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Ageing brain is associated with a slow drift in structural network properties over the lifespan accompanied by reorganization in neuromolecular interactions giving rise to changes in global functional markers. What are the guiding principles of the homeostatic mechanisms that maintain the desired performance of functional neural circuits and preserve brain health during healthy ageing? We hypothesize that an ageing brain alters two primary neurotransmitters, glutamate andγ-aminobutyric acid (GABA), responsible for excitation-inhibition regulation, concomitant with anatomical demyelination to preserve critical neural dynamics that are necessary to uphold optimal network performance. Thus, often observed re-organized functional connectivity with age by several investigations is a byproduct of this adaptive process. We demonstrate that the adaptive mechanism is driven by the tuning of glutamate and GABA concentration over a very slow time scale (lifespan) that can be estimated by tracking criticality from co-ordinated neural dynamics at a resting state via a biophysically driven computational framework, introduced as a computational microscope. We validate several empirical observations and model predictions across three independent aging cohorts using this computational microscope. One of the key mechanisms we discover is the reduction in local glutamate levels employed by brain regions to maintain a homeostatic balance with aging. This is further supported by the invariance of measures of global functional integration during the healthy ageing process.

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.

Auditory steady-state responses (ASSR) are induced from the brainstem to the neocortex when humans hear periodic amplitude-modulated tonal signals. ASSRs have been argued to be a key marker of auditory temporal processing and pathological reorganization of ASSR – a biomarker of neurodegenerative disorders. However, most of the earlier studies reporting the neural basis of ASSRs were focused on looking at individual brain regions. Here, we seek to characterize the large-scale directed information flow among cortical sources of ASSR entrained by 40 Hz external signals. Entrained brain rhythms with power peaking at 40 Hz were generated using both monaural and binaural tonal stimulation. First, we confirm the presence of ASSRs and their well-known right hemispheric dominance during binaural and both monaural conditions. Thereafter, reconstruction of source activity employing individual anatomy of the participant and subsequent network analysis revealed that while the sources are common among different stimulation conditions, differential levels of source activation and differential patterns of directed information flow using Granger causality among sources underlie processing of binaurally and monaurally presented tones. Particularly, we show bidirectional interactions involving the right superior temporal gyrus and inferior frontal gyrus underlie right hemispheric dominance of 40 Hz ASSR during both monaural and binaural conditions. On the other hand, for monaural conditions, the strength of inter-hemispheric flow from left primary auditory areas to right superior temporal areas followed a pattern that comply with the generally observed contralateral dominance of sensory signal processing.

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.

International investment law has consistently grappled with the issue of transparency. While the need for increased transparency in the practice of investment tribunals is generally recognized in principle, in practice the application of transparency norms often raises contentious issues. One common issue is the appropriateness of transparent proceedings where the Bilateral Investment Treaty (BIT) governing the dispute is silent on the matter. A further, more vexed question arises when claimants proceed under multiple BITs with disparate transparency obligations. This situation arose in Rand Investments v. Republic of Serbia, where the claimants instituted an arbitration under both the Canada-Serbia and the Cyprus- Serbia BITs. Noting that the Cyprus-Serbia BIT was silent on the question of transparency, the Majority held that the transparency provisions of the Canada-Serbia BIT could be applied to the entire arbitration on grounds of procedural efficiency. However, the respondent’s arbitrator dissented, finding that the Majority’s approach violated Serbia’s consent and sovereignty. Upon examining the dichotomous approaches adopted by the Majority and the Dissenting Arbitrator, this case comment offers an insight into the potential implications of the case on future investment arbitrations involving multiple BITs with disparate transparency obligations. investment arbitration, dissenting opinion, transparency, Lotus principle, Effet Utile, procedural efficiency, Eurogas v Slovakia, duty of arbitrator, state consent, residual powers of the tribunal, confidentiality.

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.

Brain dynamics recorded via electroencephalography (EEG) is conceptualized as a sum of two components, “phase-locked” and “non-phase-locked” to the stimulus. Both activities are understood to be stemming from different neuronal mechanisms and hence accurately characterizing them is of immense importance in neuroscientific studies. Here, we discuss the drawbacks of currently used methods to separate the phase-locked and non-phase-locked activity and propose a new method that decomposes the two components simultaneously. First, we demonstrate that 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 the drawbacks of the widely used averaging method and efficacy of the proposed concurrent phaser method (CPM). Using two experimental datasets, audio oddball EEG data and auditory steady-state responses (ASSR) we show how the empirical signal-to-noise estimates warrant the usage of CPM to separate phase-locked and non-phase-locked activity.

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.

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.