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The focal lesion, a form of biological perturbation damaging anatomical architecture, reasonably alters the normative healthy functional pattern but may recover over time. Nevertheless, how the brain counters deterioration in structure by global reshaping of functional connectivity (FC) after a lesion is largely unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict specific compensatory areas initiating lost excitatory-inhibitory (E-I) regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. Thereafter, a large-scale mean field model is deployed on top of a virtually lesioned structural connectome for characterizing the global brain dynamics and functional connectivity at the level of individual subjects. Despite inter-individual variability in SSAs, we found a general normative pattern in functional re-organization within the ipsi- and contra-lesional regions. The study demonstrates how SSAs and DSAs largely predict overlapping brain regions for different lesion centers/sites irrespective of the complexity of the lesion recovery process. The proposed computational framework captures the improvement of large-scale cortical cohesion by re-adjusting local inhibition. Our results further suggest that the predicted brain areas participating in recovery are not randomly distributed and widespread over the brain. Instead, the predicted brain areas are predominantly recruited from the ipsilesional hemisphere, barring a few regions from contra, suggesting that wiring proximity and similarity are the two major guiding principles of compensation-related utilization of hemisphere (CRUH) in the post-lesion FC re-organization process. Our finding further suggests that the re-organization of FC arises from the interplay between the underlying structural connectivity profile and the local inhibitory weights influencing compensatory coordinated brain dynamics during post-lesion recovery.

Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18–88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan—a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.

How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed net influence and flow. We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.

Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components - "phase-locked" and "non-phase-locked" to the stimulus. Phase-locked activity is often implicitly studied as Event-Related Potentials (ERPs), and the trial-averaged estimates - evoked potentials (EP) considered both time-locked and phase-locked to the stimulus. The non-phase-locked activity, on the other hand, refers to an increase in power in a narrow band or broadband frequencies in the signal emerging at variable phases from stimulus initiation. Both components are understood to stem from different neuronal mechanisms; hence, accurately characterizing them is of immense importance to neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a novel Concurrent Phaser Method (CPM) that simultaneously decomposes the two components. First, we establish that the single-trial separation of phase-locked and non-phase-locked power is an ill-posed problem. Second, using simulations where ground truth validation is possible, we elucidate how the estimation of non-phase-locked power gets biased by phase-locked power in the state-of-the-art averaging method and ways to resolve the issue using CPM. Next, we use two experimental EEG datasets - audio oddball and auditory steady-state responses (ASSR) to show that empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity. Thus, using ground truth validation from simulations and demonstration in real experimental scenarios, the efficacy of the proposed CPM is established.

The Moroccan Model Bilateral Investment Treaty (BIT) (2019) adds to a growing body of ‘new-generation’ model BITs that have fuelled the conversation on reforming the investment arbitration system. Broadly speaking, such agreements have attempted to rethink means of dispute resolution and traditional investment protection standards while adding detailed investor obligations and standards of conduct. In that context, this article discusses the provisions of the Moroccan Model BIT (2019), highlighting not only progressive developments but also areas of concern. Upon comparing the provisions of the BIT with those of other ‘new-generation’ investment agreements, as well as existing treaty practice, the authors offer the following findings.

A complete picture of how subcortical nodes, such as the thalamus, exert directional influence on large-scale brain network interactions across age remains elusive. Using directed functional connectivity and weighted net causal outflow on resting-state fMRI data, we provide evidence of a comprehensive reorganization within and between neurocognitive networks (default mode: DMN, salience: SN, and central executive: CEN) associated with age and thalamocortical interactions. We hypothesize that thalamus subserves both modality-specific and integrative hub role in organizing causal weighted outflow among large-scale neurocognitive networks. To this end, we observe that within-network directed functional connectivity is driven by thalamus and progressively weakens with age. Secondly, we find that age-associated increase in between CEN- and DMN-directed functional connectivity is driven by both the SN and the thalamus. Furthermore, left and right thalami act as a causal integrative hub exhibiting substantial interactions with neurocognitive networks with aging and play a crucial role in reconfiguring network outflow. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings strengthen the hypothesis that the thalamus is a key causal hub balancing both within- and between-network connectivity associated with age and maintenance of cognitive functioning with aging.

