ABSTRACT
Speech perception requires the binding of spatiotemporally disjoint auditory-visual cues. The corresponding brain network-level information processing can be characterized by two complementary mechanisms: functional segregation which refers to the localization of processing in either isolated or distributed modules across the brain, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using functional magnetic resonance imaging recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation-integration. 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 within the McGurk paradigm. The states of segregation-integration balance were captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sensory 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 states of high segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of segregation-integration were cross-validated.
Subject(s)
Brain , Magnetic Resonance Imaging , Speech Perception , Visual Perception , Humans , Brain/physiology , Brain/diagnostic imaging , Male , Female , Adult , Young Adult , Speech Perception/physiology , Visual Perception/physiology , Brain Mapping , Acoustic Stimulation , Nerve Net/physiology , Nerve Net/diagnostic imaging , Photic Stimulation/methods , Illusions/physiology , Neural Pathways/physiology , Auditory Perception/physiologyABSTRACT
The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.
Subject(s)
Attention Deficit Disorder with Hyperactivity , Brain , Connectome , Magnetic Resonance Imaging , Nerve Net , Schizophrenia , Humans , Schizophrenia/physiopathology , Schizophrenia/diagnostic imaging , Attention Deficit Disorder with Hyperactivity/physiopathology , Attention Deficit Disorder with Hyperactivity/diagnostic imaging , Female , Male , Adult , Brain/physiopathology , Brain/diagnostic imaging , Nerve Net/diagnostic imaging , Nerve Net/physiopathology , Bipolar Disorder/physiopathology , Bipolar Disorder/diagnostic imaging , Young Adult , Middle Aged , Mental Disorders/physiopathology , Mental Disorders/diagnostic imagingABSTRACT
Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To 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. Here, we intend to explore with an unsupervised and rigorous source estimation approach, the underlying cortical generators of MMN and P300, in the context of prediction error propagation along the hierarchies of brain information processing in healthy human participants. The existing methods of characterizing the two ERPs involve only approximate estimations of their amplitudes and latencies based on specific sensors of interest. Our objective is twofold: first, we introduce a novel data-driven unsupervised approach to compute latencies and amplitude of ERP components accurately on an individual-subject basis and reconfirm earlier findings. Second, we demonstrate that in multisensory environments, MMN generators seem to reflect a significant overlap of "modality-specific" and "modality-independent" information processing while P300 generators mark a shift toward completely "modality-independent" processing. Advancing earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that temporal facilitation extends to even the later components of prediction error processing, using EEG experiments. Such knowledge can be of value to clinical research for characterizing the key developmental stages of lifespan aging, schizophrenia, and depression.
Subject(s)
Electroencephalography , Event-Related Potentials, P300 , Humans , Male , Female , Adult , Electroencephalography/methods , Young Adult , Event-Related Potentials, P300/physiology , Auditory Perception/physiology , Cerebral Cortex/physiology , Acoustic Stimulation/methods , Evoked Potentials/physiologyABSTRACT
Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.
ABSTRACT
The synthesis of phosphorodiamidate morpholino oligonucleotides (PMOs) incorporating single or double triazole rings in the backbone has been achieved via Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC). The synthetic approach implemented is fundamentally convergent, involving the ligation of a 5'-azide PMO fragment to a 3'-alkyne fragment both in solution and on solid support. To access the 3'-alkyne PMO fragment, we synthesized 3'-N-propargyl chlorophosphoramidate morpholino monomers for all four nucleobases. The resulting triazole-incorporated PMOs (TzPMOs) have exhibited comparable or improved binding affinity toward complementary deoxyribonucleic acid (DNA)/ribonucleic acid (RNA) strands compared to its regular analogues. Finally, a full-length TzPMO was designed to target the Nanog gene, demonstrating almost identical hybridization properties when compared to its regular version. Circular dichroism studies revealed a B-type helical conformation for the duplexes formed by TzPMOs.
Subject(s)
Alkynes , Azides , Morpholinos , Circular Dichroism , TriazolesABSTRACT
The conventional N-glycosylation methods for nucleoside synthesis usually require strongly acidic or basic conditions. Here we report the decarboxylative C(sp3)-N coupling of glycosyl N-hydroxyphthalimide esters with nucleobases via dual photoredox/Cu catalysis, which offered a mild approach to nucleoside analogues. A total synthesis of oxetanocin A, an antiviral natural product containing an oxetanose moiety, has been achieved by using this method.
ABSTRACT
The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost 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. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.
