Search

Previous neuroimaging studies have extensively evaluated the structural and functional connectivity of the Ventral Attention Network (VAN) and its role in reorienting attention in the presence of a salient (pop-out) stimulus. However, a detailed understanding of the "directed" functional connectivity within the VAN during the process of reorientation remains elusive. Functional magnetic resonance imaging (fMRI) studies have not adequately addressed this issue due to a lack of appropriate temporal resolution required to capture this dynamic process. The present study investigates the neural changes associated with processing salient distractors operating at a slow and a fast time scale using custom-designed experiment involving visual search on static images and dynamic motion tracking, respectively. We recorded high-density scalp electroencephalography (EEG) from healthy human volunteers, obtained saliency-specific behavioral and spectral changes during the tasks, localized the sources underlying the spectral power modulations with individual-specific structural MRI scans, reconstructed the waveforms of the sources and finally, investigated the causal relationships between the sources using spectral Granger-Geweke Causality (GGC). We found that salient stimuli processing, across tasks with varying spatio-temporal complexities, involves a characteristic modulation in the alpha frequency band which is executed primarily by the nodes of the VAN constituting the temporo-parietal junction (TPJ), the insula and the lateral prefrontal cortex (lPFC). The directed functional connectivity results further revealed the presence of bidirectional interactions among prominent nodes of right-lateralized VAN, corresponding only to the trials with saliency. Thus, our study elucidates the invariant network mechanisms for processing saliency in visual attention tasks across diverse time-scales.

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 declarative memory is well established but has not been investigated in the time dimension of complex episodic memories. Here we combine these two properties of episodic memory, extend them into the temporal domain and demonstrate that prediction errors in different naturalistic contexts lead to changes in the temporal ordering of event structures in them. The wrongly predicted older sequences were weakened despite reactivating them after. Interestingly, the newly encoded sequences with prediction errors, seen once, showed as high accuracy as control sequences which were viewed repeatedly without change. Drift-diffusion modelling revealed a lower decision threshold for the newer sequences compared to older sequences, reflected by their faster recall. Moreover participants’ adjustments to their decision threshold could significantly predict their relative speed of sequence memory recall. This suggests a temporally distinct and adaptive role for prediction errors in learning and reorganizing episodic sequences.

Brain state classification using electroencephalography (EEG) finds applications in both clinical and non-clinical contexts, such as detecting sleep states or perceiving illusory effects during multisensory McGurk paradigm, respectively. Existing literature mostly considers recordings of EEG electrodes that cover the entire head. However, for real world applications, wearable devices that encompass just one (or a few) channels are desirable, which make the classification of EEG states even more challenging. With this as background, we applied variants of data driven Empirical Mode Decomposition (EMD) on McGurk EEG, which is an illusory perception of speech when the movement of lips does not match with the audio signal, for classifying whether the perception is affected by the visual cue or not. After applying a common pre-processing pipeline, we explored four EMD based frameworks to extract EEG features, which were classified using Random Forest. Among the four alternatives, the most effective framework decomposes the ensemble average of two classes of EEG into their respective intrinsic mode functions forming the basis on which the trials were projected to obtain features, which on classification resulted in accuracies of 63.66% using single electrode and 75.85% using three electrodes. The frequency band which plays vital role during audio-visual integration was also studied using traditional band pass filters. Of all, Gamma band was found to be the most prominent followed by alpha and beta bands which contemplates findings from previous studies.

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.

Visual cues from the speaker’s face influence the perception of speech. An example of this influence is demonstrated by the McGurk-effect where illusory (cross-modal) sounds are perceived following presentation of incongruent audio–visual (AV) stimuli. Previous studies report the engagement of specific cortical modules that are spatially distributed during cross-modal perception. However, the limits of the underlying representational space and the cortical network mechanisms remain unclear. In this combined psychophysical and electroencephalography (EEG) study, the participants reported their perception while listening to a set of synchronous and asynchronous incongruent AV stimuli. We identified the neural representation of subjective cross-modal perception at different organizational levels — at specific locations in sensor space and at the level of the large-scale brain network estimated from between-sensor interactions. We identified an enhanced positivity in the event-related potential peak around 300 ms following stimulus onset associated with cross-modal perception. At the spectral level, cross-modal perception involved an overall decrease in power at the frontal and temporal regions at multiple frequency bands and at all AV lags, along with an increased power at the occipital scalp region for synchronous AV stimuli. At the level of large-scale neuronal networks, enhanced functional connectivity at the gamma band involving frontal regions serves as a marker of AV integration. Thus, we report in one single study that segregation of information processing at individual brain locations and integration of information over candidate brain networks underlie multisensory speech perception.