What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modeling, on a large-scale neuroimaging and behavioral data on a passive movie-watching task, we showed that emotions naturally arise due to ongoing uncertainty estimations about future outcomes in a hierarchical neural architecture. Several prefrontal subregions hierarchically encoded a lower-dimensional signal that highly correlated with the evolving uncertainty. Crucially, the lateral orbitofrontal cortex (lOFC) tracked the temporal fluctuations of this uncertainty and was predictive of the participants’ predisposition to anxiety. Furthermore, we observed a distinct functional double-dissociation within OFC with increased connectivity between medial OFC and DMN, while with that of lOFC and FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating an individual’s beliefs spontaneously with fluctuating outcome uncertainty in the lOFC. A biologically relevant and computationally crucial parameter in the theories of brain function, we propose uncertainty to be central to the definition of complex emotions.

Prediction errors in the brain are indexed by two event-related potentials – MMN and P300, which are elicited upon violation of regularity in the occurrence of repetitive stimuli. While MMN reflects the brain’s ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection, P300 is associated with cognitive processes such as update in working memory. Till date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. However, the relationship between these two ERPs, specifically in terms of their underlying cortical generators, in context of prediction error propagation along the hierarchical brain across multiple modalities is an open question. Our objective in this article is two-fold. First, we reconfirm previous reports regarding the generators of MMN and P300 in sensor space through source-space analysis using an accurate individual subject level co-registration of MRI and EEG data collected from healthy humans. We demonstrate that in multisensory environments, MMN and P300 markers represent “modality-specific” and “modality-independent” information processing, respectively. Advancing an earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that this temporal facilitation extends to even the later components of prediction error processing, using custom-designed experiments that allow comparisons across different modality combinations. Such knowledge can be of immense value in clinical research for determining the stages of various treatments in aging, schizophrenia and depression, and their efficacy on cognitive function.

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 visuoperceptual and visuomotor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between the 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 visuoperceptual and visuomotor variants 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 modeling 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 toward 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.

Burgeoning pieces of evidence support the idea that differences in the properties of large-scale cortical networks underlie perceptual variability. Here, employing psychophysical experiments in tandem with electroencephalogram (EEG) recordings, we explored the inter-individual and inter-trial differences in McGurk percept to probe the underlying brain dynamics before (prestimulus) and after (poststimulus) the stimulus presentation. Initially, based on our participants’ (n = 18) overall susceptibility to the McGurk effect, we categorized them into two groups - ’rare’ and ’frequent’ groups of perceivers. Subsequently, we investigated the differences in their neuronal dynamics manifested in the oscillatory properties of the prestimulus and compared it with the poststimulus EEG signals during the McGurk perception (audio- /pa/, video- /ka/) and, also during the perception of the congruent audiovisual (AV) stimuli (syllables /pa/, /ta/, and /ka/). We primarily focused on the power spectrum components (periodic oscillations and aperiodic components) and global coherence. At the inter-individual and inter-trial level, we observed that for the prestimulus duration, the ’rare’ perceivers’ aperiodic (or “background” 1/f) component elicited a lower beta band (15-30 Hz) and gamma band (31-45 Hz) activity than the ’frequent’ group of perceivers. Moreover, the periodic oscillations for the ’rare’ group showed significance in the alpha (8-12 Hz) and beta bands during the prestimulus time window. ’Rare’ perceivers also showed an increased global coherence in the theta (4-7 Hz), alpha, and beta bands but a lower gamma band activity than the ’frequent’ perceivers. Finally, ’rare’ perceivers elicited higher cross-hemispheric connectivity (imaginary coherence) than the ’frequent’ group at the source across different frequency bands, supporting the notion that the fluctuations in the distributed large-scale network underlie the perceptual variability in speech across individuals.

Both healthy and pathological aging exhibits gradual deterioration of structure but in-terestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically longrange anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism.

