrate an intricate relationship between cellular metabolism and disulfide bond formation dictated by ETC and ESR, and provide the basis for examining whether similar mechanisms control envelope redox status in other gram-negative bacteria. Author summaryDisulfide bonds contribute to the folding and stability of many extracytoplasmic proteins in all domains of life. In gram-negative bacteria, including Escherichia coli, disulfide bond formation occurs in the oxidizing environment of the periplasmic space enclosed within the outer and inner membrane layers of the envelope. Because disulfide-bonded proteins are involved in diverse biological processes, bacteria must monitor the envelope redox status and elicit an appropriate response when perturbations occur; however, these mechanisms are not well elucidated. Here, we demonstrated that the metabolism of an energy-rich carbon source, long-chain fatty acid (LCFA) hampers disulfide bond formation in E. coli. An envelope stress response (ESR) system, Cpx, senses this redox imbalance and maintains proper disulfide bond formation. The {sigma}E pathway, another ESR system, plays an ancillary role in maintaining redox homeostasis. LCFA metabolism, disulfide bond formation, and ESR systems have independently been implicated in the pathogenesis of several gram-negative bacteria. The present study sets the basis to explore whether LCFA metabolism impacts the virulence of these bacteria by influencing the redox status of their envelope and activation of ESR pathways.

microbiology↗

bioRxiv2019

Alpha oscillations for processing saliency are generated by common cortical networks in visual attention tasks spanning wide spatial and spatio-temporal scales

The Ventral Attention Network (VAN) is involved in reorienting attention from an ongoing task when a salient (pop-out) stimulus is detected in the environment. Previous neuroimaging studies have extensively evaluated the structural and functional connectivity of the VAN. However, directed effective connectivity within the network and the neural oscillations driving it still remain elusive. Functional magnetic resonance imaging (fMRI) studies have not been able to address this issue due to lack of appropriate temporal resolution required to capture the process of reorientation. In this study, we recorded scalp electroencephalography (EEG) and behavioural data from healthy human volunteers, obtained saliency-specific spectral changes, localized the sources underlying the spectral power modulations with individual-specific structural MRI scans, reconstructed the waveforms of the sources and investigated the causal relationships between the areas of the VAN using Granger causality (GC). Using a custom-designed experiment involving visual search on static images and a dynamic motion tracking task, we investigated the neural processing of salient distractors operating at very slow and very fast time scales, respectively. Our results revealed how a task-independent but context-specific VAN encompassing the right insula, the right lateral pre-frontal cortex, the anterior and the posterior right temporo-parietal junction communicating in the alpha frequency band (8-12 Hz) supports saliency processing.

neuroscience↗

bioRxiv2019

How do thalamocortical connections shape causal structure among resting state brain networks during aging?

The human brain undergoes significant structural and functional changes across the lifespan. Our current understanding of the underlying causal relationships of dynamical changes in functional connectivity with age is limited. On average, functional connectivity within resting-state networks (RSNs) weakens in magnitude, while connections between RSNs tend to increase with age. Recent studies show that effective connectivity within and between large scale resting-state functional networks changes over the healthy lifespan. The vast majority of previous studies have focused primarily on characterizing cortical networks, with little work exploring the influence of subcortical nodes such as the thalamus on large-scale network interactions across the lifespan. Using directed connectivity and weighted net causal outflow measures applied to resting-state fMRI data, we examine the age-related changes in both cortical and thalamocortical causal interactions within and between RSNs. The three core neurocognitive networks from the triple network theory (default mode: DMN, salience: SN, and central executive: CEN) were identified independently using ICA and spatial matching of hub regions with these important RSNs previously reported in the literature. Multivariate granger causal analysis (GCA) was performed to test for directional connectivity and weighted causal outflow between selected nodes of RSNs accounting for thalamo-cortical interactions. Firstly, we observe that within-network causal connections become progressively weaker with age, and network dynamics are substantially reconfigured via strong thalamic drive particularly in the young group. Our findings manifest stronger between-network directional connectivity, which is further strongly mediated by the SN in flexible co-ordination with the CEN, and DMN in the old group compared with the young group. Hence, causal within- and between- triple network connectivity largely reflects age-associated effects of resting-state functional connectivity. Thalamo-cortical causality effects on the triple networks with age were next explored. We discovered that left and right thalamus exhibit substantial interactions with the triple networks and play a crucial role in the reconfiguration of directed connections and within network causal outflow. The SN displayed directed functional connectivity in strongly driving both the CEN and DMN to a greater extent in the older group. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings based on directed functional connectivity and weighted causal outflow measures strengthen the hypothesis that balancing within and between network connectivity is perhaps critical for the preservation and flexibility of cognitive functioning with aging.