This article presents behavior and EEG dataset collected from 19 healthy human volunteers (10 females) in the age group of 21–29 (mean = 26.9, SD = ±2.15) years at National Brain Research Centre, India during a psychophysical paradigm customized to characterize the brain network interactions during saliency processing. We provide all the raw stimulus files used in developing the experimental paradigm of the linked research article “Organization of directed functional connectivity among nodes of ventral attention network reveals the common network mechanisms underlying saliency processing across distinct spatial and spatio-temporal scales” [1] for replication and use by researchers across various cohorts of the population. Pre-processed EEG time-series segmented into epochs corresponding to three experimental trial conditions, across two visual attention tasks testing the effect of salient distractors on goal-driven tasks are provided. The dataset also includes reaction times corresponding to individual trials. Additionally, structural MRI files corresponding to each individual and 3D EEG sensor locations of all volunteers are provided to assist in accurate source localization. Therefore, the presented dataset will not only facilitate the conventional time resolved EEG analysis like evoked activity and time-frequency analysis at the sensor level but will also facilitate the investigation of source level analysis like global coherence or phase-amplitude coupling within selected regions of the brain.

The brain during healthy aging exhibits gradual deterioration of structure but maintains a high level of cognitive ability. These structural changes are often accompanied by reorganization of functional brain networks. Existing neurocognitive theories of aging have argued that such changes are either beneficial or detrimental. Despite numerous empirical investigations, the field lacks a coherent account of the dynamic processes that occur over our lifespan. Taking advantage of the recent developments in whole-brain computational modeling approaches, we hypothesize that the continuous process of aging can be explained by the concepts of metastability - a theoretical framework that gives a systematic account of the variability of the brain. This hypothesis can bridge the gap between existing theories and the empirical findings on age-related changes.

Cognitive processes are mediated by communication among multiple brain areas that individually exhibit oscillatory neuro-electromagnetic signals emerging from a cascade of complex physiological processes. Several studies have reported that aging changes the intrinsic properties of neural oscillations, both in resting state and in the context of cognitive tasks. For example, the amplitude of resting and motor-related beta band oscillations (16-25 Hz) is typically found to be higher in the older population compared to the younger population. Similarly, a substantial number of reports have highlighted that peak alpha frequency (8-12 Hz) is lowered in neurodegenerative disorders and in healthy aging. How such observations emerge from underlying neuronal signal processing mechanisms remains elusive. Furthermore, how do the spatiotemporal organization of these rhythms support the current neurobiological theories of aging is poorly understood. Here we addressed these issues using the resting state magnetoencephalogram (MEG) data from a large cross-sectional cohort consisting of 650 human participants with age range 18-90 covering the entire adult lifespan. Concurring with previous research in smaller cohorts, we found a consistent increase in the power of beta oscillations and a decrease in the peak alpha frequency as a function of age. Subsequently, we found significant posterior to anterior shifts in the spectral topographies of both alpha and beta bands. To reconcile these observations with a comprehensive theory at the level of network level signal processing, we computed the whole-brain global coherence that captures the degree of communication among nodes of a large-scale network as a function of aging. The global coherence among MEG signals increased with age in slower time-scales i.e. delta (1-3 Hz) and theta (3-7 Hz) frequencies. Simultaneously, global coherence decreased for faster timescales i.e. alpha (8-12 Hz) and beta (16-25 Hz) frequencies. Further, using the measure of metastability that quantifies the divergence of a network from a stable synchronous state, we characterized the dispersion of information processing in different frequency bands. Putting together, our study reveals how neurobiological theories of aging such as posterior to anterior shifts of sensory and cognitive processing, dynamic workspace hypothesis can all be reconciled using restingstate MEG data. We could highlight how the temporal structure of MEG signals is representative of a more comprehensive understanding of large-scale network mechanisms that govern lifespan dynamics.