We propose that preservation of functional integration, estimated from measures of neural synchrony, is a key neurocompensatory mechanism associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at peak alpha frequency from Magnetoencephalography (MEG) data is invariant over lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging (DTI) data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delay associated with age-related degeneration of white matter tracts and thus, preserve neural synchrony. Together with analytical solutions for non-biological all-to-all connection scenarios, our model establishes the theoretical principles by which frequency slowing with age, frequently observed in the alpha band in diverse populations, can be viewed as an epiphenomenon of the underlying neurocompensatory mechanisms.

Understanding brain functions as an outcome of underlying neuro-cognitive network mechanisms in rest and task requires accurate spatiotemporal characterization of the relevant functional brain networks. Recent endeavours of the Neuroimaging community to develop the notion of dynamic functional connectivity is a step in this direction. A key goal is to detect what are the important events in time that delimits how one functional brain network defined by known patterns of correlated brain activity transitions into a “new” network. Such characterization can also lead to more accurate conceptual realization of brain states, thereby, defined in terms of time-resolved correlations. Nonetheless, identifying the canonical temporal window over which dynamic functional connectivity is operational is currently based on an ad-hoc selection of sliding windows that can certainly lead to spurious results. Here, we introduce a data-driven unsupervised approach to characterize the high dimensional dynamic functional connectivity into dynamics of lower dimensional patterns. The whole-brain dynamic functional connectivity states bearing functional significance for task or rest can be explored through the temporal correlations, both short and long range. The present study investigates the stability of such short- and long-range temporal correlations to explore the dynamic network mechanisms across resting state, movie viewing and sensorimotor action tasks requiring varied degrees of attention. As an outcome of applying our methods to the fMRI data of a healthy ageing cohort we could quantify whole-brain temporal dynamics which indicates naturalistic movie watching task is closer to resting state than the sensorimotor task. Our analysis also revealed an overall trend of highest short range temporal network stability in the sensorimotor task, followed by naturalistic movie watching task and resting state that remains similar in both young and old adults. However, the stability of neurocognitive networks in the resting state in young adults is higher than their older counterparts. Thus, healthy ageing related differences in quantification of network stability along task and rest provides a blueprint of how our approach can be used for cohort studies of mental health and neurological disorders.

A large number of intellectual property (IP) law cases have centred around works of popular culture. Traditionally, IP scholars have focused only on technical legal analysis, neglecting research on the aesthetic and cultural value of such works, or the experiences of creators. However, a growing body of interdisciplinary scholarship has changed this trend. This chapter highlights examples of such interdisciplinary scholarship, focusing on scholarship incorporating approaches from the humanities and empirical social sciences. The chapter discusses the methodologies and perspectives used in such works, and how they can complement traditional legal analysis.

Signal transmission in the brain propagates via distinct oscillatory frequency bands but the aperiodic component, 1/f activity, almost always co-exists which most of the previous studies have not sufficiently taken into consideration. We used a recently proposed parameterization model that delimits the oscillatory and aperiodic components of neural dynamics on lifespan aging data collected from human participants using magnetoencephalography (MEG). Since healthy aging underlines an enormous change in local tissue properties, any systematic relationship of 1/f activity would highlight their impact on the self-organized critical functional states. Furthermore, we have used patterns of correlation between aperiodic background and metrics of behavior to understand the domain general effects of 1/f activity. We suggest that age-associated global change in 1/f baseline alters the functional critical states of the brain affecting the global information processing impacting critically all aspects of cognition, e.g., metacognitive awareness, speed of retrieval of memory, cognitive load, and accuracy of recall through adult lifespan. This alteration in 1/f crucially impacts the oscillatory features peak frequency (PF) and band power ratio, which relates to more local processing and selective functional aspects of cognitive processing during the visual short-term memory (VSTM) task. In summary, this study leveraging on big lifespan data for the first time tracks the cross-sectional lifespan-associated periodic and aperiodic dynamical changes in the resting state to demonstrate how normative patterns of 1/f activity, PF, and band ratio (BR) measures provide distinct functional insights about the cognitive decline through adult lifespan.

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.

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 Alzheimer’s 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.

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.