neuroscience↗

bioRxiv2019

Biophysical mechanisms to relate large-scale brain network dynamics to individual-specific variability of perception

Perception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants McGurk susceptibility was negatively correlated to their alpha-band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses - representing the sensory specific auditory/visual areas and modality non-specific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling result in a decrease in alpha band global coherence - mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception & action task paradigms.

neuroscience↗

bioRxiv2019

Purification of native CCL7 and its functional interaction with selected chemokine receptors

Chemokine receptors form a major sub-family of G protein-coupled receptors (GPCRs) and they are involved in a number of cellular and physiological processes related to our immune response and regulation. A better structural understanding of ligand-binding, activation, signaling and regulation of chemokine receptors is very important to design potentially therapeutic interventions for human disorders arising from aberrant chemokine signaling. One of the key limitations in probing the structural details of chemokine receptors is the availability of large amounts of purified, homogenous and fully functional chemokine ligands, and the commercially available products, are not affordable for in-depth structural studies. Moreover, production of uniformly isotope-labeled chemokines, for example, suitable for NMR-based structural investigation, also remains challenging. Here, we have designed a streamlined approach to express and purify the human chemokine CCL7 as well as its 15N-, 15N/13C-, 2H/15N/13C-isotope-labeled derivatives, at milligram levels using E. coli expression system. Purified CCL7 not only maintains a well-folded three-dimensional structure as analyzed using circular dichroism and 1H/15N NMR but it also induces coupling of heterotrimeric G-proteins and {beta}-arrestins for selected chemokine receptors in cellular system. Our strategy presented here may be applicable to other chemokines and therefore, provide a potentially generic and cost-effective approach to produce chemokines in large amounts for functional and structural studies.

biochemistry↗

bioRxiv2019

Differential remodeling of the electron transport chain is required to support TLR3 and TLR4 signaling and cytokine production in macrophages

Increasing evidence suggests that mitochondria play a critical role in driving innate immune responses against bacteria and viruses. However, it is unclear if differential reprogramming of mitochondrial function contributes to the fine tuning of pathogen specific immune responses. Here, we found that TLR3 and TLR4 engagement on murine bone marrow derived macrophages was associated with differential remodeling of electron transport chain complex expression. This remodeling was associated with differential accumulation of mitochondrial and cytosolic ROS, which were required to support ligand specific inflammatory and antiviral cytokine production. We also found that the magnitude of TLR3, but not TLR4, responses were modulated by glucose availability. Under conditions of low glucose conditions, TLR3 engagement was associated with increased ETC complex III expression, increased mitochondrial and cytosolic ROS and increased inflammatory and antiviral cytokine production. This amplification was selectively reversed by targeting superoxide production from the outer Q-binding site of the ETC complex III. These results suggest that ligand specific modulation of the ETC may act as a rheostat that fine-tunes innate immune responses via mitochondrial ROS production. Modulation of these processes may represent a novel mechanism to modulate the nature as well as the magnitude of antiviral versus inflammatory immune responses.

immunology↗

bioRxiv2019

Conformational sensors and domain-swapping reveal structural and functional differences between β-arrestin isoforms