Contemporary mental health practice primarily centers around the neurobiological and psychological processes at the individual level. However, a more careful consideration of interpersonal and other group-level attributes (e.g., interpersonal relationship, mutual trust/hostility, interdependence, and cooperation) and a better grasp of their pathology can add a crucial dimension to our understanding of mental health problems. A few recent studies have delved into the interpersonal behavioral processes in the context of different psychiatric abnormalities. Neuroimaging can supplement these approaches by providing insight into the neurobiology of interpersonal functioning. Keeping this view in mind, we discuss a recently developed approach in functional neuroimaging that calls for a shift from a focus on neural information contained within brain space to a multi-brain framework exploring degree of similarity/dissimilarity of neural signals between multiple interacting brains. We hypothesize novel applications of quantitative neuroimaging markers like inter-subject correlation that might be able to evaluate the role of interpersonal attributes affecting an individual or a group. Empirical evidences of the usage of these markers in understanding the neurobiology of social interactions are provided to argue for their application in future mental health research.

We assess change in metastability to characterize age-effects on the dynamic repertoire of the functional networks at rest. Resting state fMRI signals from each subject (N=48) have been used and metastability is evaluated as the standard deviation of mean phase synchrony of BOLD signals across whole-brain as well as across known resting state networks. The results suggest that significant whole-brain metastability changes occur between middle to old age. We also demonstrate that static time-averaged FC largely undermines age-effects on the interaction between functional networks. Discriminant Function Analysis reveals existence of two different patterns of change in metastability, which maximally discriminates between two different processes of maturation and ageing.

Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300–600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our study indicates that the temporal integration underlying multisensory speech perception requires to be understood in the framework of large-scale functional brain network mechanisms in addition to the established cortical loci of multisensory speech perception.

Network analysis through graph theory provides a quantitative approach to characterize specific proteins and their constituent assemblies that underlie host-pathogen interactions. In the present study, graph theory was used to analyze the interactome designed out of 50 differentially expressing proteins from proteomic analysis of Chandipura Virus (CHPV, Family: Rhabdoviridae) infected mouse brain tissue to identify the primary candidates for intervention. Using the measure of degree centrality, that quantifies the connectedness of a single protein within a milieu of several other interacting proteins, DJ-1 was selected for further molecular validation. To elucidate the generality of DJ-1’s role in propagating infection its role was also monitored in another RNA virus, Japanese Encephalitis Virus (JEV, Family: Flaviviridae) infection. Concurrently, DJ-1 got over-expressed in response to reactive oxygen species (ROS) generation following viral infection which in the early phase of infection migrated to mitochondria to remove dysfunctional mitochondria through the process of mitophagy. DJ-1 was also observed to modulate the viral replication and interferon responses along with low-density lipoprotein (LDL) receptor expression in neurons. Collectively these evidences reveal a comprehensive role for DJ-1 in neurotropic virus infection in the brain.

Multisensory processing involves participation of individual sensory streams, e.g., vision, audition to facilitate perception of environmental stimuli. An experimental realization of the underlying complexity is captured by the “McGurk-effect”- incongruent auditory and visual vocalization stimuli eliciting perception of illusory speech sounds. Further studies have established that time-delay between onset of auditory and visual signals (AV lag) and perturbations in the unisensory streams are key variables that modulate perception. However, as of now only few quantitative theoretical frameworks have been proposed to understand the interplay among these psychophysical variables or the neural systems level interactions that govern perceptual variability. Here, we propose a dynamic systems model consisting of the basic ingredients of any multisensory processing, two unisensory and one multisensory sub-system (nodes) as reported by several researchers. The nodes are connected such that biophysically inspired coupling parameters and time delays become key parameters of this network. We observed that zero AV lag results in maximum synchronization of constituent nodes and the degree of synchronization decreases when we have non-zero lags. The attractor states of this network can thus be interpreted as the facilitator for stabilizing specific perceptual experience. Thereby, the dynamic model presents a quantitative framework for understanding multisensory information processing.

A key challenge in global mental health care is the paucity of treatment for neuropsychiatric disorders, to which detection gaps contribute greatly. Most of these conditions are diagnosed either very late, or not at all.1 In particular, low-income and middle-income countries (LMICs) experience three barriers: low help-seeking behaviour, in part due to limited awareness and stigma associated with mental disorders; not enough mental health professionals to conduct diagnostic assessments; and reliance on diagnostic interviews, many of which are time-intensive or proprietary and might not be cross-culturally valid.