Lockdowns are disease mitigation strategies that aim to contain the spread of an infection by restricting the interactions of its carriers. Lockdowns can thus have a considerable economic cost, which makes the identification of optimal lockdown windows that minimize both infection spread and economic disruption imperative. A well-known feature of complex dynamical systems is their sensitivity to the timing of external inputs. Hence, we hypothesized that the timing and duration of lockdowns should dictate lockdown outcomes. We demonstrate this concept computationally from two perspectives - Firstly, a stochastic "small-scale" Agent Based Model (ABM) of a Susceptible-Infected-Recovered (SIR) disease spread and secondly, a deterministic "large-scale" perspective using a multi-group SIR mass model with parameters determined from the SARS-CoV2 data in India. Lockdowns were implemented as an effective reduction of interaction probabilities in both models. This allowed us to evaluate the parametric variations of lockdown intensity and duration onto the dynamical properties of the infection spread over different connection topologies. We definitively show that the lockdown outcomes in both the stochastic small-scale and deterministic large-scale perspectives depend sensitively on the timing of its imposition and that it is possible to minimize lockdown duration while limiting case loads to numbers below hospitalization thresholds.

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 Broca’s 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.

Speech perception requires the binding of spatiotemporally disjoint auditory–visual cues. The corresponding brain network-level information can be characterized by two complementary mechanisms, functional segregation which refers to the localization of processing, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using fMRI recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation–integration balance. We controlled the inter-subject variability of illusory/cross-modal perception parametrically, by introducing temporal lags in the incongruent auditory–visual articulations of speech sounds using the McGurk paradigm. The functional segregation–integration balance was captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sesnory signals defined as the perceptual binding network (PBN) was identified using standardized parametric statistical approaches and their temporal correlations with all other brain areas were computed. With increasing illusory perception, the majority of the nodes of PBN showed decreased cooperation with the rest of the brain, reflecting high local segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of local segregation–global integration were cross-validated.

Temporal structure of cognitive and sensory processing holds the key to understanding complex neural mechanisms involved in higher order brain functions like perception of time. A hypothesis of embodied cognition posits that cognitive processes are deeply rooted in the interactions with the external world (Wilson et al., 2002; Anderson et al., 2012). These interactions of the brain with the external world depend on the accurate representation of the time-dimension in neural circuits (Gupta, 2014). For example, one cannot catch a flying ball unless the timing of the movements matches the speed of the ball. Many real world situations depend on the mapping between the neural and physical representation of time, which is maintained at different hierarchical levels. Hierarchical processing, consistent with multiple time scales, is manifested during goal-driven tasks, such as interval timing, duration judgement, and movement coordination. Contributions to this Research Topic elucidate how key aspects of the time-dimension such as the temporal binding of neural events play important roles in various cognitive processes, which include perception, mental time travel, and speech production. Additionally, the multi-scale representation of such processes from the micro to meso scales—from single neurons to a population of neurons to field potentials and macroscopic scales of EEG - is, discussed.

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.

We sought to identify optimal temporal windows for lockdown-based mitigation strategies on infectious disease spreads. An age-structured multi-compartmental Susceptible- Infected-Recovered (SIR) model was used to estimate infection spreads under parametric variation of lockdown intensity and duration from the data of SARS-CoV2 cases in India between January to July, 2020. The resulting parameter values were used to simulate lockdown outcomes for a wide range of start times and durations. Lockdowns were simulated as intervention strategies that modified weights assigned to social contact matrices for work, school and other places. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Our analysis shows that lockdown outcomes depend sensitively on the timing of imposition and that it is possible to minimize lockdown durations while limiting case loads to numbers below the hospitalization thresholds. Such timing based effects arise naturally from the non-linear nature of SIR dynamics.NotationNTotal PopulationSNumber of susceptible individualsINumber of infected individualsRNumber of recovered/removed individualsβPer-individual disease transmission rateγRecovery rateτLockdown start-timeΔ Duration of lockdownpPost-lockdown coefficienthTotal number of hospital bedsξMaximum fraction of infected individualsξ0Hospitalization threshold