Desensitization, signaling and trafficking of G protein-coupled receptors (GPCRs) are critically regulated by multifunctional adaptor proteins, {beta}-arrestins ({beta}arrs). The two isoforms of {beta}arrs ({beta}arr1 and 2) share a high degree of sequence and structural similarity, still however, they often mediate distinct functional outcomes in the context of GPCR signaling and regulation. A mechanistic basis for such a functional divergence of {beta}arr isoforms is still lacking. Using a set of complementary approaches including antibody fragment based conformational sensors, we discover structural differences between {beta}arr1 and 2 upon their interaction with activated and phosphorylated receptors. Interestingly, domain swapped chimeras of {beta}arrs display robust complementation in functional assays thereby, linking the structural differences between the receptor-bound {beta}arr1 and 2 with their divergent functional outcomes. Our findings reveal important insights into the ability of {beta}arr isoforms to drive distinct functional outcomes, and underscore the importance of integrating this aspect in the current framework of biased agonism.

biophysics↗

bioRxiv2019

Parallel Streams to Interconnected Networks: Organization and Reorganization of Cortical Processing During Visual Perception and Action

Visual dual stream theory posits that two distinct neural pathways of specific functional significance originate from primary visual areas and reach the inferior temporal (ventral) and posterior parietal areas (dorsal). However, there are several unresolved questions concerning the fundamental aspects of this theory. For example, is the functional dissociation between ventral and dorsal stream driven by features in input stimuli or is it driven by categorical differences between visuo-perceptual and visuo-motor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between two streams? We addressed these questions using fMRI recordings on healthy human volunteers and employing stimuli and tasks that can tease out the divergence between visuo-perceptual and visuo-motor models of dual stream theory. fMRI scans were repeated after seven practice sessions that were conducted in a non-MRI environment to investigate the effects of neuroplasticity. Brain activation analysis supports an input-based functional dissociation and existence of context-dependent neuroplasticity in dual stream areas. Intriguingly, premotor cortex activation was observed in the position perception task and distributed deactivated regions were observed in all perception tasks thus, warranting a network level analysis. Dynamic causal modelling (DCM) analysis incorporating activated and deactivated brain areas during perception tasks indicates that the brain dynamics during visual perception and actions could be interpreted within the framework of predictive coding. Effectively, the network level findings point towards the existence of more intricate context-driven functional networks selective of "what" and "where" information rather than segregated streams of processing along ventral and dorsal brain regions.

neuroscience↗

bioRxiv2019

Identification and classification of hubs in miRNA target gene networks in human neural stem/progenitor cells following Japanese encephalitis virus infection

Micro RNA dysregulation is observed in many viral diseases. RNA viruses modulate host miRNA machinery for their own benefit. JEV, a neurotropic RNA virus has been reported to manipulate several miRNAs in neuron or microglia. However, no report indicates a complete sketch of the miRNA profile of NSPCs contributing to viral persistence; hence being focused in our current study. We performed a miRNA array of 84 miRNAs in human neuronal progenitor cell line and primary neural precursor cells isolated from aborted foetus. Several fold down-regulation of hsa-miR-9-5p, hsa-miR-22-3p, hsa-miR-124-3p and hsa-miR-132-3p were found in both of the cells. Subsequently, we screened for the target genes of these miRNAs and looked for the important biological pathways the genes significantly regulate. Then we sorted out the target genes which are involved in two or more than two pathways. We constructed a protein-protein interaction (PPI) network of the miRNA target genes based on their interaction patterns. A binary adjacency matrix for each gene network was prepared. Different modules or communities were identified in those networks using community detection algorithms. Mathematically, we identified the hub genes by analyzing their degree centrality and participation co-efficient in the network. The hub genes were classified either as provincial (P<0.4) or connector hubs (P>0.4). We validated the expression of hub genes in both cell line and primary cells through qRT-PCR post JEV infection and respective miR-mimic transfection. Taken together, our findings highlight the importance of specific target gene networks of miRNAs affected by JEV infection in NSPCs.\n\nImportanceJEV damages the neural stem/progenitor cell population of mammalian brain. However, JEV induced alteration in miRNA expression pattern of the cell population remains an open question, hence warrants our present study. In this study, we specifically address the down-regulation of four miRNAs and we prepared a protein-protein interaction network of miRNA target genes. We identified two types of hub genes in the PPI network namely connector hubs and provincial hubs. These two types of miRNA target hub genes critically influence the participation strength in the networks and thereby significantly influence up and down regulation in several key biological pathways. Computational analysis of the PPI networks identifies key protein interactions and hubs in those modules which opens up the possibility of precise identification and classification of host factors for viral infection in NSPCs and how RNA viruses modulate host miRNA machinery for their own benefit post JEV infection to the cells.