Bimanual coordination engages a distributed network of brain areas, the spatiotemporal organization of which has given rise to intense debates. Do bimanual movements require information processing in the same set of brain areas that are engaged by movements of the individual components (left and right hands)? Or is it necessary that other brain areas are recruited to help in the act of coordination? These two possibilities are often considered as mutually exclusive, with studies yielding support for one or the other depending on techniques and hypotheses. However, as yet there is no account of how the two views may work together dynamically. Using the method of Mode-Level Cognitive Subtraction (MLCS) on high density EEG recorded during unimanual and bimanual movements, we expose spatiotemporal reorganization of large-scale cortical networks during stable inphase and antiphase coordination and transitions between them. During execution of stable bimanual coordination patterns, neural dynamics were dominated by temporal modulation of unimanual networks. At instability and transition, there was evidence for recruitment of additional areas. Our study provides a framework to quantify large-scale network mechanisms underlying complex cognitive tasks often studied with macroscopic neurophysiological recordings.

The timing of neural responses to ongoing behavior is an important measure of the underlying neural processes. Neural processes are distributed across many different brain regions and measures of the timing of neural responses are routinely used to test relationships between different brain regions. Testing detailed models of functional neural circuitry underlying behavior depends on extracting information from single trials. Despite their importance, existing methods for analyzing the timing of information in neural signals on single trials remain limited in their scope and application. We develop a novel method for estimating the timing of information in neural activity that we use to measure selection times, when an observer can reliably use observations of neural activity to select between two descriptions of the activity. The method is designed to satisfy three criteria: selection times should be computed from single trials, they should be computed from both spiking and local field potential (LFP) activity, and they should allow us to make comparisons between different recordings. Our approach characterizes the timing of information in terms of an accumulated log-likelihood ratio (AccLLR), which distinguishes between two alternative hypotheses and uses the AccLLR to estimate the selection time. We develop the AccLLR procedure for binary discrimination using example recordings of spiking and LFP activity in the posterior parietal cortex of a monkey performing a memory-guided saccade task. We propose that the AccLLR method is a general and practical framework for the analysis of signal timing in the nervous system.

Animals and humans rapidly detect specific features of sounds, but the time courses of the underlying neural response for different stimulus categories is largely unknown. Furthermore, the intricate functional organization of auditory information processing pathways is poorly understood. Here, we computed neuronal response latencies from simultaneously recorded spike trains and local field potentials (LFPs) along the first two stages of cortical sound processing, primary auditory cortex (A1) and lateral belt (LB), of awake, behaving macaques. Two types of response latencies were measured for spike trains as well as LFPs: (1) onset latency, time-locked to onset of external auditory stimuli; and (2) selection latency, time taken from stimulus onset to a selective response to a specific stimulus category. Trial-by-trial LFP onset latencies predominantly reflecting synaptic input arrival typically preceded spike onset latencies, assumed to be representative of neuronal output indicating that both areas may receive input environmental signals and relay the information to the next stage. In A1, simple sounds, such as pure tones (PTs), yielded shorter spike onset latencies compared to complex sounds, such as monkey vocalizations (“Coos”). This trend was reversed in LB, indicating a hierarchical functional organization of auditory cortex in the macaque. LFP selection latencies in A1 were always shorter than those in LB for both PT and Coo reflecting the serial arrival of stimulus-specific information in these areas. Thus, chronometry on spike-LFP signals revealed some of the effective neural circuitry underlying complex sound discrimination.

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial-by-trial behavioral performance than existing models of neural information processing. Our results highlight the utility of the unified modeling framework for characterizing spike-LFP recordings obtained during behavioral performance.

Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We show that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are affected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, fronto-parietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest are achievable by re-establishment of balance. This study was funded by A.B. Ramalingaswami fellowship (BT/RLF/Re-entry/31/2011) and Innovative Young Bio-technologist Award (IYBA) (BT/07/IYBA/2013) from the Department of Biotechnology, Ministry of Science & Technology, Government of India. GD is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research ProjectPSI2013-42091-P, and funding from the European Union Seventh Framework Programme (FP7-ICT Human Brain Project (grant no. 60402)).