We sought to simulate lockdown scenarios using an Agent Based Modelling (ABM) strategy, which is a new modelling paradigm that seeks to simulate the actions and interactions of autonomous agents within an environment. The spread of infectious viral diseases occur over a connected social network. Specifically, the goal was to understand the effect of network topology and lockdown strategies on disease spreading dynamics. To explore the effect of topology we assumed the social network over which the disease spreads to have small-world or scale-free properties characterized by a rewiring probability and degree distribution respectively. Lockdowns were simulated as intervention strategies that modified the spreading dynamics of infection over a given graph structure through changes in properties of agent interaction. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Thereafter, lockdown efficacy was evaluated as a function of lockdown start times and duration. Thus, we propose that ABM approach can be used to assess various lockdown strategies that aim to prevent breakdown of medical infrastructure while accounting for realistic social network configurations specific to a local population.Notation

Reorientation of attention towards unexpected salient changes around us is critical for survival. Current understanding from fMRI studies point towards a network comprising of pre-frontal cortical areas (PFC), temporo-parietal junction (TPJ) and insula being responsible for processing and reorienting attention in a myriad of tasks. However, little is known about the temporal structure of the dynamical changes that govern neural systems while reorienting attentional focus to visual stimuli operating at very fast and very slow time scales. Using a custom-designed behavioral experiment and simultaneous EEG recordings, we investigated the effect of saliency across different task conditions to see if the underlying neural signatures involved in such rapid attentional shifts change with the task conditions. Interestingly, the EEG signal power at alpha band showed near identical rise in amplitude during salient conditions for a visual search and a dynamic motion tracking task. Source reconstruction underlying the enhanced alpha activity across task conditions revealed the involvement of lateral PFC, right insula and right TPJ which are regions of the Ventral Attention Network responsible for the deployment of bottom-up attention in response to salient stimuli. The results suggested a more general role of alpha oscillations which is dependent on the task context (saliency) but not on the task complexity or goals. Employing source-level effective connectivity analysis, we observed that the posterior right TPJ receives causal influences from the anterior right TPJ. Thus, we characterized the specific roles of both these regions in salient distractor processing for the first time through an EEG study. SIGNIFICANCE STATEMENT Regardless of a vast body of extant literature that has linked alpha oscillations to distractor suppression, the neural implementation of this mechanism of inhibition at the level of attentional networks lacks clarity. The importance of context specificity while processing saliency and the extent to which it reflects in the alpha modulation has garnered recent interest. Through an EEG study, we see the effect of salient distractors on two visual attention tasks: 1) involving spatial complexity and 2) involving spatio-temporal complexity. The results bring forth an interesting revelation where the neural patterns of alpha enhancement vis-a-vis saliency and the underlying neural networks are completely agnostic to the task conditions, thus elucidating the specific role of alpha oscillations in perceptual processing.

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.

This chapter discusses legal strategies to enforce copyright online in India, with a focus on the film industry. The chapter begins by acknowledging various limitations of online copyright enforcement. Then, in Sect. 2, the chapter provides a broad overview of the landscape concerning film piracy in India. In Sect. 3, the chapter discusses a proposed (and unimplemented) recommendation to target end-users in India. In Sect. 4, the chapter discusses recent developments concerning website-blocking injunctions, which is a strategy that the industry has consistently pursued in recent years. In Sect. 5, the chapter discusses the ad-supported financial model of pirate websites, referencing a study conducted in India. The chapter concludes by reflecting on the efficacy of non-litigious measures, such as the targeting of ad-supported piracy and negotiations with search engines.

Several studies have proposed constraints under which a low-dimensional representation can be derived from large-scale real-world networks exhibiting complex nonlinear dynamics. Typically, these representations are formulated under certain assumptions, such as when solutions converge to attractor states using linear stability analysis or using projections of large-scale dynamical data into a set of lower dimensional modes that are selected heuristically. Here, we propose a generative framework for selection of lower dimensional modes onto which the entire network dynamics can be projected based on the symmetry of the input distribution for a large-scale network driven by external inputs, thus relaxing the heuristic selection of modes made in the earlier reduction approaches. The proposed mode reduction technique is tractable analytically and applied to different kinds of real-world large-scale network scenarios with nodes comprising of (a) Van der Pol oscillators (b) Hindmarsh–Rose neurons. These two demonstrations elucidate how order parameter is conserved at original and reduced descriptions thus validating our proposition.