microbiology↗

bioRxiv2018

Lifespan driven reorganization of the global network dynamics unfold on a multifrequency landscape

Healthy ageing is accompanied by changes to spontaneous electromagnetic oscillations. At the macroscopic scale, previous studies have quantified the basic features, e.g., power and frequencies in rhythms of interest from the perspective of attention, perception, learning and memory. On the other hand, signatures and modes of neural communication have recently been argued to be identifiable from global measures applied on neuro-electromagnetic data such as global coherence that quantifies the degree of togetherness of distributed neural oscillations and metastability that parametrizes the transient dynamics of the network switching between successive stable states. Here, we demonstrate that global coherence and metastability can be informative measures to track healthy ageing dynamics over lifespan and together with the traditional spectral measures provides an attractive explanation of neuronal information processing. Finding normative patterns of brain rhythms in resting state MEG would naturally pave the way for tracking task relevant metrics that could crucially determine cognitive flexibility and performance. While previously reported observations of a reduction in peak alpha frequency and increased beta power in older adults are reflective of changes at individual sensors (during rest and task), global coherence and metastability truly pinpoint the underlying coordination dynamics over multiple brain areas across the entire lifespan. In addition to replication of the previous observations in a substantially larger lifespan cohort than what was previously reported, we also demonstrate, for the first time to the best of our knowledge, age related changes in coherence and metastability in signals over time scales of neuronal processing. Furthermore, we observed a marked frequency dependence in changes in global coordination dynamics, which, coupled with the long held view of specific frequency bands sub-serving different aspects of cognition, hints at differential functional processing roles for slower and faster brain dynamics.

neuroscience↗

bioRxiv2018

Multiscale dynamic mean field (MDMF) model to relate resting state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease.

Previous neuro-computational studies have established the connection of spontaneous resting-state brain activity with "large-scale" neuronal ensembles using dynamic mean field approach and showed the impact of local excitatory-inhibitory (E-I) balance in sculpting dynamical patterns. Here, we argue that whole brain models that link multiple scales of physiological organization namely brain metabolism that governs synaptic concentrations of gamma-aminobutyric acid (GABA) and glutamate on one hand and neural field dynamics that operate on the macroscopic scale. The multiscale dynamic mean field (MDMF) model captures the synaptic gating dynamics over a cortical macrocolumn as a function of neurotransmitter kinetics. Multiple MDMF units were placed in brain locations guided by an anatomical parcellation and connected by tractography data from diffusion tensor imaging. The resulting whole-brain model generates the resting-state functional connectivity and also reveal that optimal configurations of glutamate and GABA captures the dynamic working point of the brain, that is the state of maximum metsatability as observed in BOLD signals. To demonstrate test-retest reliability we validate the observation that healthy resting brain dynamics is governed by optimal glutamate-GABA configurations using two different brain parcellations for model set-up. Furthermore, graph theoretical measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) on the functional connectivity generated from healthy and pathological brain network studies could be explained by the MDMF model. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to whole-brain resting-state network topology in health and disease.

neuroscience↗

bioRxiv2018

Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the age, disease phenotype and their interaction in the whole brain transient connectivity patterns. Contrasting with most extant literature in the present study, we investigated precisely how age and disease phenotypes factors into dynamic changes in functional connectivity of TD and ASD using resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years) and adolescents (12-17 years), and adults (18+) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures: flexibility, cohesion strength and disjointness were explored for each subject to characterize the differences between ASD and TD.\n\nIn ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hypervariability relates to the symptom severity in ASD. We found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with the flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing the high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

neuroscience↗

bioRxiv2018

Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics

Over the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.

neuroscience↗