ABSTRACT
Brain damage sustained from repeated blows in boxing, wrestling, and other combat sports has serious physical and mental health consequences. The degenerative brain disease, chronic traumatic encephalopathy (CTE), presents clinically with memory loss, aggression, difficulty in rational thinking and other cognitive problems. This spectrum, which mimics Alzheimer's disease, is diagnosed post-mortem through a brain biopsy in many professional athletes. However, little is known about the process of development and how to identify vulnerable individuals who may be on course for developing CTE. Boxing is a sport that has a severe toll on athletes' health, primarily on their brain health and function. This review addresses the concerns of brain injury, describes the pathologies that manifest in multiple scales, e.g., molecular and cognitive, and also proposes possible diagnostic and prognostic markers to characterize the early onset of CTE along with the aim to identify a starting point for future precautions and interventions.
ABSTRACT
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 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.
Subject(s)
Auditory Cortex , Hearing , Humans , Acoustic Stimulation , Hearing/physiology , Auditory Cortex/physiology , Auditory Perception , Temporal Lobe , Evoked Potentials, Auditory/physiology , ElectroencephalographyABSTRACT
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.
Subject(s)
Emotions , Prefrontal Cortex , Humans , Bayes Theorem , Prefrontal Cortex/physiology , Emotions/physiology , Uncertainty , NeuroimagingABSTRACT
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.
Subject(s)
Brain Mapping , Longevity , Young Adult , Humans , Aged , Brain Mapping/methods , Magnetic Resonance Imaging/methods , Neural Pathways , Models, Neurological , Rest , Brain/diagnostic imaging , Nerve Net/diagnostic imagingABSTRACT
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.NEW & NOTEWORTHY Parametric models for estimation of phase-locked and non-phase-locked brain signals reveals how estimation of non-phase-locked component is biased by the variability of phase-locked component and at the level of single trial becomes an ill-posed problem. Furthermore, the modeling framework delimits the boundaries where traditional averaging approach can be trusted to estimate the phase-locked and non-phase-locked components.
Subject(s)
Electroencephalography , Evoked Potentials , Electroencephalography/methods , Evoked Potentials/physiology , Brain/physiology , Reaction Time/physiology , CognitionABSTRACT
Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.
ABSTRACT
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.
Subject(s)
Diffusion Tensor Imaging , Longevity , Adult , Brain , Diffusion Tensor Imaging/methods , Humans , Magnetoencephalography/methods , Neural PathwaysABSTRACT
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.
In this work, we use controlled perturbations in silico to identify regions that influence and mediate information flow in active brain networks. Conventional approaches of identifying such regions require the extensive analytical treatment of mathematical models describing node dynamics, thus restricting its scope only to systems where such models have been defined. The presented formalism can identify regions of dynamical and functional importance by simply measuring responses to perturbations, and can thus be applied at any scale where regions can be perturbed, and without any prerequisite information about node dynamics. Furthermore, the relation of metrics to interregional communication, functional capabilities, and structure-function mapping in general affords them considerable practical importance, especially for identifying targets for therapeutic interventions.
ABSTRACT
Previous computational models have related spontaneous resting-state brain activity with local excitatory-inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance govern resting-state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions, relate to functional brain activity is of critical importance. We propose a multiscale dynamic mean field (MDMF) model-a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modeled as population of MDMF and are connected by realistic connection topologies estimated from diffusion tensor imaging data. First, MDMF successfully predicts resting-state functional connectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level-dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from an MDMF model.
ABSTRACT
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.
Subject(s)
Cognition , Longevity , Adult , Brain , Cross-Sectional Studies , Humans , MagnetoencephalographyABSTRACT
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 their reactivation. Interestingly the newly encoded sequences with prediction errors, seen once, showed accuracy as high as control sequences which were viewed repeatedly without change. Drift-diffusion modelling revealed a lower decision threshold for the newer sequences than older sequences, reflected by their faster recall. Moreover, participants' adjustments to their decision threshold significantly correlated with their relative speed of sequence memory recall. These results suggest a temporally distinct and adaptive role for prediction errors in learning and reorganizing episodic temporal sequences.
ABSTRACT
This article presents behavior and EEG dataset collected from 19 healthy human volunteers (10 females) in the age group of 21-29 (mean = 26.9, SD = ±2.15) years at National Brain Research Centre, India during a psychophysical paradigm customized to characterize the brain network interactions during saliency processing. We provide all the raw stimulus files used in developing the experimental paradigm of the linked research article "Organization of directed functional connectivity among nodes of ventral attention network reveals the common network mechanisms underlying saliency processing across distinct spatial and spatio-temporal scales" [1] for replication and use by researchers across various cohorts of the population. Pre-processed EEG time-series segmented into epochs corresponding to three experimental trial conditions, across two visual attention tasks testing the effect of salient distractors on goal-driven tasks are provided. The dataset also includes reaction times corresponding to individual trials. Additionally, structural MRI files corresponding to each individual and 3D EEG sensor locations of all volunteers are provided to assist in accurate source localization. Therefore, the presented dataset will not only facilitate the conventional time resolved EEG analysis like evoked activity and time-frequency analysis at the sensor level but will also facilitate the investigation of source level analysis like global coherence or phase-amplitude coupling within selected regions of the brain.