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.

Oscillatory brain electromagnetic activity is an established tool to study neurophysiological mechanisms of human behavior using electro-encephalogram (EEG) and magneto-encephalogram (MEG) techniques. Often, to extract source level information in the cortex, researchers have to rely on inverse techniques that generate probabilistic estimation of the cortical activation underlying EEG/ MEG data from sensors located outside the body. State of the art source localization methods using current density estimates such as exact low resolution electromagnetic tomography (eLORETA) and minimum norm estimates (MNE) as well as beamformers such as Dynamic Imaging of Coherent Sources (DICS) and Linearly Constrained Minimum Variance (LCMV) have over the years been established as the prominent techniques of choice. However, these algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. Furthermore, the volume conduction effects, phase lags between sources and noise of the environment play a considerable role in adding uncertainty to source localization. There are very few comparative analyses that evaluates the “ground truth detection” capabilities of these methods and evaluates their efficacies based on sources in temporal cortex relevant for auditory processing as well as mesial temporal lobe epilepsies. In this Methods article, we compare the aforementioned techniques to estimate sources of spectral event generators in the cortex using a two-pronged approach. First, we simulated EEG data with point dipole (single and two-point), as well as, distributed dipole modelling techniques to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive ratios while detecting already known location of neural activity generators under varying signal to noise ratios and depths of sources from cortical surface. Secondly, we performed source localization on empricial EEG data collected from human participants while they listened to rhythmic tone stimuli binaurally. Importantly, we found a less-distributed activation map is generated by LCMV and DICS when compared to eLORETA. However, control of false positives is much superior in eLORETA especially while using realistic distributed dipole scenarios. We also highlight the strengths and drawbacks of eLORETA, LCMV and DICS following a comprehensive analysis of simulated and empirical EEG data.

A widely used experimental design in multisensory integration is the McGurk paradigm that entail illusory (cross-modal) perception of speech sounds when presented with incongruent audio-visual (AV) stimuli. However, the distribution of responses across trials and individuals is heterogeneous and not necessarily everyone in a given group of individuals perceives the effect. Nonetheless, existing studies in the field primarily focus on addressing the correlation between subjective behavior and cortical activations to reveal the neuronal mechanisms underlying the perception of McGurk effect, typically in the “frequent perceivers”. Additionally, a solely neuroimaging approach does not provide mechanistic explanation for the observed inter-trial or inter-individual heterogeneity. In the current study we employ high density electroencephalogram (EEG) recordings in a group of 25 human subjects that allow us to distinguish “frequent perceivers” from “rare perceivers” using behavioral responses as well as from the perspective of large-scale brain functional connectivity (FC). Using global coherence as a measure of large-scale FC, we find that alpha band coherence, a distinctive feature in frequent perceivers is absent in the rare perceivers. Secondly, a decrease in alpha band coherence and increase in gamma band coherence occur during illusory perception trials in both frequent and rare perceivers. Source analysis followed up with source time series reconstructions reveals a large scale network of brain areas involving frontal, temporal and parietal areas that are involved in network level processing of cross-modal perception. Finally, we demonstrate that how a biophysically realistic computational model representing the interaction among key neuronal systems (visual, auditory and multisensory cortical regions) can explain the empirical observations. Each system involves a group of excitatory and inhibitory Hindmarsh Rose neurons that are coupled amongst each other. Large-scale FC between areas is conceptualized using coupling functions and the identity of a specific system, e.g., visual/ auditory/ multisensory is chosen using empirical estimates of the time-scale of information processing in these systems. The model predicts that the disappearance of alpha band coherence observed in rare perceivers stems from a negligible direct A-V (audio-visual) coupling however, an increase in indirect interaction via multisensory node leads to enhanced gamma band and reduced alpha band coherences observed during illusory perception. Overall, we establish the mechanistic basis of large-scale FC patterns underlying cross-modal perception.